Annotated combined sequence logos.txt

#
# Translate the basepairs in the CDR3 to amino acids
#
# WT_1
#
# 5N LN
#
loc_WT_1_5N_LN_all <- mutate(loc_WT_1_5N_LN_all, CDR3_AA = translateDNA(loc_WT_1_5N_LN_all$CDR3_IMGT))
#
loc_WT_1_5N_LN_2 <- mutate(loc_WT_1_5N_LN_2, CDR3_AA = translateDNA(loc_WT_1_5N_LN_2$CDR3_IMGT))
#
loc_WT_1_5N_LN_1 <- mutate(loc_WT_1_5N_LN_1, CDR3_AA = translateDNA(loc_WT_1_5N_LN_1$CDR3_IMGT))
#
# RAG_1
#
# 5N LN
#
loc_RAG_1_5N_LN_all <- mutate(loc_RAG_1_5N_LN_all, CDR3_AA = translateDNA(loc_RAG_1_5N_LN_all$CDR3_IMGT))
#
loc_RAG_1_5N_LN_2 <- mutate(loc_RAG_1_5N_LN_2, CDR3_AA = translateDNA(loc_RAG_1_5N_LN_2$CDR3_IMGT))
#
loc_RAG_1_5N_LN_1 <- mutate(loc_RAG_1_5N_LN_1, CDR3_AA = translateDNA(loc_RAG_1_5N_LN_1$CDR3_IMGT))
#
# 5P LN
#
loc_RAG_1_5P_LN_all <- mutate(loc_RAG_1_5P_LN_all, CDR3_AA = translateDNA(loc_RAG_1_5P_LN_all$CDR3_IMGT))
#
loc_RAG_1_5P_LN_2 <- mutate(loc_RAG_1_5P_LN_2, CDR3_AA = translateDNA(loc_RAG_1_5P_LN_2$CDR3_IMGT))
#
loc_RAG_1_5P_LN_1 <- mutate(loc_RAG_1_5P_LN_1, CDR3_AA = translateDNA(loc_RAG_1_5P_LN_1$CDR3_IMGT))
#
# 5P LP
#
loc_RAG_1_5P_LP_all <- mutate(loc_RAG_1_5P_LP_all, CDR3_AA = translateDNA(loc_RAG_1_5P_LP_all$CDR3_IMGT))
#
loc_RAG_1_5P_LP_2 <- mutate(loc_RAG_1_5P_LP_2, CDR3_AA = translateDNA(loc_RAG_1_5P_LP_2$CDR3_IMGT))
#
loc_RAG_1_5P_LP_1 <- mutate(loc_RAG_1_5P_LP_1, CDR3_AA = translateDNA(loc_RAG_1_5P_LP_1$CDR3_IMGT))
#
# VH12_1
#
# 5N LN
#
loc_VH12_1_5N_LN_all <- mutate(loc_VH12_1_5N_LN_all, CDR3_AA = translateDNA(loc_VH12_1_5N_LN_all$CDR3_IMGT))
#
loc_VH12_1_5N_LN_2 <- mutate(loc_VH12_1_5N_LN_2, CDR3_AA = translateDNA(loc_VH12_1_5N_LN_2$CDR3_IMGT))
#
loc_VH12_1_5N_LN_1 <- mutate(loc_VH12_1_5N_LN_1, CDR3_AA = translateDNA(loc_VH12_1_5N_LN_1$CDR3_IMGT))
#
# 5P LP
#
loc_VH12_1_5P_LP_all <- mutate(loc_VH12_1_5P_LP_all, CDR3_AA = translateDNA(loc_VH12_1_5P_LP_all$CDR3_IMGT))
#
loc_VH12_1_5P_LP_2 <- mutate(loc_VH12_1_5P_LP_2, CDR3_AA = translateDNA(loc_VH12_1_5P_LP_2$CDR3_IMGT))
#
loc_VH12_1_5P_LP_1 <- mutate(loc_VH12_1_5P_LP_1, CDR3_AA = translateDNA(loc_VH12_1_5P_LP_1$CDR3_IMGT))
#
# VH12RAG_1
#
# 5N LN
#
loc_VH12RAG_1_5N_LN_all <- mutate(loc_VH12RAG_1_5N_LN_all, CDR3_AA = translateDNA(loc_VH12RAG_1_5N_LN_all$CDR3_IMGT))
#
loc_VH12RAG_1_5N_LN_2 <- mutate(loc_VH12RAG_1_5N_LN_2, CDR3_AA = translateDNA(loc_VH12RAG_1_5N_LN_2$CDR3_IMGT))
#
loc_VH12RAG_1_5N_LN_1 <- mutate(loc_VH12RAG_1_5N_LN_1, CDR3_AA = translateDNA(loc_VH12RAG_1_5N_LN_1$CDR3_IMGT))
#
# 5P LP
#
loc_VH12RAG_1_5P_LP_all <- mutate(loc_VH12RAG_1_5P_LP_all, CDR3_AA = translateDNA(loc_VH12RAG_1_5P_LP_all$CDR3_IMGT))
#
loc_VH12RAG_1_5P_LP_2 <- mutate(loc_VH12RAG_1_5P_LP_2, CDR3_AA = translateDNA(loc_VH12RAG_1_5P_LP_2$CDR3_IMGT))
#
loc_VH12RAG_1_5P_LP_1 <- mutate(loc_VH12RAG_1_5P_LP_1, CDR3_AA = translateDNA(loc_VH12RAG_1_5P_LP_1$CDR3_IMGT))
#
#
#
# Sample WT_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_WT_1_5N_LN_2 <- mutate(pull_37_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_37_seq_WT_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_WT_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_WT_1_5N_LN_2 <- filter(pull_37_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# Sample RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_RAG_1_5N_LN_2 <- mutate(pull_37_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_37_seq_RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_RAG_1_5N_LN_2 <- filter(pull_37_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_RAG_1_5P_LN_2 <- mutate(pull_37_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH = str_length(pull_37_seq_RAG_1_5P_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_RAG_1_5P_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_RAG_1_5P_LN_2 <- filter(pull_37_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_RAG_1_5P_LP_2 <- mutate(pull_37_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_37_seq_RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_RAG_1_5P_LP_2 <- filter(pull_37_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_VH12_1_5N_LN_2 <- mutate(pull_37_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_37_seq_VH12_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_VH12_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_VH12_1_5N_LN_2 <- filter(pull_37_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_VH12_1_5P_LP_2 <- mutate(pull_37_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_37_seq_VH12_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_VH12_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_VH12_1_5P_LP_2 <- filter(pull_37_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_VH12RAG_1_5N_LN_2 <- mutate(pull_37_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_37_seq_VH12RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_VH12RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_VH12RAG_1_5N_LN_2 <- filter(pull_37_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_37_seq_VH12RAG_1_5P_LP_2 <- mutate(pull_37_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_37_seq_VH12RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_37_seq_VH12RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_37_seq_VH12RAG_1_5P_LP_2 <- filter(pull_37_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
#
# Vk 4-86
#
# Sample WT_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_WT_1_5N_LN_2 <- mutate(pull_486_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_486_seq_WT_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_WT_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_WT_1_5N_LN_2 <- filter(pull_486_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# Sample RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_RAG_1_5N_LN_2 <- mutate(pull_486_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_486_seq_RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_RAG_1_5N_LN_2 <- filter(pull_486_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_RAG_1_5P_LN_2 <- mutate(pull_486_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH = str_length(pull_486_seq_RAG_1_5P_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_RAG_1_5P_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_RAG_1_5P_LN_2 <- filter(pull_486_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_RAG_1_5P_LP_2 <- mutate(pull_486_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_486_seq_RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_RAG_1_5P_LP_2 <- filter(pull_486_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_VH12_1_5N_LN_2 <- mutate(pull_486_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_486_seq_VH12_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_VH12_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_VH12_1_5N_LN_2 <- filter(pull_486_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
#UMI 2
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_VH12_1_5P_LP_2 <- mutate(pull_486_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_486_seq_VH12_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_VH12_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_VH12_1_5P_LP_2 <- filter(pull_486_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_VH12RAG_1_5N_LN_2 <- mutate(pull_486_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_486_seq_VH12RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_VH12RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_VH12RAG_1_5N_LN_2 <- filter(pull_486_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_486_seq_VH12RAG_1_5P_LP_2 <- mutate(pull_486_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_486_seq_VH12RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_486_seq_VH12RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_486_seq_VH12RAG_1_5P_LP_2 <- filter(pull_486_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
#
# Vk 4-91
#
# Sample WT_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_WT_1_5N_LN_2 <- mutate(pull_491_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_WT_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_WT_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_WT_1_5N_LN_2 <- filter(pull_491_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# Sample RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_RAG_1_5N_LN_2 <- mutate(pull_491_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_RAG_1_5N_LN_2 <- filter(pull_491_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_RAG_1_5P_LN_2 <- mutate(pull_491_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_RAG_1_5P_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_RAG_1_5P_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_RAG_1_5P_LN_2 <- filter(pull_491_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_RAG_1_5P_LP_2 <- mutate(pull_491_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_491_seq_RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_RAG_1_5P_LP_2 <- filter(pull_491_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_VH12_1_5N_LN_2 <- mutate(pull_491_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_VH12_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_VH12_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_VH12_1_5N_LN_2 <- filter(pull_491_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_VH12_1_5P_LP_2 <- mutate(pull_491_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_491_seq_VH12_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_VH12_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_VH12_1_5P_LP_2 <- filter(pull_491_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_VH12RAG_1_5N_LN_2 <- mutate(pull_491_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_VH12RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_VH12RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_VH12RAG_1_5N_LN_2 <- filter(pull_491_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_491_seq_VH12RAG_1_5P_LP_2 <- mutate(pull_491_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_491_seq_VH12RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_491_seq_VH12RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_491_seq_VH12RAG_1_5P_LP_2 <- filter(pull_491_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
#
# Vk 14-126
#
# Sample WT_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_WT_1_5N_LN_2 <- mutate(pull_14126_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_WT_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_WT_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_WT_1_5N_LN_2 <- filter(pull_14126_seq_WT_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# Sample RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_RAG_1_5N_LN_2 <- mutate(pull_14126_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_RAG_1_5N_LN_2 <- filter(pull_14126_seq_RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_RAG_1_5P_LN_2 <- mutate(pull_14126_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_RAG_1_5P_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_RAG_1_5P_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_RAG_1_5P_LN_2 <- filter(pull_14126_seq_RAG_1_5P_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_RAG_1_5P_LP_2 <- mutate(pull_14126_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_RAG_1_5P_LP_2 <- filter(pull_14126_seq_RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_VH12_1_5N_LN_2 <- mutate(pull_14126_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_VH12_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_VH12_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_VH12_1_5N_LN_2 <- filter(pull_14126_seq_VH12_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_VH12_1_5P_LP_2 <- mutate(pull_14126_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_VH12_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_VH12_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_VH12_1_5P_LP_2 <- filter(pull_14126_seq_VH12_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
# Sample VH12RAG_1
#
# 5N LN
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_VH12RAG_1_5N_LN_2 <- mutate(pull_14126_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_VH12RAG_1_5N_LN_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_VH12RAG_1_5N_LN_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_VH12RAG_1_5N_LN_2 <- filter(pull_14126_seq_VH12RAG_1_5N_LN_2, CDR3_AA_LENGTH == M)
#
# 5P LP
#
# Add the length of the CDR3 (in amino acids) to the data frame
#
pull_14126_seq_VH12RAG_1_5P_LP_2 <- mutate(pull_14126_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH = str_length(pull_14126_seq_VH12RAG_1_5P_LP_2$CDR3_AA))
#
# Find the most common (the mode) length of the CDR3
#
M <- getmode(pull_14126_seq_VH12RAG_1_5P_LP_2$CDR3_AA_LENGTH)
#
# Select only sequences of the mode length to create the sequence logo
#
logo_14126_seq_VH12RAG_1_5P_LP_2 <- filter(pull_14126_seq_VH12RAG_1_5P_LP_2, CDR3_AA_LENGTH == M)
#
#
# Vk 3-7
#
# WT
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_WT_5N_LN_2 <- rbind(logo_37_seq_WT_1_5N_LN_2, logo_37_seq_WT_3_5N_LN_2)
seq_logo_37_WT_5N_LN_2 <- rbind(seq_logo_37_WT_5N_LN_2, logo_37_seq_WT_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_WT_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_WT_5N_LN_2 <- ggseqlogo(seq_logo_37_WT_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_WT_5N_LN_2.jpeg", width=14, height=10)
#
# dnRAG1
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_dnRAG1_5N_LN_2 <- rbind(logo_37_seq_RAG_1_5N_LN_2, logo_37_seq_RAG_3_5N_LN_2)
seq_logo_37_dnRAG1_5N_LN_2 <- rbind(seq_logo_37_dnRAG1_5N_LN_2, logo_37_seq_RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_dnRAG1_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_5N_LN_2 <- ggseqlogo(seq_logo_37_dnRAG1_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_dnRAG1_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_dnRAG1_5P_LN_2 <- rbind(logo_37_seq_RAG_1_5P_LN_2, logo_37_seq_RAG_3_5P_LN_2)
seq_logo_37_dnRAG1_5P_LN_2 <- rbind(seq_logo_37_dnRAG1_5P_LN_2, logo_37_seq_RAG_2_5P_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_dnRAG1_5P_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_5P_LN_2 <- ggseqlogo(seq_logo_37_dnRAG1_5P_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_dnRAG1_5P_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_dnRAG1_5P_LP_2 <- rbind(logo_37_seq_RAG_1_5P_LP_2, logo_37_seq_RAG_3_5P_LP_2)
seq_logo_37_dnRAG1_5P_LP_2 <- rbind(seq_logo_37_dnRAG1_5P_LP_2, logo_37_seq_RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_dnRAG1_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_5P_LP_2 <- ggseqlogo(seq_logo_37_dnRAG1_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_dnRAG1_5P_LP_2.jpeg", width=14, height=10)
#
# VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_VH12_5N_LN_2 <- rbind(logo_37_seq_VH12_1_5N_LN_2, logo_37_seq_VH12_3_5N_LN_2)
seq_logo_37_VH12_5N_LN_2 <- rbind(seq_logo_37_VH12_5N_LN_2, logo_37_seq_VH12_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_VH12_5N_LN_2 <- ggseqlogo(seq_logo_37_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_VH12_5P_LP_2 <- rbind(logo_37_seq_VH12_1_5P_LP_2, logo_37_seq_VH12_3_5P_LP_2)
seq_logo_37_VH12_5P_LP_2 <- rbind(seq_logo_37_VH12_5P_LP_2, logo_37_seq_VH12_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_VH12_5P_LP_2 <- ggseqlogo(seq_logo_37_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_VH12_5P_LP_2.jpeg", width=14, height=10)
#
# dnRAG1_VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_dnRAG1_VH12_5N_LN_2 <- rbind(logo_37_seq_VH12RAG_1_5N_LN_2, logo_37_seq_VH12RAG_3_5N_LN_2)
seq_logo_37_dnRAG1_VH12_5N_LN_2 <- rbind(seq_logo_37_dnRAG1_VH12_5N_LN_2, logo_37_seq_VH12RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_dnRAG1_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_VH12_5N_LN_2 <- ggseqlogo(seq_logo_37_dnRAG1_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_dnRAG1_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_37_dnRAG1_VH12_5P_LP_2 <- rbind(logo_37_seq_VH12RAG_1_5P_LP_2, logo_37_seq_VH12RAG_3_5P_LP_2)
seq_logo_37_dnRAG1_VH12_5P_LP_2 <- rbind(seq_logo_37_dnRAG1_VH12_5P_LP_2, logo_37_seq_VH12RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_37_dnRAG1_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_VH12_5P_LP_2 <- ggseqlogo(seq_logo_37_dnRAG1_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_37_dnRAG1_VH12_5P_LP_2.jpeg", width=14, height=10)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_37_dnRAG1_VH12_5P_LP_2_axis_legend <- ggseqlogo(seq_logo_37_dnRAG1_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(axis.text=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+theme(legend.key.size = unit(0.75, "in"))+theme(legend.text = element_text(size=32, face="bold"))+theme(legend.title=element_blank())
ggsave("graph_seq_logo_37_dnRAG1_VH12_5P_LP_2_axis_legend.jpeg", width=15, height=11)
#
#
# Vk 4-86
#
#
# WT
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_WT_5N_LN_2 <- rbind(logo_486_seq_WT_1_5N_LN_2, logo_486_seq_WT_3_5N_LN_2)
seq_logo_486_WT_5N_LN_2 <- rbind(seq_logo_486_WT_5N_LN_2, logo_486_seq_WT_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_WT_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_WT_5N_LN_2 <- ggseqlogo(seq_logo_486_WT_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_WT_5N_LN_2.jpeg", width=14, height=10)
#
# dnRAG1
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_dnRAG1_5N_LN_2 <- rbind(logo_486_seq_RAG_1_5N_LN_2, logo_486_seq_RAG_3_5N_LN_2)
seq_logo_486_dnRAG1_5N_LN_2 <- rbind(seq_logo_486_dnRAG1_5N_LN_2, logo_486_seq_RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_dnRAG1_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_dnRAG1_5N_LN_2 <- ggseqlogo(seq_logo_486_dnRAG1_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_dnRAG1_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_dnRAG1_5P_LN_2 <- rbind(logo_486_seq_RAG_1_5P_LN_2, logo_486_seq_RAG_3_5P_LN_2)
seq_logo_486_dnRAG1_5P_LN_2 <- rbind(seq_logo_486_dnRAG1_5P_LN_2, logo_486_seq_RAG_2_5P_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_dnRAG1_5P_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_dnRAG1_5P_LN_2 <- ggseqlogo(seq_logo_486_dnRAG1_5P_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_dnRAG1_5P_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_dnRAG1_5P_LP_2 <- rbind(logo_486_seq_RAG_1_5P_LP_2, logo_486_seq_RAG_3_5P_LP_2)
seq_logo_486_dnRAG1_5P_LP_2 <- rbind(seq_logo_486_dnRAG1_5P_LP_2, logo_486_seq_RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_dnRAG1_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_dnRAG1_5P_LP_2 <- ggseqlogo(seq_logo_486_dnRAG1_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_dnRAG1_5P_LP_2.jpeg", width=14, height=10)
#
# VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_VH12_5N_LN_2 <- rbind(logo_486_seq_VH12_1_5N_LN_2, logo_486_seq_VH12_3_5N_LN_2)
seq_logo_486_VH12_5N_LN_2 <- rbind(seq_logo_486_VH12_5N_LN_2, logo_486_seq_VH12_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_VH12_5N_LN_2 <- ggseqlogo(seq_logo_486_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_VH12_5P_LP_2 <- rbind(logo_486_seq_VH12_1_5P_LP_2, logo_486_seq_VH12_3_5P_LP_2)
seq_logo_486_VH12_5P_LP_2 <- rbind(seq_logo_486_VH12_5P_LP_2, logo_486_seq_VH12_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_VH12_5P_LP_2 <- ggseqlogo(seq_logo_486_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_VH12_5P_LP_2.jpeg", width=14, height=10)
#
# dnRAG1_VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_dnRAG1_VH12_5N_LN_2 <- rbind(logo_486_seq_VH12RAG_1_5N_LN_2, logo_486_seq_VH12RAG_3_5N_LN_2)
seq_logo_486_dnRAG1_VH12_5N_LN_2 <- rbind(seq_logo_486_dnRAG1_VH12_5N_LN_2, logo_486_seq_VH12RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_dnRAG1_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_dnRAG1_VH12_5N_LN_2 <- ggseqlogo(seq_logo_486_dnRAG1_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_dnRAG1_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_486_dnRAG1_VH12_5P_LP_2 <- rbind(logo_486_seq_VH12RAG_2_5P_LP_2, logo_486_seq_VH12RAG_3_5P_LP_2)
