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@khaled-rahman
khaled-rahman committed Oct 14, 2017
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119 changes: 70 additions & 49 deletions CRISPRpred/DrawPlots/barchartsreadcount.R
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Original file line number Diff line number Diff line change
@@ -1,50 +1,71 @@
data <- read.csv("FC_plus_RES.csv")
sgrnas <- data$X30mer
sgrnalist <- unlist(sgrnas)
sgrnaLength <- 30
aVal <- c(1:sgrnaLength) * 0
cVal <- c(1:sgrnaLength) * 0
gVal <- c(1:sgrnaLength) * 0
tVal <- c(1:sgrnaLength) * 0

for (i in 1:length(sgrnalist)) {
s <- toString(sgrnas[i])
for (j in 1:sgrnaLength) {
if (j %in% gregexpr(pattern = "A", s)[[1]]) {
aVal[j] <- aVal[j] + 1
}
if (j %in% gregexpr(pattern = "C", s)[[1]]) {
cVal[j] <- cVal[j] + 1
}
if (j %in% gregexpr(pattern = "G", s)[[1]]) {
gVal[j] <- gVal[j] + 1
}
if (j %in% gregexpr(pattern = "T", s)[[1]]) {
tVal[j] <- tVal[j] + 1
}
}
}
aVal <- aVal / length(sgrnalist)
cVal <- cVal / length(sgrnalist)
gVal <- gVal / length(sgrnalist)
tVal <- tVal / length(sgrnalist)
#cat('A:',aVal, '\n','C:', cVal, '\n','G:', gVal, '\n','T:', tVal,'\n')
#sgrnalist <- unlist(sgrnas)
#print(length(sgrnas))

#xAxis = c(
# "1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"
#)
xAxis = c(
"-4","-3","-2","-1","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","N","G", "G","+1","+2","+3")

data <- matrix(c(aVal,cVal,gVal,tVal),nrow = 4, ncol = 30,byrow = TRUE)
colours <- c("red", "blue", "yellow", "green")
barplot(
data, xlab = "Position", ylab = "Normalized Read Count", cex.lab = 1.2, cex.axis = 1.2, cex.names = 0.6, beside =
TRUE, col = colours, names.arg = xAxis, axes = TRUE
)
legend(
"topleft", c("A","C","G","T"), cex = 1.3, bty =
"n", fill = colours
data <- read.csv("FC_plus_RES.csv")
sgrnas <- data$X30mer
sgrnalist <- unlist(sgrnas)
sgrnaLength <- 30
aVal <- c(1:sgrnaLength) * 0
cVal <- c(1:sgrnaLength) * 0
gVal <- c(1:sgrnaLength) * 0
tVal <- c(1:sgrnaLength) * 0

for (i in 1:length(sgrnalist)) {
s <- toString(sgrnas[i])
for (j in 1:sgrnaLength) {
if (j %in% gregexpr(pattern = "A", s)[[1]]) {
aVal[j] <- aVal[j] + 1
}
if (j %in% gregexpr(pattern = "C", s)[[1]]) {
cVal[j] <- cVal[j] + 1
}
if (j %in% gregexpr(pattern = "G", s)[[1]]) {
gVal[j] <- gVal[j] + 1
}
if (j %in% gregexpr(pattern = "T", s)[[1]]) {
tVal[j] <- tVal[j] + 1
}
}
}
aVal <- aVal / length(sgrnalist)
cVal <- cVal / length(sgrnalist)
gVal <- gVal / length(sgrnalist)
tVal <- tVal / length(sgrnalist)
#cat('A:',aVal, '\n','C:', cVal, '\n','G:', gVal, '\n','T:', tVal,'\n')
#sgrnalist <- unlist(sgrnas)
#print(length(sgrnas))

#xAxis = c(
# "1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30"
#)
xAxis = c(
"-4","-3","-2","-1","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","N","G", "G","+1","+2","+3")

data <- matrix(c(aVal,cVal,gVal,tVal),nrow = 4, ncol = 30,byrow = TRUE)
colours <- c("red", "blue", "yellow", "green")
barplot(
data, xlab = "Position", ylab = "Normalized Read Count", cex.lab = 1.2, cex.axis = 1.2, cex.names = 0.6, beside =
TRUE, col = colours, names.arg = xAxis, axes = TRUE
)
legend(
"topleft", c("A","C","G","T"), cex = 1.3, bty =
