From c827df4b8e9da8a2271c7d543f1cee32908eee7d Mon Sep 17 00:00:00 2001 From: Shannan Ho Sui Date: Thu, 30 Jan 2025 16:01:42 -0500 Subject: [PATCH 1/2] Add methods draft and restructure similarity analysis --- .../rnaseq/01_quality_assesment/QC.Rmd | 35 ++++++++++++------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/inst/templates/rnaseq/01_quality_assesment/QC.Rmd b/inst/templates/rnaseq/01_quality_assesment/QC.Rmd index b64434a..6679b28 100644 --- a/inst/templates/rnaseq/01_quality_assesment/QC.Rmd +++ b/inst/templates/rnaseq/01_quality_assesment/QC.Rmd @@ -411,19 +411,6 @@ pca2 + scale_color_grafify(palette = "kelly") ``` -# Covariates analysis - -When there are multiple factors that can influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. This method adapts the method described by Daily et al. for which they integrated a method to correlate covariates with principal components values to determine the importance of each factor. - -```{r covariate-plot,fig.height=12, fig.width=10} -## Remove non-useful columns output by nf-core -coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness", "qualimap_5_3_bias"))]) - -# Remove missing data -coldat_2 <- na.omit(coldat_2) -degCovariates(vst, metadata = coldat_2) -``` - ## Hierarchical clustering Inter-correlation analysis (ICA) is another way to look at how well samples @@ -454,6 +441,28 @@ p <- pheatmap(vst_cor, p ``` +# Covariates analysis + +When there are multiple factors that can influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. This method adapts the method described by Daily et al. for which they integrated a method to correlate covariates with principal components values to determine the importance of each factor. + +```{r covariate-plot,fig.height=12, fig.width=10} +## Remove non-useful columns output by nf-core +coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness", "qualimap_5_3_bias"))]) + +# Remove missing data +coldat_2 <- na.omit(coldat_2) +degCovariates(vst, metadata = coldat_2) +``` + +# Conclusions + + + +# Methods + +RNA-seq counts were generated by the nf-core rnaseq pipeline [version] using salmon (Patro et al. 2017). Counts were imported into R using DESeq2 [see SessionInfo for version] (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015). + + # R session List and version of tools used for the QC report generation. From c98bd9a1a2e95a2d39802a61425e6d590410dd2d Mon Sep 17 00:00:00 2001 From: Elizabeth Partan <36081569+epartan@users.noreply.github.com> Date: Fri, 31 Jan 2025 10:10:45 -0500 Subject: [PATCH 2/2] Add package citations to QC.Rmd --- inst/templates/rnaseq/01_quality_assesment/QC.Rmd | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/inst/templates/rnaseq/01_quality_assesment/QC.Rmd b/inst/templates/rnaseq/01_quality_assesment/QC.Rmd index 6679b28..9c291a8 100644 --- a/inst/templates/rnaseq/01_quality_assesment/QC.Rmd +++ b/inst/templates/rnaseq/01_quality_assesment/QC.Rmd @@ -460,8 +460,15 @@ degCovariates(vst, metadata = coldat_2) # Methods -RNA-seq counts were generated by the nf-core rnaseq pipeline [version] using salmon (Patro et al. 2017). Counts were imported into R using DESeq2 [see SessionInfo for version] (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015). +RNA-seq counts were generated by the nf-core rnaseq pipeline [version] using Salmon (Patro et al. 2017). Downstream analyses were performed using `r version$version.string`. Counts were imported into R using DESeq2 version `r packageVersion("DESeq2")` (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2016). Heatmaps were generated by pheatmap (Kolde 2019). +## R package references + +```{r citations} +citation("DESeq2") +citation("ggplot2") +citation("pheatmap") +``` # R session