seq_logo_486_dnRAG1_VH12_5P_LP_2 <- rbind(seq_logo_486_dnRAG1_VH12_5P_LP_2, logo_486_seq_VH12RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_486_dnRAG1_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_486_dnRAG1_VH12_5P_LP_2 <- ggseqlogo(seq_logo_486_dnRAG1_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_486_dnRAG1_VH12_5P_LP_2.jpeg", width=14, height=10)
#
#
# Vk 4-91
#
#
# WT
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_WT_5N_LN_2 <- rbind(logo_491_seq_WT_1_5N_LN_2, logo_491_seq_WT_3_5N_LN_2)
seq_logo_491_WT_5N_LN_2 <- rbind(seq_logo_491_WT_5N_LN_2, logo_491_seq_WT_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_WT_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_WT_5N_LN_2 <- ggseqlogo(seq_logo_491_WT_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_WT_5N_LN_2.jpeg", width=14, height=10)
#
# dnRAG1
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_dnRAG1_5N_LN_2 <- rbind(logo_491_seq_RAG_1_5N_LN_2, logo_491_seq_RAG_3_5N_LN_2)
seq_logo_491_dnRAG1_5N_LN_2 <- rbind(seq_logo_491_dnRAG1_5N_LN_2, logo_491_seq_RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_dnRAG1_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_dnRAG1_5N_LN_2 <- ggseqlogo(seq_logo_491_dnRAG1_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_dnRAG1_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_dnRAG1_5P_LN_2 <- rbind(logo_491_seq_RAG_1_5P_LN_2, logo_491_seq_RAG_3_5P_LN_2)
seq_logo_491_dnRAG1_5P_LN_2 <- rbind(seq_logo_491_dnRAG1_5P_LN_2, logo_491_seq_RAG_2_5P_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_dnRAG1_5P_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_dnRAG1_5P_LN_2 <- ggseqlogo(seq_logo_491_dnRAG1_5P_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_dnRAG1_5P_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_dnRAG1_5P_LP_2 <- rbind(logo_491_seq_RAG_1_5P_LP_2, logo_491_seq_RAG_3_5P_LP_2)
seq_logo_491_dnRAG1_5P_LP_2 <- rbind(seq_logo_491_dnRAG1_5P_LP_2, logo_491_seq_RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_dnRAG1_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_dnRAG1_5P_LP_2 <- ggseqlogo(seq_logo_491_dnRAG1_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_dnRAG1_5P_LP_2.jpeg", width=14, height=10)
#
# VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_VH12_5N_LN_2 <- rbind(logo_491_seq_VH12_1_5N_LN_2, logo_491_seq_VH12_3_5N_LN_2)
seq_logo_491_VH12_5N_LN_2 <- rbind(seq_logo_491_VH12_5N_LN_2, logo_491_seq_VH12_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_VH12_5N_LN_2 <- ggseqlogo(seq_logo_491_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_VH12_5P_LP_2 <- rbind(logo_491_seq_VH12_1_5P_LP_2, logo_491_seq_VH12_3_5P_LP_2)
seq_logo_491_VH12_5P_LP_2 <- rbind(seq_logo_491_VH12_5P_LP_2, logo_491_seq_VH12_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_VH12_5P_LP_2 <- ggseqlogo(seq_logo_491_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_VH12_5P_LP_2.jpeg", width=14, height=10)
#
# dnRAG1_VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_dnRAG1_VH12_5N_LN_2 <- rbind(logo_491_seq_VH12RAG_1_5N_LN_2, logo_491_seq_VH12RAG_3_5N_LN_2)
seq_logo_491_dnRAG1_VH12_5N_LN_2 <- rbind(seq_logo_491_dnRAG1_VH12_5N_LN_2, logo_491_seq_VH12RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_dnRAG1_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_dnRAG1_VH12_5N_LN_2 <- ggseqlogo(seq_logo_491_dnRAG1_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_dnRAG1_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_491_dnRAG1_VH12_5P_LP_2 <- rbind(logo_491_seq_VH12RAG_1_5P_LP_2, logo_491_seq_VH12RAG_3_5P_LP_2)
seq_logo_491_dnRAG1_VH12_5P_LP_2 <- rbind(seq_logo_491_dnRAG1_VH12_5P_LP_2, logo_491_seq_VH12RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_491_dnRAG1_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_491_dnRAG1_VH12_5P_LP_2 <- ggseqlogo(seq_logo_491_dnRAG1_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_491_dnRAG1_VH12_5P_LP_2.jpeg", width=14, height=10)
#
#
# Vk 14-126
#
#
# WT
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_WT_5N_LN_2 <- rbind(logo_14126_seq_WT_1_5N_LN_2, logo_14126_seq_WT_3_5N_LN_2)
seq_logo_14126_WT_5N_LN_2 <- rbind(seq_logo_14126_WT_5N_LN_2, logo_14126_seq_WT_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_WT_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_WT_5N_LN_2 <- ggseqlogo(seq_logo_14126_WT_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_WT_5N_LN_2.jpeg", width=14, height=10)
#
# dnRAG1
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_dnRAG1_5N_LN_2 <- rbind(logo_14126_seq_RAG_1_5N_LN_2, logo_14126_seq_RAG_3_5N_LN_2)
seq_logo_14126_dnRAG1_5N_LN_2 <- rbind(seq_logo_14126_dnRAG1_5N_LN_2, logo_14126_seq_RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_dnRAG1_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_dnRAG1_5N_LN_2 <- ggseqlogo(seq_logo_14126_dnRAG1_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_dnRAG1_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_dnRAG1_5P_LN_2 <- rbind(logo_14126_seq_RAG_1_5P_LN_2, logo_14126_seq_RAG_3_5P_LN_2)
seq_logo_14126_dnRAG1_5P_LN_2 <- rbind(seq_logo_14126_dnRAG1_5P_LN_2, logo_14126_seq_RAG_2_5P_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_dnRAG1_5P_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_dnRAG1_5P_LN_2 <- ggseqlogo(seq_logo_14126_dnRAG1_5P_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_dnRAG1_5P_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_dnRAG1_5P_LP_2 <- rbind(logo_14126_seq_RAG_1_5P_LP_2, logo_14126_seq_RAG_3_5P_LP_2)
seq_logo_14126_dnRAG1_5P_LP_2 <- rbind(seq_logo_14126_dnRAG1_5P_LP_2, logo_14126_seq_RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_dnRAG1_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_dnRAG1_5P_LP_2 <- ggseqlogo(seq_logo_14126_dnRAG1_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_dnRAG1_5P_LP_2.jpeg", width=14, height=10)
#
# VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_VH12_5N_LN_2 <- rbind(logo_14126_seq_VH12_1_5N_LN_2, logo_14126_seq_VH12_3_5N_LN_2)
seq_logo_14126_VH12_5N_LN_2 <- rbind(seq_logo_14126_VH12_5N_LN_2, logo_14126_seq_VH12_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_VH12_5N_LN_2 <- ggseqlogo(seq_logo_14126_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_VH12_5P_LP_2 <- rbind(logo_14126_seq_VH12_1_5P_LP_2, logo_14126_seq_VH12_3_5P_LP_2)
seq_logo_14126_VH12_5P_LP_2 <- rbind(seq_logo_14126_VH12_5P_LP_2, logo_14126_seq_VH12_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_VH12_5P_LP_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_VH12_5P_LP_2 <- ggseqlogo(seq_logo_14126_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_VH12_5P_LP_2.jpeg", width=14, height=10)
#
# dnRAG1_VH12
#
# 5N LN
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_dnRAG1_VH12_5N_LN_2 <- rbind(logo_14126_seq_VH12RAG_1_5N_LN_2, logo_14126_seq_VH12RAG_3_5N_LN_2)
seq_logo_14126_dnRAG1_VH12_5N_LN_2 <- rbind(seq_logo_14126_dnRAG1_VH12_5N_LN_2, logo_14126_seq_VH12RAG_2_5N_LN_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_dnRAG1_VH12_5N_LN_2$CDR3_AA)
#
# Generate the sequence logo using the probablity method and print the number of sequences used for that logo
#
graph_seq_logo_14126_dnRAG1_VH12_5N_LN_2 <- ggseqlogo(seq_logo_14126_dnRAG1_VH12_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_dnRAG1_VH12_5N_LN_2.jpeg", width=14, height=10)
#
# 5P LP
#
# Combine the mode-selected data frames from the three replicates
#
seq_logo_14126_dnRAG1_VH12_5P_LP_2 <- rbind(logo_14126_seq_VH12RAG_1_5P_LP_2, logo_14126_seq_VH12RAG_3_5P_LP_2)
seq_logo_14126_dnRAG1_VH12_5P_LP_2 <- rbind(seq_logo_14126_dnRAG1_VH12_5P_LP_2, logo_14126_seq_VH12RAG_2_5P_LP_2)
#
# Count the number of sequences of the mode length
#
N <- length(seq_logo_14126_dnRAG1_VH12_5P_LP_2$CDR3_AA)
#
graph_seq_logo_14126_dnRAG1_VH12_5P_LP_2 <- ggseqlogo(seq_logo_14126_dnRAG1_VH12_5P_LP_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y=element_text(size=32, face="bold"), axis.title=element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.05, size = 20, fontface="bold", label= paste("n =",N))
ggsave("graph_seq_logo_14126_dnRAG1_VH12_5P_LP_2.jpeg", width=14, height=10)
#
#
# Length plots of CDR3 sequences
#
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_WT_5N_LN <- rbind(pull_37_seq_WT_1_5N_LN_2, pull_37_seq_WT_2_5N_LN_2)
cdr3_length_37_WT_5N_LN <- rbind(cdr3_length_37_WT_5N_LN, pull_37_seq_WT_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_WT_5N_LN <- mutate(cdr3_length_37_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_37_WT_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_WT_5N_LN_count <- count(cdr3_length_37_WT_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_WT_5N_LN_count <- mutate(cdr3_length_37_WT_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_WT_5N_LN_count <- mutate(cdr3_length_37_WT_5N_LN_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_37_WT <- cdr3_length_37_WT_5N_LN_count
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_RAG_5N_LN <- rbind(pull_37_seq_RAG_1_5N_LN_2, pull_37_seq_RAG_2_5N_LN_2)
cdr3_length_37_RAG_5N_LN <- rbind(cdr3_length_37_RAG_5N_LN, pull_37_seq_RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_RAG_5N_LN <- mutate(cdr3_length_37_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_37_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_RAG_5N_LN_count <- count(cdr3_length_37_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_RAG_5N_LN_count <- mutate(cdr3_length_37_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_RAG_5N_LN_count <- mutate(cdr3_length_37_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_RAG_5P_LN <- rbind(pull_37_seq_RAG_1_5P_LN_2, pull_37_seq_RAG_2_5P_LN_2)
cdr3_length_37_RAG_5P_LN <- rbind(cdr3_length_37_RAG_5P_LN, pull_37_seq_RAG_3_5P_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_RAG_5P_LN <- mutate(cdr3_length_37_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_37_RAG_5P_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_RAG_5P_LN_count <- count(cdr3_length_37_RAG_5P_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_RAG_5P_LN_count <- mutate(cdr3_length_37_RAG_5P_LN_count, POPULATION = paste("CD5+ PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_RAG_5P_LN_count <- mutate(cdr3_length_37_RAG_5P_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_RAG_5P_LP <- rbind(pull_37_seq_RAG_1_5P_LP_2, pull_37_seq_RAG_2_5P_LP_2)