"n", fill = colours
)

aps = .8125
api = .78125
agap = .875
com = .965
maps = .28716
mpi = .36242
mgap=.6636
mcom=.91
allscore = c(aps,maps,api,mpi,agap,mgap,com,mcom)
allmat = matrix(allscore, nrow = 2, ncol = 4,byrow = FALSE)
colr = c("red","blue")
xas = c("PSF","PIF","GAP","COM")
barplot(
allmat, xlab = "Feature Category", ylab = "Accuracy and MCC Value", cex.lab = 1.2, cex.axis = 1.2, cex.names = 1.1, beside =
TRUE, col = colr, names.arg = xas, axes = TRUE, ylim = c(0,1)
)
legend(
"topleft", c("Accuracy x 100%", "MCC"), cex = 1.3, bty =
"n", fill = colr
)
26 changes: 13 additions & 13 deletions CRISPRpred/crisprpred/R_src/metrics/calculateclassificationscore.R
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Original file line number Diff line number Diff line change
Expand Up @@ -21,34 +21,34 @@ calculateclassificationscore <- function(name = "Temporary", predictedValue, tru
roccurve <- performance(pred,"tpr","fpr")
par(mar = c(4, 5, 1.98, 1))
plot(roccurve, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "ROCcurve.pdf")
dev.off()
#dev.copy(pdf, "ROCcurve.pdf")
#dev.off()
pr <- performance(pred, "prec", "rec")
par(mar = c(4, 5, 1.98, 1))
plot(pr, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)

dev.copy(pdf, "PRcurve.pdf")
dev.off()
#dev.copy(pdf, "PRcurve.pdf")
#dev.off()
ss <- performance(pred, "sens", "spec")
par(mar = c(4, 5, 1.98, 1))
plot(ss, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "SensSpeci.pdf")
dev.off()
#dev.copy(pdf, "SensSpeci.pdf")
#dev.off()
acc <- performance(pred, "acc")
par(mar = c(4, 5, 1.98, 1))
plot(acc,main = name, cex.main = 1.7, xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "Accuracy.pdf")
dev.off()
#dev.copy(pdf, "Accuracy.pdf")
#dev.off()
mcc <- performance(pred, "mat")
par(mar = c(4, 5, 1.98, 1))
plot(mcc,main = name, cex.main = 1.7,xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "MCCcurve.pdf")
dev.off()
#dev.copy(pdf, "MCCcurve.pdf")
#dev.off()
f <- performance(pred, "f")
par(mar = c(4, 5, 1.98, 1))
plot(f, main = name, cex.main = 1.7, xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "Fmeasure.pdf")
dev.off()
#dev.copy(pdf, "Fmeasure.pdf")
#dev.off()
xx.df <- prediction(predictedValue, trueValue)
perf <- performance(xx.df, "prec", "rec")
xy <- data.frame(recall=perf@x.values[[1]], precision=perf@y.values[[1]])
Expand All @@ -62,4 +62,4 @@ calculateclassificationscore <- function(name = "Temporary", predictedValue, tru
acc = max(acc)
classscore = data.frame(acc, roc, aucpr)
return(classscore)
}
}
65 changes: 65 additions & 0 deletions CRISPRpred/crisprpred/R_src/metrics/calculateclassificationscore.R~
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@@ -0,0 +1,65 @@
#' Illustration of calculateclassificationscore
#'
#' This function takes predicted and observed value as input and produce accuracy, auROC and auPR values. In addition it also draws several plots.
#'
#' @param name a name of the classification method
#' @param predictedValue a list of predicted value
#' @param trueValue a list of true observed value
#' @return a dataframe containing accuracy auroc and aupr values
#' @export
#' @examples
#' setwd("..")
