Commit 114fade9 authored by Holger Brandl's avatar Holger Brandl

reworked toc

parent 31e77b26
#!/usr/bin/env Rscript
#' - [Differential Expression Analysis](#differential-expression-analysis)
#' - [Data Preparation](#data-preparation)
#' - [Quality Control](#quality-control)
#' - [Size Factors](#size-factors)
#' - [Data Dispersion](#data-dispersion)
#' - [PCA and Clustering](#pca-and-clustering)
#' - [Perform Differential Expression Analysis](#perform-differential-expression-analysis)
#' - [Significnce of differential binding](#significnce-of-differential-binding)
#' - [MA and Volcano plots](#ma-and-volcano-plots)
#' - [Count Outlier Analysis](#count-outlier-analysis)
#' - [Hits Summary](#hits-summary)
#' - [DEG Counts](#deg-counts)
#' - [MA Plots](#ma-plots)
#' - [Exported Data](#exported-data)
#' # Differential Expression Analysis
#'
#' Contents
#'
#' - [Data Preparation](#data-preparation)
#' - [Quality Control](#quality-control)
#' - [Size Factors](#size-factors)
#' - [Global Dispersion Model](#global-dispersion-model)
#' - [PCA and Clustering](#pca-and-clustering)
#' - [Differential Expression Test](#differential-expression-test)
#' - [MA and Volcano plots](#ma-and-volcano-plots)
#' - [Count Outlier Analysis](#count-outlier-analysis)
#' - [Hits Summary](#hits-summary)
#' - [Exported Data](#exported-data)
# to rebuild toc do knitr::spin("featcounts_deseq_mf.R", FALSE)
#'
#+ include=FALSE
# to rebuild toc do knitr::spin("featcounts_deseq_mf.R", FALSE)
#' # Differential Expression Analysis
#' Created by: `r system("whoami", intern=T)`
#'
#' Created at: `r format(Sys.Date(), format="%B %d %Y")`
#+ include=FALSE
suppressMessages(require(docopt))
doc = '
Perform a differntial gene expression analysis using deseq2
Perform a differential gene expression analysis using deseq2
Usage: featcounts_deseq_mf.R [options] <count_matrix> <design_matrix>
Options:
......@@ -456,7 +457,7 @@ pheatmap(mat, annotation_col = column2rownames(exDesign, "replicate") %>% select
########################################################################################################################
#' ## Differential Expresssion Test
#' ## Differential Expression Test
# Run Deseq Test
......@@ -550,7 +551,7 @@ normCounts %>% write_tsv(paste0(resultsBase, "sizefac_normalized_counts_by_repli
#filter(countData, ensembl_gene_id=="ENSDARG00000000001")
########################################################################################################################
#' # MA and Volcano plots
#' ### MA and Volcano plots
#' MA-plot: The log2 fold change for a particular comparison is plotted on the y-axis and the average of the counts normalized by log2 is shown on the x-axis ("M" for minus, because a log ratio is equal to log minus log, and "A" for average). Each gene is represented with a dot. Genes with an adjusted p value below a certain threshold are shown in cyan (True).
#' This plot demonstrates that only genes with a large average normalized count contain sufficient information to yield a significant call.
......@@ -794,7 +795,7 @@ deAnnot %>%
########################################################################################################################
#' ## Count Outlier Analysis
#' ### Count Outlier Analysis
#' For how many genes did Deseq did not assign a p-value (due to outliers; see section 1.5.3 os [DEseq manual](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#count-outlier-detection))
......
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