cdr3_length_37_RAG_5P_LP <- rbind(cdr3_length_37_RAG_5P_LP, pull_37_seq_RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_RAG_5P_LP <- mutate(cdr3_length_37_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_37_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_RAG_5P_LP_count <- count(cdr3_length_37_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_RAG_5P_LP_count <- mutate(cdr3_length_37_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_RAG_5P_LP_count <- mutate(cdr3_length_37_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_37_RAG <- rbind(cdr3_length_37_RAG_5N_LN_count, cdr3_length_37_RAG_5P_LP_count)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_VH12_5N_LN <- rbind(pull_37_seq_VH12_1_5N_LN_2, pull_37_seq_VH12_2_5N_LN_2)
cdr3_length_37_VH12_5N_LN <- rbind(cdr3_length_37_VH12_5N_LN, pull_37_seq_VH12_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_VH12_5N_LN <- mutate(cdr3_length_37_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_37_VH12_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_VH12_5N_LN_count <- count(cdr3_length_37_VH12_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_VH12_5N_LN_count <- mutate(cdr3_length_37_VH12_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_VH12_5N_LN_count <- mutate(cdr3_length_37_VH12_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_VH12_5P_LP <- rbind(pull_37_seq_VH12_1_5P_LP_2, pull_37_seq_VH12_2_5P_LP_2)
cdr3_length_37_VH12_5P_LP <- rbind(cdr3_length_37_VH12_5P_LP, pull_37_seq_VH12_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_VH12_5P_LP <- mutate(cdr3_length_37_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_37_VH12_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_VH12_5P_LP_count <- count(cdr3_length_37_VH12_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_VH12_5P_LP_count <- mutate(cdr3_length_37_VH12_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_VH12_5P_LP_count <- mutate(cdr3_length_37_VH12_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_37_VH12 <- rbind(cdr3_length_37_VH12_5N_LN_count, cdr3_length_37_VH12_5P_LP_count)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_VH12_RAG_5N_LN <- rbind(pull_37_seq_VH12RAG_1_5N_LN_2, pull_37_seq_VH12RAG_2_5N_LN_2)
cdr3_length_37_VH12_RAG_5N_LN <- rbind(cdr3_length_37_VH12_RAG_5N_LN, pull_37_seq_VH12RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_VH12_RAG_5N_LN <- mutate(cdr3_length_37_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_37_VH12_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_VH12_RAG_5N_LN_count <- count(cdr3_length_37_VH12_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_VH12_RAG_5N_LN_count <- mutate(cdr3_length_37_VH12_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_VH12_RAG_5N_LN_count <- mutate(cdr3_length_37_VH12_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_37_VH12_RAG_5P_LP <- rbind(pull_37_seq_VH12RAG_1_5P_LP_2, pull_37_seq_VH12RAG_2_5P_LP_2)
cdr3_length_37_VH12_RAG_5P_LP <- rbind(cdr3_length_37_VH12_RAG_5P_LP, pull_37_seq_VH12RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_37_VH12_RAG_5P_LP <- mutate(cdr3_length_37_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_37_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_37_VH12_RAG_5P_LP_count <- count(cdr3_length_37_VH12_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_37_VH12_RAG_5P_LP_count <- mutate(cdr3_length_37_VH12_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_37_VH12_RAG_5P_LP_count <- mutate(cdr3_length_37_VH12_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_37_VH12_RAG <- rbind(cdr3_length_37_VH12_RAG_5N_LN_count, cdr3_length_37_VH12_RAG_5P_LP_count)
#
# Graph
#
# red and black colors
#
dodge<-position_dodge(width=2.75, preserve = "single")
#
#WT
#
plot_cdr3_length_37_WT <- ggplot(data=cdr3_length_37_WT, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk3-7 CDR3 Lengths for WT1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.lticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_37_WT.jpeg", width=14, height=8)
#
#dnRAG1
#
plot_cdr3_length_37_RAG <- ggplot(data=cdr3_length_37_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk3-7 CDR3 Lengths for dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_37_RAG.jpeg", width=14, height=8)
#
#VH12
#
plot_cdr3_length_37_VH12 <- ggplot(data=cdr3_length_37_VH12, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk3-7 CDR3 Lengths for VH121 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_37_VH12.jpeg", width=14, height=8)
#
#VH12 RAG
#
plot_cdr3_length_37_VH12_RAG <- ggplot(data=cdr3_length_37_VH12_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk3-7 CDR3 Lengths for VH12 dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_37_VH12_RAG.jpeg", width=14, height=8)
#
# Vk 4-86
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_WT_5N_LN <- rbind(pull_486_seq_WT_1_5N_LN_2, pull_486_seq_WT_2_5N_LN_2)
cdr3_length_486_WT_5N_LN <- rbind(cdr3_length_486_WT_5N_LN, pull_486_seq_WT_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_WT_5N_LN <- mutate(cdr3_length_486_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_486_WT_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_WT_5N_LN_count <- count(cdr3_length_486_WT_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_WT_5N_LN_count <- mutate(cdr3_length_486_WT_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_WT_5N_LN_count <- mutate(cdr3_length_486_WT_5N_LN_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_486_WT <- cdr3_length_486_WT_5N_LN_count
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_RAG_5N_LN <- rbind(pull_486_seq_RAG_1_5N_LN_2, pull_486_seq_RAG_2_5N_LN_2)
cdr3_length_486_RAG_5N_LN <- rbind(cdr3_length_486_RAG_5N_LN, pull_486_seq_RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_RAG_5N_LN <- mutate(cdr3_length_486_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_486_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_RAG_5N_LN_count <- count(cdr3_length_486_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_RAG_5N_LN_count <- mutate(cdr3_length_486_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_RAG_5N_LN_count <- mutate(cdr3_length_486_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_RAG_5P_LN <- rbind(pull_486_seq_RAG_1_5P_LN_2, pull_486_seq_RAG_2_5P_LN_2)
cdr3_length_486_RAG_5P_LN <- rbind(cdr3_length_486_RAG_5P_LN, pull_486_seq_RAG_3_5P_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_RAG_5P_LN <- mutate(cdr3_length_486_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_486_RAG_5P_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_RAG_5P_LN_count <- count(cdr3_length_486_RAG_5P_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_RAG_5P_LN_count <- mutate(cdr3_length_486_RAG_5P_LN_count, POPULATION = paste("CD5+ PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_RAG_5P_LN_count <- mutate(cdr3_length_486_RAG_5P_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_RAG_5P_LP <- rbind(pull_486_seq_RAG_1_5P_LP_2, pull_486_seq_RAG_2_5P_LP_2)
cdr3_length_486_RAG_5P_LP <- rbind(cdr3_length_486_RAG_5P_LP, pull_486_seq_RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_RAG_5P_LP <- mutate(cdr3_length_486_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_486_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_RAG_5P_LP_count <- count(cdr3_length_486_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_RAG_5P_LP_count <- mutate(cdr3_length_486_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_RAG_5P_LP_count <- mutate(cdr3_length_486_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_486_RAG <- rbind(cdr3_length_486_RAG_5N_LN_count, cdr3_length_486_RAG_5P_LP_count)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_VH12_5N_LN <- rbind(pull_486_seq_VH12_1_5N_LN_2, pull_486_seq_VH12_2_5N_LN_2)
cdr3_length_486_VH12_5N_LN <- rbind(cdr3_length_486_VH12_5N_LN, pull_486_seq_VH12_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_VH12_5N_LN <- mutate(cdr3_length_486_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_486_VH12_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_VH12_5N_LN_count <- count(cdr3_length_486_VH12_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_VH12_5N_LN_count <- mutate(cdr3_length_486_VH12_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_VH12_5N_LN_count <- mutate(cdr3_length_486_VH12_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_VH12_5P_LP <- rbind(pull_486_seq_VH12_1_5P_LP_2, pull_486_seq_VH12_2_5P_LP_2)
cdr3_length_486_VH12_5P_LP <- rbind(cdr3_length_486_VH12_5P_LP, pull_486_seq_VH12_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_VH12_5P_LP <- mutate(cdr3_length_486_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_486_VH12_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_VH12_5P_LP_count <- count(cdr3_length_486_VH12_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_VH12_5P_LP_count <- mutate(cdr3_length_486_VH12_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_VH12_5P_LP_count <- mutate(cdr3_length_486_VH12_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_486_VH12 <- rbind(cdr3_length_486_VH12_5N_LN_count, cdr3_length_486_VH12_5P_LP_count)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_VH12_RAG_5N_LN <- rbind(pull_486_seq_VH12RAG_1_5N_LN_2, pull_486_seq_VH12RAG_2_5N_LN_2)
cdr3_length_486_VH12_RAG_5N_LN <- rbind(cdr3_length_486_VH12_RAG_5N_LN, pull_486_seq_VH12RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_VH12_RAG_5N_LN <- mutate(cdr3_length_486_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_486_VH12_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_VH12_RAG_5N_LN_count <- count(cdr3_length_486_VH12_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_VH12_RAG_5N_LN_count <- mutate(cdr3_length_486_VH12_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_VH12_RAG_5N_LN_count <- mutate(cdr3_length_486_VH12_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_486_VH12_RAG_5P_LP <- rbind(pull_486_seq_VH12RAG_1_5P_LP_2, pull_486_seq_VH12RAG_2_5P_LP_2)