#' dir = getwd()
#' filepath = paste0(dir,'/data-raw/sample.csv')
#' data = read.csv(filepath)
#' prediction = data$predictions
#' data$predictions = NULL
#' observed = data$score_drug_gene_threshold
#' features = calculateclassificationscore("Simple Method", prediction, observed)
calculateclassificationscore <- function(name = "Temporary", predictedValue, trueValue){
pred <- prediction(predictedValue, trueValue)
roccurve <- performance(pred,"tpr","fpr")
par(mar = c(4, 5, 1.98, 1))
plot(roccurve, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
#dev.copy(pdf, "ROCcurve.pdf")
#dev.off()
pr <- performance(pred, "prec", "rec")
par(mar = c(4, 5, 1.98, 1))
plot(pr, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)

#dev.copy(pdf, "PRcurve.pdf")
#dev.off()
ss <- performance(pred, "sens", "spec")
par(mar = c(4, 5, 1.98, 1))
plot(ss, main = name, cex.main = 1.7, cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
dev.copy(pdf, "SensSpeci.pdf")
dev.off()
acc <- performance(pred, "acc")
par(mar = c(4, 5, 1.98, 1))
plot(acc,main = name, cex.main = 1.7, xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
#dev.copy(pdf, "Accuracy.pdf")
#dev.off()
mcc <- performance(pred, "mat")
par(mar = c(4, 5, 1.98, 1))
plot(mcc,main = name, cex.main = 1.7,xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
#dev.copy(pdf, "MCCcurve.pdf")
#dev.off()
f <- performance(pred, "f")
par(mar = c(4, 5, 1.98, 1))
plot(f, main = name, cex.main = 1.7, xlab = "Threshold", cex.lab = 1.7, box.lty = 7, box.lwd = 4,xaxis.cex.axis=1.3,yaxis.cex.axis=1.3, lwd = 4, yaxis.lwd = 4, xaxis.lwd = 4, yaxis.las = 1)
#dev.copy(pdf, "Fmeasure.pdf")
#dev.off()
xx.df <- prediction(predictedValue, trueValue)
perf <- performance(xx.df, "prec", "rec")
xy <- data.frame([email protected][[1]], [email protected][[1]])
xy <- subset(xy, !is.nan(xy$precision))
xy <- rbind(c(0, 0), xy)
aucpr <- trapz(xy$recall, xy$precision)
auc = performance(pred, "auc")
roc = [email protected][[1]]
acc = [email protected][[1]]
acc = acc[!is.infinite(acc)]
acc = max(acc)
classscore = data.frame(acc, roc, aucpr)
return(classscore)
}
1 change: 1 addition & 0 deletions CRISPRpred/crisprpred/R_src/mlmethods/svmregression.R
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
#' featurelist = c("Percent.Peptide", "Amino.Acid.Cut.position","predictions")
#' dir = getwd()
#' filepath = paste0(dir,'/data-raw/sample.csv')
#' cat(filepath)
#' data = read.csv(filepath)
#' svmregression(featurelist,data)
svmregression = function(featurelist,featuredata) {
Expand Down
19 changes: 19 additions & 0 deletions CRISPRpred/crisprpred/R_src/mlmethods/svmregression.R~
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@@ -0,0 +1,19 @@
#' SMV Regression
#'
#' This function takes feature list and dataset. Now, it outputs prediction score.
#' @param featurelist a list of features
#' @param featuredata provided dataset
#' @return prediction score
#' @export
#' @examples
#' featurelist = c("Percent.Peptide", "Amino.Acid.Cut.position","predictions")
#' dir = getwd()
#' filepath = paste0(dir,'/data-raw/sample.csv')
#' data = read.csv(filepath)
#' svmregression(featurelist,data)
svmregression = function(featurelist,featuredata) {
fformula = featureformula(featurelist)
modelS = svm(as.formula(fformula), featuredata, cross = 10)
predictionsS = predict(modelS, featuredata)
return(predictionsS)
}
102 changes: 102 additions & 0 deletions CRISPRpred/crisprpred/data-raw/sample.csv
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Original file line number Diff line number Diff line change
@@ -0,0 +1,102 @@
,30mer,Target gene,Percent Peptide,Amino Acid Cut position,score_drug_gene_rank,score_drug_gene_threshold,drug,predictions
0,CAGAAAAAAAAACACTGCAACAAGAGGGTA,CD5,72.87,360,0.083682008,0,nodrug,0.5444118584
1,TTTTAAAAAACCTACCGTAAACTCGGGTCA,NF1,65.8,1868,0.868206522,1,PLX_2uM,0.6175122138
2,TCAGAAAAAGCAGCGTCAGTGGATTGGCCC,CD5,84.21,416,0.184100418,0,nodrug,0.4762318347
3,AATAAAAAATAGGATTCCCAGCTTTGGAAG,NF1,56.39,1601,0.432065217,0,PLX_2uM,0.4598820009
4,GATGAAAAATATGTAAACAGCATTTGGGAC,CUL3,4.3,33,0.149350649,0,PLX_2uM,0.2908406197
5,TTCCAAAAATTCTAACGTGAGGTGTGGCTC,NF1,67.7,1922,0.239130435,0,PLX_2uM,0.5704079655
6,CCAGAAAACAACGGCCCAGGAGGGCGGCCA,CD5,52.23,258,0.188284519,0,nodrug,0.592484894
7,TTAGAAAACATGTTCCAGAGCAGTTGGTAG,NF1,44.24,1256,0.533967391,0,PLX_2uM,0.6194023163
8,GCCAAAAACCAGGGGCCGGTGCTGAGGGCA,MED12,16.31,355,0.579004329,0,AZD_200nM,0.4947430432
9,GCCAAAAACCAGGGGCCGGTGCTGAGGGCA,MED12,16.31,355,0.612554113,0,PLX_2uM,0.4947430432
10,AACAAAAACCTAGCCGGCTCCAAACGGGGA,TADA2B,78.1,328,0.084210526,0,AZD_200nM,0.3752269313
11,CGTCAAAACTGGAAGGAATGTTTAGGGATA,CUL3,61.72,474,0.188311688,0,PLX_2uM,0.4143436943
12,TTCAAAAAGATCGTACATGTTCACCGGAGG,TADA1,82.39,276,0.71559633,0,AZD_200nM,0.5607602117
13,ATACAAAAGGAAGATTGCTGATGAGGGCAG,CD45,41.95,474,0.581227437,0,nodrug,0.6174712871
14,GGTGAAAAGGACCCCACGAAGTGTTGGATA,HPRT1,78.44,171,0.34375,0,6TG_2ug/mL,0.5004091901
15,TCCTAAAAGGCAAGAAATGGAATCAGGGAT,NF1,90.17,2560,0.282608696,0,PLX_2uM,0.2402105197
16,CAATAAAAGTATCAGTGTGTATAGAGGTGA,THY1,76.54,124,0.677419355,0,nodrug,0.5123059645