cdr3_length_486_VH12_RAG_5P_LP <- rbind(cdr3_length_486_VH12_RAG_5P_LP, pull_486_seq_VH12RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_486_VH12_RAG_5P_LP <- mutate(cdr3_length_486_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_486_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_486_VH12_RAG_5P_LP_count <- count(cdr3_length_486_VH12_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_486_VH12_RAG_5P_LP_count <- mutate(cdr3_length_486_VH12_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_486_VH12_RAG_5P_LP_count <- mutate(cdr3_length_486_VH12_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_486_VH12_RAG <- rbind(cdr3_length_486_VH12_RAG_5N_LN_count, cdr3_length_486_VH12_RAG_5P_LP_count)
#
# Graph
#
# red and black colors
#
dodge<-position_dodge(width=2.75, preserve = "single")
#
# WT
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_486_WT <- ggplot(data=cdr3_length_486_WT, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-86 CDR3 Lengths for WT1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_486_WT.jpeg", width=14, height=8)
#
# dnRAG1
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_486_RAG <- ggplot(data=cdr3_length_486_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-86 CDR3 Lengths for dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_486_RAG.jpeg", width=14, height=8)
#
# VH12
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_486_VH12 <- ggplot(data=cdr3_length_486_VH12, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-86 CDR3 Lengths for VH121 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_486_VH12.jpeg", width=14, height=8)
#
# VH12 RAG
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_486_VH12_RAG <- ggplot(data=cdr3_length_486_VH12_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-86 CDR3 Lengths for VH12 dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_486_VH12_RAG.jpeg", width=14, height=8)
#
#
# Vk 4-91
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_WT_5N_LN <- rbind(pull_491_seq_WT_1_5N_LN_2, pull_491_seq_WT_2_5N_LN_2)
cdr3_length_491_WT_5N_LN <- rbind(cdr3_length_491_WT_5N_LN, pull_491_seq_WT_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_WT_5N_LN <- mutate(cdr3_length_491_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_491_WT_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_WT_5N_LN_count <- count(cdr3_length_491_WT_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_WT_5N_LN_count <- mutate(cdr3_length_491_WT_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_WT_5N_LN_count <- mutate(cdr3_length_491_WT_5N_LN_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_491_WT <- cdr3_length_491_WT_5N_LN_count
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_RAG_5N_LN <- rbind(pull_491_seq_RAG_1_5N_LN_2, pull_491_seq_RAG_2_5N_LN_2)
cdr3_length_491_RAG_5N_LN <- rbind(cdr3_length_491_RAG_5N_LN, pull_491_seq_RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_RAG_5N_LN <- mutate(cdr3_length_491_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_491_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_RAG_5N_LN_count <- count(cdr3_length_491_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_RAG_5N_LN_count <- mutate(cdr3_length_491_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_RAG_5N_LN_count <- mutate(cdr3_length_491_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_RAG_5P_LN <- rbind(pull_491_seq_RAG_1_5P_LN_2, pull_491_seq_RAG_2_5P_LN_2)
cdr3_length_491_RAG_5P_LN <- rbind(cdr3_length_491_RAG_5P_LN, pull_491_seq_RAG_3_5P_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_RAG_5P_LN <- mutate(cdr3_length_491_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_491_RAG_5P_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_RAG_5P_LN_count <- count(cdr3_length_491_RAG_5P_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_RAG_5P_LN_count <- mutate(cdr3_length_491_RAG_5P_LN_count, POPULATION = paste("CD5+ PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_RAG_5P_LN_count <- mutate(cdr3_length_491_RAG_5P_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_RAG_5P_LP <- rbind(pull_491_seq_RAG_1_5P_LP_2, pull_491_seq_RAG_2_5P_LP_2)
cdr3_length_491_RAG_5P_LP <- rbind(cdr3_length_491_RAG_5P_LP, pull_491_seq_RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_RAG_5P_LP <- mutate(cdr3_length_491_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_491_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_RAG_5P_LP_count <- count(cdr3_length_491_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_RAG_5P_LP_count <- mutate(cdr3_length_491_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_RAG_5P_LP_count <- mutate(cdr3_length_491_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_491_RAG <- rbind(cdr3_length_491_RAG_5N_LN_count, cdr3_length_491_RAG_5P_LP_count)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_VH12_5N_LN <- rbind(pull_491_seq_VH12_1_5N_LN_2, pull_491_seq_VH12_2_5N_LN_2)
cdr3_length_491_VH12_5N_LN <- rbind(cdr3_length_491_VH12_5N_LN, pull_491_seq_VH12_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_VH12_5N_LN <- mutate(cdr3_length_491_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_491_VH12_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_VH12_5N_LN_count <- count(cdr3_length_491_VH12_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_VH12_5N_LN_count <- mutate(cdr3_length_491_VH12_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_VH12_5N_LN_count <- mutate(cdr3_length_491_VH12_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_VH12_5P_LP <- rbind(pull_491_seq_VH12_1_5P_LP_2, pull_491_seq_VH12_2_5P_LP_2)
cdr3_length_491_VH12_5P_LP <- rbind(cdr3_length_491_VH12_5P_LP, pull_491_seq_VH12_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_VH12_5P_LP <- mutate(cdr3_length_491_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_491_VH12_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_VH12_5P_LP_count <- count(cdr3_length_491_VH12_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_VH12_5P_LP_count <- mutate(cdr3_length_491_VH12_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_VH12_5P_LP_count <- mutate(cdr3_length_491_VH12_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_491_VH12 <- rbind(cdr3_length_491_VH12_5N_LN_count, cdr3_length_491_VH12_5P_LP_count)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_VH12_RAG_5N_LN <- rbind(pull_491_seq_VH12RAG_1_5N_LN_2, pull_491_seq_VH12RAG_2_5N_LN_2)
cdr3_length_491_VH12_RAG_5N_LN <- rbind(cdr3_length_491_VH12_RAG_5N_LN, pull_491_seq_VH12RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_VH12_RAG_5N_LN <- mutate(cdr3_length_491_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_491_VH12_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_VH12_RAG_5N_LN_count <- count(cdr3_length_491_VH12_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_VH12_RAG_5N_LN_count <- mutate(cdr3_length_491_VH12_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_VH12_RAG_5N_LN_count <- mutate(cdr3_length_491_VH12_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_491_VH12_RAG_5P_LP <- rbind(pull_491_seq_VH12RAG_1_5P_LP_2, pull_491_seq_VH12RAG_2_5P_LP_2)
cdr3_length_491_VH12_RAG_5P_LP <- rbind(cdr3_length_491_VH12_RAG_5P_LP, pull_491_seq_VH12RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_491_VH12_RAG_5P_LP <- mutate(cdr3_length_491_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_491_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_491_VH12_RAG_5P_LP_count <- count(cdr3_length_491_VH12_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_491_VH12_RAG_5P_LP_count <- mutate(cdr3_length_491_VH12_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_491_VH12_RAG_5P_LP_count <- mutate(cdr3_length_491_VH12_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_491_VH12_RAG <- rbind(cdr3_length_491_VH12_RAG_5N_LN_count, cdr3_length_491_VH12_RAG_5P_LP_count)
#
# Graph
#
# red and black colors
#
dodge<-position_dodge(width=2.75, preserve = "single")
#
# WT
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_491_WT <- ggplot(data=cdr3_length_491_WT, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-91 CDR3 Lengths for WT1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_491_WT.jpeg", width=14, height=8)
#
# dnRAG1
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_491_RAG <- ggplot(data=cdr3_length_491_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-91 CDR3 Lengths for dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_491_RAG.jpeg", width=14, height=8)
#
# VH12
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_491_VH12 <- ggplot(data=cdr3_length_491_VH12, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-91 CDR3 Lengths for VH121 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_491_VH12.jpeg", width=14, height=8)
#
# VH12 RAG
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_491_VH12_RAG <- ggplot(data=cdr3_length_491_VH12_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk4-91 CDR3 Lengths for VH12 dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_491_VH12_RAG.jpeg", width=14, height=8)
#
#
# Vk 14-126
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_WT_5N_LN <- rbind(pull_14126_seq_WT_1_5N_LN_2, pull_14126_seq_WT_2_5N_LN_2)
cdr3_length_14126_WT_5N_LN <- rbind(cdr3_length_14126_WT_5N_LN, pull_14126_seq_WT_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_WT_5N_LN <- mutate(cdr3_length_14126_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_14126_WT_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_WT_5N_LN_count <- count(cdr3_length_14126_WT_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_WT_5N_LN_count <- mutate(cdr3_length_14126_WT_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_WT_5N_LN_count <- mutate(cdr3_length_14126_WT_5N_LN_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_14126_WT <- cdr3_length_14126_WT_5N_LN_count
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_RAG_5N_LN <- rbind(pull_14126_seq_RAG_1_5N_LN_2, pull_14126_seq_RAG_2_5N_LN_2)