17,ATGAAAAATATGTAAACAGCATTTGGGACC,CUL3,4.3,33,0.246753247,0,PLX_2uM,0.4098619261
18,TGATAAAATCTACAGTCATAGGAATGGATC,HPRT1,43.12,94,0.5625,0,6TG_2ug/mL,0.5861378369
19,GTGCAAAATTAAAACGACTCCTGAAGGGTA,NF1,6.83,194,0.100543478,0,PLX_2uM,0.5083450384
20,CACCAAACACAAGTGGGAGCAGGCTGGTGA,H2-K,46.07,170,0.692307692,0,nodrug,0.6424022417
21,TTTAAAACAGACTTTCTCTCTAAGTGGTTT,NF1,58.51,1661,0.975543478,1,PLX_2uM,0.591609864
22,AAGAAAACAGGGGCCCGAAACCCAAGGCAG,NF1,18.25,518,0.442934783,0,PLX_2uM,0.6313676905
23,AACGAAACAGTGACGTTCCGTCTCTGGAAT,CD28,45.87,100,0.013513514,0,nodrug,0.443900453
24,TGGAAAACAGTGTGCAGTTCCAGTTGGAGG,CD5,12.55,62,0.715481172,0,nodrug,0.6305218551
25,TGAGAAACATAAGGAGTATAGCCCTGGAGG,MED12,89.25,1943,0.198051948,0,AZD_200nM,0.3551152317
26,TGAGAAACATAAGGAGTATAGCCCTGGAGG,MED12,89.25,1943,0.1504329,0,PLX_2uM,0.3551152317
27,TCCGAAACATCTCGTACAGTGACAAGGAGG,NF2,45.04,268,0.632286996,0,PLX_2uM,0.6712923254
28,AAAAAAACATGATATCTAAGTTAAAGGTAA,CUL3,59.64,458,0.071428571,0,PLX_2uM,0.3396189216
29,CCAAAAACCAGGGGCCGGTGCTGAGGGCAC,MED12,16.26,354,0.477272727,0,AZD_200nM,0.554644706
30,CCAAAAACCAGGGGCCGGTGCTGAGGGCAC,MED12,16.26,354,0.533549784,0,PLX_2uM,0.554644706
31,ACAAAAACCTAGCCGGCTCCAAACGGGGAA,TADA2B,78.1,328,0.126315789,0,AZD_200nM,0.3835387749
32,CCAGAAACGCCTAAGCCTAGTTGTGGGGAT,CD45,19.56,221,0.657039711,0,nodrug,0.5713270027
33,ACAGAAACTGATGGGCTGGCATTCTGGGCT,CD43,11.14,44,0.007246377,0,nodrug,0.1918936941
34,TGCTAAACTGCCCACCTCAGTCCAGGGACA,MED12,66.1,1439,0.836580087,1,AZD_200nM,0.7059911675
35,TGCTAAACTGCCCACCTCAGTCCAGGGACA,MED12,66.1,1439,0.851731602,1,PLX_2uM,0.7059911675
36,CTTCAAAGAAGGCCACTCGGGACTTGGCGC,NF2,98.66,587,0.139013453,0,PLX_2uM,0.2260444309
37,TGACAAAGACTTCTGTGTCCAGAAGGGCAA,CD45,1.5,17,0.996389892,1,nodrug,0.6728217154
38,CCCCAAAGAGGAGGAGAAGGTGCAAGGCCA,CD43,1.01,4,0.644927536,0,nodrug,0.5940822656
39,CAGAAAAGAGTGATGGCACTGCTGAGGCGC,NF1,26.88,763,0.690217391,0,PLX_2uM,0.6298749852
40,ATGAAAAGATCTACTGCCCTCCTGAGGCTT,NF2,22.69,135,0.937219731,1,PLX_2uM,0.7471390019
41,AAGGAAAGCAGACTTATGGAGACATGGAAG,CD45,80.62,911,0.725631769,0,nodrug,0.5854654185
42,GAGGAAAGCAGCCAGGACAGCAGTGGGCAG,CD33,80.49,293,0.264069264,0,nodrug,0.540375454
43,TGGTAAAGCAGTCGCCCCTGCTTGTGGTAG,CD28,13.76,30,0.405405405,0,nodrug,0.5893242228
44,CTCAAAAGCATGTCTTGGTGCTGGTGGGAT,CUL3,68.62,527,0.279220779,0,PLX_2uM,0.4613847192
45,ATGCAAAGCCATATGAAATTGTAGTGGACC,NF1,57.84,1642,0.964673913,1,PLX_2uM,0.7199002459