cdr3_length_14126_RAG_5N_LN <- rbind(cdr3_length_14126_RAG_5N_LN, pull_14126_seq_RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_RAG_5N_LN <- mutate(cdr3_length_14126_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_14126_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_RAG_5N_LN_count <- count(cdr3_length_14126_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_RAG_5N_LN_count <- mutate(cdr3_length_14126_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_RAG_5N_LN_count <- mutate(cdr3_length_14126_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_RAG_5P_LN <- rbind(pull_14126_seq_RAG_1_5P_LN_2, pull_14126_seq_RAG_2_5P_LN_2)
cdr3_length_14126_RAG_5P_LN <- rbind(cdr3_length_14126_RAG_5P_LN, pull_14126_seq_RAG_3_5P_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_RAG_5P_LN <- mutate(cdr3_length_14126_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_14126_RAG_5P_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_RAG_5P_LN_count <- count(cdr3_length_14126_RAG_5P_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_RAG_5P_LN_count <- mutate(cdr3_length_14126_RAG_5P_LN_count, POPULATION = paste("CD5+ PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_RAG_5P_LN_count <- mutate(cdr3_length_14126_RAG_5P_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_RAG_5P_LP <- rbind(pull_14126_seq_RAG_1_5P_LP_2, pull_14126_seq_RAG_2_5P_LP_2)
cdr3_length_14126_RAG_5P_LP <- rbind(cdr3_length_14126_RAG_5P_LP, pull_14126_seq_RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_RAG_5P_LP <- mutate(cdr3_length_14126_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_14126_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_RAG_5P_LP_count <- count(cdr3_length_14126_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_RAG_5P_LP_count <- mutate(cdr3_length_14126_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_RAG_5P_LP_count <- mutate(cdr3_length_14126_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_14126_RAG <- rbind(cdr3_length_14126_RAG_5N_LN_count, cdr3_length_14126_RAG_5P_LP_count)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_VH12_5N_LN <- rbind(pull_14126_seq_VH12_1_5N_LN_2, pull_14126_seq_VH12_2_5N_LN_2)
cdr3_length_14126_VH12_5N_LN <- rbind(cdr3_length_14126_VH12_5N_LN, pull_14126_seq_VH12_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_VH12_5N_LN <- mutate(cdr3_length_14126_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_14126_VH12_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_VH12_5N_LN_count <- count(cdr3_length_14126_VH12_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_VH12_5N_LN_count <- mutate(cdr3_length_14126_VH12_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_VH12_5N_LN_count <- mutate(cdr3_length_14126_VH12_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_VH12_5P_LP <- rbind(pull_14126_seq_VH12_1_5P_LP_2, pull_14126_seq_VH12_2_5P_LP_2)
cdr3_length_14126_VH12_5P_LP <- rbind(cdr3_length_14126_VH12_5P_LP, pull_14126_seq_VH12_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_VH12_5P_LP <- mutate(cdr3_length_14126_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_14126_VH12_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_VH12_5P_LP_count <- count(cdr3_length_14126_VH12_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_VH12_5P_LP_count <- mutate(cdr3_length_14126_VH12_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_VH12_5P_LP_count <- mutate(cdr3_length_14126_VH12_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_14126_VH12 <- rbind(cdr3_length_14126_VH12_5N_LN_count, cdr3_length_14126_VH12_5P_LP_count)
#
#VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_VH12_RAG_5N_LN <- rbind(pull_14126_seq_VH12RAG_1_5N_LN_2, pull_14126_seq_VH12RAG_2_5N_LN_2)
cdr3_length_14126_VH12_RAG_5N_LN <- rbind(cdr3_length_14126_VH12_RAG_5N_LN, pull_14126_seq_VH12RAG_3_5N_LN_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_VH12_RAG_5N_LN <- mutate(cdr3_length_14126_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_14126_VH12_RAG_5N_LN$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_VH12_RAG_5N_LN_count <- count(cdr3_length_14126_VH12_RAG_5N_LN, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_VH12_RAG_5N_LN_count <- mutate(cdr3_length_14126_VH12_RAG_5N_LN_count, POPULATION = paste("CD5- PtC-, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_VH12_RAG_5N_LN_count <- mutate(cdr3_length_14126_VH12_RAG_5N_LN_count, FREQ = n/sum(n))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_14126_VH12_RAG_5P_LP <- rbind(pull_14126_seq_VH12RAG_1_5P_LP_2, pull_14126_seq_VH12RAG_2_5P_LP_2)
cdr3_length_14126_VH12_RAG_5P_LP <- rbind(cdr3_length_14126_VH12_RAG_5P_LP, pull_14126_seq_VH12RAG_3_5P_LP_2)
#
# Add the length of the CDR3 sequence (in basepairs) to the data frame
#
cdr3_length_14126_VH12_RAG_5P_LP <- mutate(cdr3_length_14126_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_14126_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Count the number of sequences with each CDR3 length
#
cdr3_length_14126_VH12_RAG_5P_LP_count <- count(cdr3_length_14126_VH12_RAG_5P_LP, CDR3_LENGTH)
#
# Add the population type and numbers of sequences to each data frame
#
cdr3_length_14126_VH12_RAG_5P_LP_count <- mutate(cdr3_length_14126_VH12_RAG_5P_LP_count, POPULATION = paste("CD5+ PtC+, n =",sum(n)))
#
# Calculate the occurrence frequency for each CDR3 length
#
cdr3_length_14126_VH12_RAG_5P_LP_count <- mutate(cdr3_length_14126_VH12_RAG_5P_LP_count, FREQ = n/sum(n))
# 
# Create one data frame for each phenotype
#
cdr3_length_14126_VH12_RAG <- rbind(cdr3_length_14126_VH12_RAG_5N_LN_count, cdr3_length_14126_VH12_RAG_5P_LP_count)
#
# Graph
#
# red and black colors
#
dodge<-position_dodge(width=2.75, preserve = "single")
#
# WT
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_14126_WT <- ggplot(data=cdr3_length_14126_WT, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk14-126 CDR3 Lengths for WT1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_14126_WT.jpeg", width=14, height=8)
#
# dnRAG1
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_14126_RAG <- ggplot(data=cdr3_length_14126_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk14-126 CDR3 Lengths for dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_14126_RAG.jpeg", width=14, height=8)
#
# VH12
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_14126_VH12 <- ggplot(data=cdr3_length_14126_VH12, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk14-126 CDR3 Lengths for VH121 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_14126_VH12.jpeg", width=14, height=8)
#
# VH12 RAG
#
# Graph the length distribution of of the CDR3 sequences for each animal phenotype
#
plot_cdr3_length_14126_VH12_RAG <- ggplot(data=cdr3_length_14126_VH12_RAG, mapping=aes(x=CDR3_LENGTH, y = FREQ, fill=POPULATION))+geom_col(position = dodge, width=2.5)+scale_x_continuous(name="CDR3 Length (bp)", breaks=seq(18,36,3))+coord_cartesian(xlim=c(16,38),ylim=c(0,1))+scale_y_continuous(labels=scales::percent)+ylab("% of Total")+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=36, hjust=0.5, vjust=0, color = "black"))+theme(axis.title.x = element_text(margin = margin(t = 20, r = 0, b = 0, l = 0)))+theme(axis.text.y=element_text(size=36, face="bold", hjust=0.5, vjust=0.5, color = "black"))+theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)))+theme(panel.background=element_rect(fill="white"))+ggtitle("Vk14-126 CDR3 Lengths for VH12 dnRAG1 B Cell Subsets")+theme(plot.title=element_text(h=0.5, face="bold", size=36, color="white"))+theme(axis.title = element_text(face="bold", size=36))+theme(axis.ticks.x = element_line(colour = "black", size =2))+theme(axis.ticks.length.x = unit(0.25, "in"))+theme(legend.position = c(0.2, 0.9))+theme(legend.key.size = unit(0.5, "in"))+theme(legend.text = element_text(size=28, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = c("#D62839", "#221E22"))
ggsave("plot_cdr3_length_14126_VH12_RAG.jpeg", width=14, height=8)
#
#
# Statistical analysis of CDR3 length distributions
#
# Vk 3-7
#
# Add the sample type to each respective data frame
#
pull_37_seq_WT_1_5N_LN_2<-mutate(pull_37_seq_WT_1_5N_LN_2, Sample = "WT CD5- PtC-")
pull_37_seq_RAG_1_5N_LN_2<-mutate(pull_37_seq_RAG_1_5N_LN_2, Sample = "dnRAG1 CD5- PtC-")
pull_37_seq_RAG_1_5P_LN_2<-mutate(pull_37_seq_RAG_1_5P_LN_2, Sample = "dnRAG1 CD5+ PtC-")
pull_37_seq_RAG_1_5P_LP_2<-mutate(pull_37_seq_RAG_1_5P_LP_2, Sample = "dnRAG1 CD5+ PtC+")
pull_37_seq_VH12_1_5N_LN_2<-mutate(pull_37_seq_VH12_1_5N_LN_2, Sample = "VH12 CD5- PtC-")
pull_37_seq_VH12_1_5P_LP_2<-mutate(pull_37_seq_VH12_1_5P_LP_2, Sample = "VH12 CD5+ PtC+")
pull_37_seq_VH12RAG_1_5N_LN_2<-mutate(pull_37_seq_VH12RAG_1_5N_LN_2, Sample = "dnRAG1 VH12 CD5- PtC-")
pull_37_seq_VH12RAG_1_5P_LP_2<-mutate(pull_37_seq_VH12RAG_1_5P_LP_2, Sample = "dnRAG1 VH12 CD5+ PtC+")
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_WT_5N_LN <- rbind(pull_37_seq_WT_1_5N_LN_2, pull_37_seq_WT_2_5N_LN_2)
cdr3_length_stat_37_WT_5N_LN <- rbind(cdr3_length_stat_37_WT_5N_LN, pull_37_seq_WT_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_WT_5N_LN <- mutate(cdr3_length_stat_37_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_37_WT_5N_LN$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_37_WT <- cdr3_length_stat_37_WT_5N_LN
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_RAG_5N_LN <- rbind(pull_37_seq_RAG_1_5N_LN_2, pull_37_seq_RAG_2_5N_LN_2)
cdr3_length_stat_37_RAG_5N_LN <- rbind(cdr3_length_stat_37_RAG_5N_LN, pull_37_seq_RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_RAG_5N_LN <- mutate(cdr3_length_stat_37_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_37_RAG_5N_LN$CDR3_IMGT))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_RAG_5P_LN <- rbind(pull_37_seq_RAG_1_5P_LN_2, pull_37_seq_RAG_2_5P_LN_2)
cdr3_length_stat_37_RAG_5P_LN <- rbind(cdr3_length_stat_37_RAG_5P_LN, pull_37_seq_RAG_3_5P_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_RAG_5P_LN <- mutate(cdr3_length_stat_37_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_stat_37_RAG_5P_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_RAG_5P_LP <- rbind(pull_37_seq_RAG_1_5P_LP_2, pull_37_seq_RAG_2_5P_LP_2)
cdr3_length_stat_37_RAG_5P_LP <- rbind(cdr3_length_stat_37_RAG_5P_LP, pull_37_seq_RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_RAG_5P_LP <- mutate(cdr3_length_stat_37_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_37_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_37_RAG <- rbind(cdr3_length_stat_37_RAG_5N_LN, cdr3_length_stat_37_RAG_5P_LP)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_VH12_5N_LN <- rbind(pull_37_seq_VH12_1_5N_LN_2, pull_37_seq_VH12_2_5N_LN_2)
cdr3_length_stat_37_VH12_5N_LN <- rbind(cdr3_length_stat_37_VH12_5N_LN, pull_37_seq_VH12_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_VH12_5N_LN <- mutate(cdr3_length_stat_37_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_37_VH12_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_VH12_5P_LP <- rbind(pull_37_seq_VH12_1_5P_LP_2, pull_37_seq_VH12_2_5P_LP_2)
cdr3_length_stat_37_VH12_5P_LP <- rbind(cdr3_length_stat_37_VH12_5P_LP, pull_37_seq_VH12_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_VH12_5P_LP <- mutate(cdr3_length_stat_37_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_37_VH12_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_VH12_RAG_5N_LN <- rbind(pull_37_seq_VH12RAG_1_5N_LN_2, pull_37_seq_VH12RAG_2_5N_LN_2)
cdr3_length_stat_37_VH12_RAG_5N_LN <- rbind(cdr3_length_stat_37_VH12_RAG_5N_LN, pull_37_seq_VH12RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_VH12_RAG_5N_LN <- mutate(cdr3_length_stat_37_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_37_VH12_RAG_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_37_VH12_RAG_5P_LP <- rbind(pull_37_seq_VH12RAG_1_5P_LP_2, pull_37_seq_VH12RAG_2_5P_LP_2)
cdr3_length_stat_37_VH12_RAG_5P_LP <- rbind(cdr3_length_stat_37_VH12_RAG_5P_LP, pull_37_seq_VH12RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_37_VH12_RAG_5P_LP <- mutate(cdr3_length_stat_37_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_37_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_37_VH12_RAG <- rbind(cdr3_length_stat_37_VH12_RAG_5N_LN, cdr3_length_stat_37_VH12_RAG_5P_LP)
#
# Compile the tables for the different phenotypes into one data frame
#
cdr3_length_stat_37 <- bind_rows(cdr3_length_stat_37_WT, cdr3_length_stat_37_RAG, cdr3_length_stat_37_VH12, cdr3_length_stat_37_VH12_RAG)
#
# Assign factors and levels to the sample types to ensure the ANOVA output is in the preferred order
#
cdr3_length_stat_37$Sample <- factor(cdr3_length_stat_37$Sample, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))
#
# Calculate the mean and standard deviation of the CDR3 length distributions
#
cdr3_length_stat_37_summary <- group_by(cdr3_length_stat_37, Sample) %>%
  summarise(
    count = n(),
    mean = mean(CDR3_LENGTH, na.rm = TRUE),
    sd = sd(CDR3_LENGTH, na.rm = TRUE)
  )
cdr3_length_stat_37_summary
#
# Perform a one way ANOVA of the CDR3 length distributions by sample type
#
aov_37_cdr3 <- aov(CDR3_LENGTH ~ Sample, data = cdr3_length_stat_37)
summary(aov_37_cdr3)key
#
# If the ANOVA shows a statistically significant difference, perform a Tukey post hoc analysis
#
aov_tuk_37_cdr3 <- TukeyHSD(aov_37_cdr3)
aov_tuk_37_cdr3
#
# Vk 4-86
#
pull_486_seq_WT_1_5N_LN_2<-mutate(pull_486_seq_WT_1_5N_LN_2, Sample = "WT CD5- PtC-")
pull_486_seq_RAG_1_5N_LN_2<-mutate(pull_486_seq_RAG_1_5N_LN_2, Sample = "dnRAG1 CD5- PtC-")
pull_486_seq_RAG_1_5P_LN_2<-mutate(pull_486_seq_RAG_1_5P_LN_2, Sample = "dnRAG1 CD5+ PtC-")
pull_486_seq_RAG_1_5P_LP_2<-mutate(pull_486_seq_RAG_1_5P_LP_2, Sample = "dnRAG1 CD5+ PtC+")
pull_486_seq_VH12_1_5N_LN_2<-mutate(pull_486_seq_VH12_1_5N_LN_2, Sample = "VH12 CD5- PtC-")
pull_486_seq_VH12_1_5P_LP_2<-mutate(pull_486_seq_VH12_1_5P_LP_2, Sample = "VH12 CD5+ PtC+")
pull_486_seq_VH12RAG_1_5N_LN_2<-mutate(pull_486_seq_VH12RAG_1_5N_LN_2, Sample = "dnRAG1 VH12 CD5- PtC-")
pull_486_seq_VH12RAG_1_5P_LP_2<-mutate(pull_486_seq_VH12RAG_1_5P_LP_2, Sample = "dnRAG1 VH12 CD5+ PtC+")
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_WT_5N_LN <- rbind(pull_486_seq_WT_1_5N_LN_2, pull_486_seq_WT_2_5N_LN_2)
cdr3_length_stat_486_WT_5N_LN <- rbind(cdr3_length_stat_486_WT_5N_LN, pull_486_seq_WT_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_WT_5N_LN <- mutate(cdr3_length_stat_486_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_486_WT_5N_LN$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_486_WT <- cdr3_length_stat_486_WT_5N_LN
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_RAG_5N_LN <- rbind(pull_486_seq_RAG_1_5N_LN_2, pull_486_seq_RAG_2_5N_LN_2)
cdr3_length_stat_486_RAG_5N_LN <- rbind(cdr3_length_stat_486_RAG_5N_LN, pull_486_seq_RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_RAG_5N_LN <- mutate(cdr3_length_stat_486_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_486_RAG_5N_LN$CDR3_IMGT))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_RAG_5P_LN <- rbind(pull_486_seq_RAG_1_5P_LN_2, pull_486_seq_RAG_2_5P_LN_2)
cdr3_length_stat_486_RAG_5P_LN <- rbind(cdr3_length_stat_486_RAG_5P_LN, pull_486_seq_RAG_3_5P_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_RAG_5P_LN <- mutate(cdr3_length_stat_486_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_stat_486_RAG_5P_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_RAG_5P_LP <- rbind(pull_486_seq_RAG_1_5P_LP_2, pull_486_seq_RAG_2_5P_LP_2)
cdr3_length_stat_486_RAG_5P_LP <- rbind(cdr3_length_stat_486_RAG_5P_LP, pull_486_seq_RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_RAG_5P_LP <- mutate(cdr3_length_stat_486_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_486_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_486_RAG <- rbind(cdr3_length_stat_486_RAG_5N_LN, cdr3_length_stat_486_RAG_5P_LP)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_VH12_5N_LN <- rbind(pull_486_seq_VH12_1_5N_LN_2, pull_486_seq_VH12_2_5N_LN_2)
cdr3_length_stat_486_VH12_5N_LN <- rbind(cdr3_length_stat_486_VH12_5N_LN, pull_486_seq_VH12_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_VH12_5N_LN <- mutate(cdr3_length_stat_486_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_486_VH12_5N_LN$CDR3_IMGT))
#
# 5P LP
#
cdr3_length_stat_486_VH12_5P_LP <- rbind(pull_486_seq_VH12_1_5P_LP_2, pull_486_seq_VH12_2_5P_LP_2)
cdr3_length_stat_486_VH12_5P_LP <- rbind(cdr3_length_stat_486_VH12_5P_LP, pull_486_seq_VH12_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_VH12_5P_LP <- mutate(cdr3_length_stat_486_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_486_VH12_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_486_VH12 <- rbind(cdr3_length_stat_486_VH12_5N_LN, cdr3_length_stat_486_VH12_5P_LP)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_VH12_RAG_5N_LN <- rbind(pull_486_seq_VH12RAG_1_5N_LN_2, pull_486_seq_VH12RAG_2_5N_LN_2)
cdr3_length_stat_486_VH12_RAG_5N_LN <- rbind(cdr3_length_stat_486_VH12_RAG_5N_LN, pull_486_seq_VH12RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_VH12_RAG_5N_LN <- mutate(cdr3_length_stat_486_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_486_VH12_RAG_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_486_VH12_RAG_5P_LP <- rbind(pull_486_seq_VH12RAG_1_5P_LP_2, pull_486_seq_VH12RAG_2_5P_LP_2)
cdr3_length_stat_486_VH12_RAG_5P_LP <- rbind(cdr3_length_stat_486_VH12_RAG_5P_LP, pull_486_seq_VH12RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_486_VH12_RAG_5P_LP <- mutate(cdr3_length_stat_486_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_486_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_486_VH12_RAG <- rbind(cdr3_length_stat_486_VH12_RAG_5N_LN, cdr3_length_stat_486_VH12_RAG_5P_LP)
#
# Compile the tables for the different phenotypes into one data frame
#
cdr3_length_stat_486 <- bind_rows(cdr3_length_stat_486_WT, cdr3_length_stat_486_RAG, cdr3_length_stat_486_VH12, cdr3_length_stat_486_VH12_RAG)
#
# Assign factors and levels to the sample types to ensure the ANOVA output is in the preferred order
#
cdr3_length_stat_486$Sample <- factor(cdr3_length_stat_486$Sample, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))
#
# Calculate the mean and standard deviation of the CDR3 length distributions
#
cdr3_length_stat_486_summary <- group_by(cdr3_length_stat_486, Sample) %>%
  summarise(
    count = n(),
    mean = mean(CDR3_LENGTH, na.rm = TRUE),
    sd = sd(CDR3_LENGTH, na.rm = TRUE)
  )
cdr3_length_stat_486_summary
#
# Perform a one way ANOVA of the CDR3 length distributions by sample type
#
aov_486_cdr3 <- aov(CDR3_LENGTH ~ Sample, data = cdr3_length_stat_486)
summary(aov_486_cdr3)
#
# If the ANOVA shows a statistically significant difference, perform a Tukey post hoc analysis
#
aov_tuk_486_cdr3 <- TukeyHSD(aov_486_cdr3)
aov_tuk_486_cdr3
#
# Vk 4-91
#
pull_491_seq_WT_1_5N_LN_2<-mutate(pull_491_seq_WT_1_5N_LN_2, Sample = "WT CD5- PtC-")
pull_491_seq_RAG_1_5N_LN_2<-mutate(pull_491_seq_RAG_1_5N_LN_2, Sample = "dnRAG1 CD5- PtC-")
pull_491_seq_RAG_1_5P_LN_2<-mutate(pull_491_seq_RAG_1_5P_LN_2, Sample = "dnRAG1 CD5+ PtC-")
pull_491_seq_RAG_1_5P_LP_2<-mutate(pull_491_seq_RAG_1_5P_LP_2, Sample = "dnRAG1 CD5+ PtC+")
pull_491_seq_VH12_1_5N_LN_2<-mutate(pull_491_seq_VH12_1_5N_LN_2, Sample = "VH12 CD5- PtC-")
pull_491_seq_VH12_1_5P_LP_2<-mutate(pull_491_seq_VH12_1_5P_LP_2, Sample = "VH12 CD5+ PtC+")
pull_491_seq_VH12RAG_1_5N_LN_2<-mutate(pull_491_seq_VH12RAG_1_5N_LN_2, Sample = "dnRAG1 VH12 CD5- PtC-")
pull_491_seq_VH12RAG_1_5P_LP_2<-mutate(pull_491_seq_VH12RAG_1_5P_LP_2, Sample = "dnRAG1 VH12 CD5+ PtC+")
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_WT_5N_LN <- rbind(pull_491_seq_WT_1_5N_LN_2, pull_491_seq_WT_2_5N_LN_2)
cdr3_length_stat_491_WT_5N_LN <- rbind(cdr3_length_stat_491_WT_5N_LN, pull_491_seq_WT_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_WT_5N_LN <- mutate(cdr3_length_stat_491_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_491_WT_5N_LN$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_491_WT <- cdr3_length_stat_491_WT_5N_LN