46,ACGAAAAGCTCTTGCTGGCCATGGAGGAAG,NF1,10.92,310,0.369565217,0,PLX_2uM,0.6781406533
47,AGGTAAAGCTCTTGTTTCTGAAGAAGGAGA,CUL3,43.1,331,0.62987013,0,PLX_2uM,0.4996671892
48,AAGGAAAGCTTATCGGTTACGAGATGGTCA,TADA1,59.7,200,0.568807339,0,AZD_200nM,0.6154171379
49,CCTCAAAGCTTCGTGTTAATAAGCTGGTAA,NF2,49.41,294,0.439461883,0,PLX_2uM,0.5705774338
50,ATCAAAAGGAGATACTTACACAACAGGAAA,NF1,93.59,2657,0.669836957,0,PLX_2uM,0.2317550952
51,ATTGAAAGGATCATCACGAAACTCTGGCAT,TADA1,91.94,308,0.23853211,0,AZD_200nM,0.4406354964
52,CCTAAAAGGCAAGAAATGGAATCAGGGATC,NF1,90.21,2561,0.266304348,0,PLX_2uM,0.3890429003
53,AGCAAAAGGCCACAGTTATTGTCATGGTCA,CD45,50.88,575,0.653429603,0,nodrug,0.674194574
54,CTGAAAAGGCCCAGATCACCGAGGAGGAGG,NF2,65.88,392,0.600896861,0,PLX_2uM,0.7274310081
55,CCGGAAAGGCCCGGATCCGCATCTTGGTGT,CUL3,2.08,16,0.506493506,0,PLX_2uM,0.484396172
56,TGTTAAAGGCTGCTGGGGAAGAATTGGAGA,MED12,66.61,1450,0.653679654,0,AZD_200nM,0.433673934
57,TGTTAAAGGCTGCTGGGGAAGAATTGGAGA,MED12,66.61,1450,0.762987013,0,PLX_2uM,0.433673934
58,CCGCAAAGGGACAGCAGAAACTGGTGGGTT,MED12,36.29,790,0.699134199,0,AZD_200nM,0.6403670819
59,CCGCAAAGGGACAGCAGAAACTGGTGGGTT,MED12,36.29,790,0.682900433,0,PLX_2uM,0.6403670819
60,AAAAAAAGTAATTCACTTACAGTCTGGCTT,HPRT1,81.65,178,0.375,0,6TG_2ug/mL,0.4295260995
61,CACAAAAGTAGTCGCCCTCATCCTTGGTGG,THY1,61.11,99,0.612903226,0,nodrug,0.5468501976
62,CTGCAAAGTCTCTGGCAGGGGCGTAGGGCT,CD28,95.41,208,0.5,0,nodrug,0.3304016684
63,GATGAAAGTGCGCAAACAGGTGGCAGGAAA,NF1,39.77,1129,0.22826087,0,PLX_2uM,0.5710475192
64,TCAGAAATAATACCAACAACTGGAGGGAGA,CD13,76.53,740,0.752747253,0,nodrug,0.5760529154
65,GTGGAAATACCAGTCAAATGTCCATGGATC,NF1,22.68,644,0.39673913,0,PLX_2uM,0.6243503475
66,ACCCAAATCAAAGGAAATAGAAAATGGTCA,CUL3,85.16,654,0.75974026,0,PLX_2uM,0.3776143351
67,GAGTAAATCCACTTACCTATAGGAAGGGTC,NF1,87.53,2485,0.546195652,0,PLX_2uM,0.5140931709
68,CAGGAAATCCATGAGCCTGGACATGGGGCA,NF1,88.94,2525,0.710597826,0,PLX_2uM,0.4453548166
69,CCAAAAATCCCCGCTTGTGAACACTGGGGT,NF2,26.39,157,0.502242152,0,PLX_2uM,0.5168211871
70,TTCCAAATCCTCAGCATAATGATTAGGTAT,HPRT1,12.39,27,0.3125,0,6TG_2ug/mL,0.5035094466
71,TAATAAATCTGTATCAGATGACTCCGGAAA,NF2,30.25,180,0.34529148,0,PLX_2uM,0.6508489781
72,TTCTAAATGACATTTATTATGCTTCGGAAA,NF1,62.87,1785,0.225543478,0,PLX_2uM,0.4310209889
73,CCAGAAATGATGATTGCTGCTCAGGGGCCA,CD45,76.28,862,0.931407942,1,nodrug,0.629647012