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_RAG_5N_LN <- rbind(pull_491_seq_RAG_1_5N_LN_2, pull_491_seq_RAG_2_5N_LN_2)
cdr3_length_stat_491_RAG_5N_LN <- rbind(cdr3_length_stat_491_RAG_5N_LN, pull_491_seq_RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_RAG_5N_LN <- mutate(cdr3_length_stat_491_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_491_RAG_5N_LN$CDR3_IMGT))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_RAG_5P_LN <- rbind(pull_491_seq_RAG_1_5P_LN_2, pull_491_seq_RAG_2_5P_LN_2)
cdr3_length_stat_491_RAG_5P_LN <- rbind(cdr3_length_stat_491_RAG_5P_LN, pull_491_seq_RAG_3_5P_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_RAG_5P_LN <- mutate(cdr3_length_stat_491_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_stat_491_RAG_5P_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_RAG_5P_LP <- rbind(pull_491_seq_RAG_1_5P_LP_2, pull_491_seq_RAG_2_5P_LP_2)
cdr3_length_stat_491_RAG_5P_LP <- rbind(cdr3_length_stat_491_RAG_5P_LP, pull_491_seq_RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_RAG_5P_LP <- mutate(cdr3_length_stat_491_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_491_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_491_RAG <- rbind(cdr3_length_stat_491_RAG_5N_LN, cdr3_length_stat_491_RAG_5P_LP)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_VH12_5N_LN <- rbind(pull_491_seq_VH12_1_5N_LN_2, pull_491_seq_VH12_2_5N_LN_2)
cdr3_length_stat_491_VH12_5N_LN <- rbind(cdr3_length_stat_491_VH12_5N_LN, pull_491_seq_VH12_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_VH12_5N_LN <- mutate(cdr3_length_stat_491_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_491_VH12_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_VH12_5P_LP <- rbind(pull_491_seq_VH12_1_5P_LP_2, pull_491_seq_VH12_2_5P_LP_2)
cdr3_length_stat_491_VH12_5P_LP <- rbind(cdr3_length_stat_491_VH12_5P_LP, pull_491_seq_VH12_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_VH12_5P_LP <- mutate(cdr3_length_stat_491_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_491_VH12_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_491_VH12 <- rbind(cdr3_length_stat_491_VH12_5N_LN, cdr3_length_stat_491_VH12_5P_LP)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_VH12_RAG_5N_LN <- rbind(pull_491_seq_VH12RAG_1_5N_LN_2, pull_491_seq_VH12RAG_2_5N_LN_2)
cdr3_length_stat_491_VH12_RAG_5N_LN <- rbind(cdr3_length_stat_491_VH12_RAG_5N_LN, pull_491_seq_VH12RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_VH12_RAG_5N_LN <- mutate(cdr3_length_stat_491_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_491_VH12_RAG_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_491_VH12_RAG_5P_LP <- rbind(pull_491_seq_VH12RAG_1_5P_LP_2, pull_491_seq_VH12RAG_2_5P_LP_2)
cdr3_length_stat_491_VH12_RAG_5P_LP <- rbind(cdr3_length_stat_491_VH12_RAG_5P_LP, pull_491_seq_VH12RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_491_VH12_RAG_5P_LP <- mutate(cdr3_length_stat_491_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_491_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_491_VH12_RAG <- rbind(cdr3_length_stat_491_VH12_RAG_5N_LN, cdr3_length_stat_491_VH12_RAG_5P_LP)
#
# Compile the tables for the different phenotypes into one data frame
#
cdr3_length_stat_491 <- bind_rows(cdr3_length_stat_491_WT, cdr3_length_stat_491_RAG, cdr3_length_stat_491_VH12, cdr3_length_stat_491_VH12_RAG)
#
# Assign factors and levels to the sample types to ensure the ANOVA output is in the preferred order
#
cdr3_length_stat_491$Sample <- factor(cdr3_length_stat_491$Sample, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))
#
# Calculate the mean and standard deviation of the CDR3 length distributions
#
cdr3_length_stat_491_summary <- group_by(cdr3_length_stat_491, Sample) %>%
  summarise(
    count = n(),
    mean = mean(CDR3_LENGTH, na.rm = TRUE),
    sd = sd(CDR3_LENGTH, na.rm = TRUE)
  )
cdr3_length_stat_491_summary
#
# Perform a one way ANOVA of the CDR3 length distributions by sample type
#
aov_491_cdr3 <- aov(CDR3_LENGTH ~ Sample, data = cdr3_length_stat_491)
summary(aov_491_cdr3)
#
# If the ANOVA shows a statistically significant difference, perform a Tukey post hoc analysis
#
aov_tuk_491_cdr3 <- TukeyHSD(aov_491_cdr3)
aov_tuk_491_cdr3
#
#
# Vk 14-126
#
pull_14126_seq_WT_1_5N_LN_2<-mutate(pull_14126_seq_WT_1_5N_LN_2, Sample = "WT CD5- PtC-")
pull_14126_seq_RAG_1_5N_LN_2<-mutate(pull_14126_seq_RAG_1_5N_LN_2, Sample = "dnRAG1 CD5- PtC-")
pull_14126_seq_RAG_1_5P_LN_2<-mutate(pull_14126_seq_RAG_1_5P_LN_2, Sample = "dnRAG1 CD5+ PtC-")
pull_14126_seq_RAG_1_5P_LP_2<-mutate(pull_14126_seq_RAG_1_5P_LP_2, Sample = "dnRAG1 CD5+ PtC+")
pull_14126_seq_VH12_1_5N_LN_2<-mutate(pull_14126_seq_VH12_1_5N_LN_2, Sample = "VH12 CD5- PtC-")
pull_14126_seq_VH12_1_5P_LP_2<-mutate(pull_14126_seq_VH12_1_5P_LP_2, Sample = "VH12 CD5+ PtC+")
pull_14126_seq_VH12RAG_1_5N_LN_2<-mutate(pull_14126_seq_VH12RAG_1_5N_LN_2, Sample = "dnRAG1 VH12 CD5- PtC-")
pull_14126_seq_VH12RAG_1_5P_LP_2<-mutate(pull_14126_seq_VH12RAG_1_5P_LP_2, Sample = "dnRAG1 VH12 CD5+ PtC+")
#
# WT
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_WT_5N_LN <- rbind(pull_14126_seq_WT_1_5N_LN_2, pull_14126_seq_WT_2_5N_LN_2)
cdr3_length_stat_14126_WT_5N_LN <- rbind(cdr3_length_stat_14126_WT_5N_LN, pull_14126_seq_WT_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_WT_5N_LN <- mutate(cdr3_length_stat_14126_WT_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_14126_WT_5N_LN$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_14126_WT <- cdr3_length_stat_14126_WT_5N_LN
#
# RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_RAG_5N_LN <- rbind(pull_14126_seq_RAG_1_5N_LN_2, pull_14126_seq_RAG_2_5N_LN_2)
cdr3_length_stat_14126_RAG_5N_LN <- rbind(cdr3_length_stat_14126_RAG_5N_LN, pull_14126_seq_RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_RAG_5N_LN <- mutate(cdr3_length_stat_14126_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_14126_RAG_5N_LN$CDR3_IMGT))
#
# 5P LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_RAG_5P_LN <- rbind(pull_14126_seq_RAG_1_5P_LN_2, pull_14126_seq_RAG_2_5P_LN_2)
cdr3_length_stat_14126_RAG_5P_LN <- rbind(cdr3_length_stat_14126_RAG_5P_LN, pull_14126_seq_RAG_3_5P_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_RAG_5P_LN <- mutate(cdr3_length_stat_14126_RAG_5P_LN, CDR3_LENGTH = str_length(cdr3_length_stat_14126_RAG_5P_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_RAG_5P_LP <- rbind(pull_14126_seq_RAG_1_5P_LP_2, pull_14126_seq_RAG_2_5P_LP_2)
cdr3_length_stat_14126_RAG_5P_LP <- rbind(cdr3_length_stat_14126_RAG_5P_LP, pull_14126_seq_RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_RAG_5P_LP <- mutate(cdr3_length_stat_14126_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_14126_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_14126_RAG <- rbind(cdr3_length_stat_14126_RAG_5N_LN, cdr3_length_stat_14126_RAG_5P_LP)
#
# VH12
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_VH12_5N_LN <- rbind(pull_14126_seq_VH12_1_5N_LN_2, pull_14126_seq_VH12_2_5N_LN_2)
cdr3_length_stat_14126_VH12_5N_LN <- rbind(cdr3_length_stat_14126_VH12_5N_LN, pull_14126_seq_VH12_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_VH12_5N_LN <- mutate(cdr3_length_stat_14126_VH12_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_14126_VH12_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_VH12_5P_LP <- rbind(pull_14126_seq_VH12_1_5P_LP_2, pull_14126_seq_VH12_2_5P_LP_2)
cdr3_length_stat_14126_VH12_5P_LP <- rbind(cdr3_length_stat_14126_VH12_5P_LP, pull_14126_seq_VH12_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_VH12_5P_LP <- mutate(cdr3_length_stat_14126_VH12_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_14126_VH12_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_14126_VH12 <- rbind(cdr3_length_stat_14126_VH12_5N_LN, cdr3_length_stat_14126_VH12_5P_LP)
#
# VH12_RAG
#
# 5N LN
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_VH12_RAG_5N_LN <- rbind(pull_14126_seq_VH12RAG_1_5N_LN_2, pull_14126_seq_VH12RAG_2_5N_LN_2)
cdr3_length_stat_14126_VH12_RAG_5N_LN <- rbind(cdr3_length_stat_14126_VH12_RAG_5N_LN, pull_14126_seq_VH12RAG_3_5N_LN_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_VH12_RAG_5N_LN <- mutate(cdr3_length_stat_14126_VH12_RAG_5N_LN, CDR3_LENGTH = str_length(cdr3_length_stat_14126_VH12_RAG_5N_LN$CDR3_IMGT))
#
# 5P LP
#
# Combine the replicates into one data frame per sample type
#
cdr3_length_stat_14126_VH12_RAG_5P_LP <- rbind(pull_14126_seq_VH12RAG_1_5P_LP_2, pull_14126_seq_VH12RAG_2_5P_LP_2)
cdr3_length_stat_14126_VH12_RAG_5P_LP <- rbind(cdr3_length_stat_14126_VH12_RAG_5P_LP, pull_14126_seq_VH12RAG_3_5P_LP_2)
#
# Add the CDR3 sequence length (in basepairs)
#
cdr3_length_stat_14126_VH12_RAG_5P_LP <- mutate(cdr3_length_stat_14126_VH12_RAG_5P_LP, CDR3_LENGTH = str_length(cdr3_length_stat_14126_VH12_RAG_5P_LP$CDR3_IMGT))
#
# Create one data frame for each phenotype
#
cdr3_length_stat_14126_VH12_RAG <- rbind(cdr3_length_stat_14126_VH12_RAG_5N_LN, cdr3_length_stat_14126_VH12_RAG_5P_LP)
#
# Compile the tables for the different phenotypes into one data frame
#
cdr3_length_stat_14126 <- bind_rows(cdr3_length_stat_14126_WT, cdr3_length_stat_14126_RAG, cdr3_length_stat_14126_VH12, cdr3_length_stat_14126_VH12_RAG)
#
# Assign factors and levels to the sample types to ensure the ANOVA output is in the preferred order
#
cdr3_length_stat_14126$Sample <- factor(cdr3_length_stat_14126$Sample, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))
#
# Calculate the mean and standard deviation of the CDR3 length distributions
#
cdr3_length_stat_14126_summary <- group_by(cdr3_length_stat_14126, Sample) %>%
  summarise(
    count = n(),
    mean = mean(CDR3_LENGTH, na.rm = TRUE),
    sd = sd(CDR3_LENGTH, na.rm = TRUE)
  )
cdr3_length_stat_14126_summary
#
# Perform a one way ANOVA of the CDR3 length distributions by sample type
#
aov_14126_cdr3 <- aov(CDR3_LENGTH ~ Sample, data = cdr3_length_stat_14126)
summary(aov_14126_cdr3)
#
# If the ANOVA shows a statistically significant difference, perform a Tukey post hoc analysis
#
aov_tuk_14126_cdr3 <- TukeyHSD(aov_14126_cdr3)
aov_tuk_14126_cdr3
#
#