74,CGATAAATGCCAGGAAGCTACTGCAGGTAT,MED12,20.58,448,0.801948052,1,AZD_200nM,0.620321017
75,CGATAAATGCCAGGAAGCTACTGCAGGTAT,MED12,20.58,448,0.890692641,1,PLX_2uM,0.620321017
76,TCCAAAATGCTAAGTGTGGAAATGAGGATT,CD45,6.55,74,0.386281588,0,nodrug,0.5747327853
77,TGCAAAATTAAAACGACTCCTGAAGGGTAA,NF1,6.83,194,0.900815217,1,PLX_2uM,0.6817258651
78,TCATAAATTCCTCAAACTTTGAACTGGTAA,NF1,55.2,1567,0.75951087,0,PLX_2uM,0.4457953822
79,ATCCAAATTTCTGGCTGCAAGTGCAGGAGT,CD33,6.87,25,0.201298701,0,nodrug,0.5120353802
80,GATAAAATTTGGCCAAGAAGTGAAAGGTGA,NF2,15.97,95,0.677130045,0,PLX_2uM,0.6506766709
81,TTCCAACAAAATGTCAGAATGGATTGGCTC,CD45,40.71,460,0.288808664,0,nodrug,0.4860502206
82,GATGAACAACAGGAACTCGTTGAAAGGGGT,CD45,39.47,446,0.458483755,0,nodrug,0.4816440129
83,AGACAACAAGAGCTCTTGGTTGCAGGGATG,NF1,79.08,2245,0.61548913,0,PLX_2uM,0.4554503216
84,TAGAAACAATATTGGATAAAGCAATGGTCC,CUL3,54.04,415,0.285714286,0,PLX_2uM,0.5916450041
85,AAGCAACAATGGCCAACGAAGCACTGGTGA,NF2,62.69,373,0.874439462,1,PLX_2uM,0.5487629834
86,TCAAAACACTCAAGCACCCAGGTCAGGAAC,MED12,11.76,256,0.861471861,1,AZD_200nM,0.5617918933
87,TCAAAACACTCAAGCACCCAGGTCAGGAAC,MED12,11.76,256,0.811688312,1,PLX_2uM,0.5617918933
88,TGGTAACACTGAGACAGCTTCTCCTGGGCA,CD5,69.64,344,0.468619247,0,nodrug,0.4556350807
89,GAGGAACAGAAGAACTTCCAGAGGAGGAGG,MED12,57.46,1251,0.795454545,0,AZD_200nM,0.6694770671
90,GAGGAACAGAAGAACTTCCAGAGGAGGAGG,MED12,57.46,1251,0.856060606,1,PLX_2uM,0.6694770671
91,CAGCAACAGACAGCAGCTTTGGTCCGGCAA,MED12,98.99,2155,0.00974026,0,AZD_200nM,0.1708750175
92,CAGCAACAGACAGCAGCTTTGGTCCGGCAA,MED12,98.99,2155,0.03030303,0,PLX_2uM,0.1708750175
93,CCAGAACAGCAACGGTCGCCATGTTGGAGA,H2-K,82.66,305,0.426035503,0,nodrug,0.5072004452
94,CGCCAACAGCAGGAGCAGCGTGCACGGTAC,H2-K,1.36,5,0.769230769,0,nodrug,0.6002272873
95,GTCAAACAGCTCACTGATCTGGGCCGGCGT,CD13,48.29,467,0.685714286,0,nodrug,0.5450770953
96,AGACAACAGCTGAAACAGCCTCCTCGGTGA,TADA1,50.75,170,0.834862385,1,AZD_200nM,0.5568530269
97,TGGAAACAGTCACAGAAGCTTTGTTGGAGA,NF1,77.81,2209,0.523097826,0,PLX_2uM,0.3545229735
98,CTGTAACAGTGGACGAACTCGCCACGGATC,NF1,99.12,2814,0.044836957,0,PLX_2uM,0.241392569
99,CATGAACATGACTCCCCGGAGGCCTGGGCT,CD28,89.91,196,0.054054054,0,nodrug,0.2430828261
100,GGTCAACATGTCTCTTCTTAACCTTGGTCT,MED12,8.04,175,0.594155844,0,AZD_200nM,0.5011373614
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