The 'countToFPKM' package provides a robust function to convert the feature counts of paired-end RNA-Seq into FPKM normalised values by library size and feature effective length. Implements the algorithm described in Trapnell,C. et al. (2010). This package includes two functions:
fpkm() function converts the feature counts into FPKM values, it requires three arguments to return FPKM as numeric matrix normalized by library size and feature length:
countsa numeric matrix of raw feature counts.
featureLengtha numeric vector with feature lengths that can be obtained using
meanFragmentLengtha numeric vector with mean fragment lengths, which can be calculated using the
fpkmheatmap() function provides users with a robust method to generate a FPKM heatmap plot of the highly variable features in RNA-Seq dataset. It takes an FPKM numeric matrix which can be obtained using
fpkm() function as input. By default using Pearson correlation - 1 to measure the distance between features, and Spearman correlation -1 for clustering of samples. By default log10 transformation of (FPKM+1) is applied to make variation similar across orders of magnitude. It uses the var() function to identify the highly variable features. It then uses Heatmap() function from the 'ComplexHeatmap' package to generate a heatmap plot.
## Install dependances source("http://bioconductor.org/biocLite.R") biocLite("ComplexHeatmap") ## Install countToFPKM from CRAN install.packages("countToFPKM") ## Alternatively, install countToFPKM from GitHub if(!require(devtools)) install.packages("devtools") devtools::install_github("AAlhendi1707/countToFPKM")
library(countToFPKM) file.readcounts <- system.file("extdata", "RNA-seq.read.counts.csv", package="countToFPKM") file.annotations <- system.file("extdata", "Biomart.annotations.hg38.txt", package="countToFPKM") file.sample.metrics <- system.file("extdata", "RNA-seq.samples.metrics.txt", package="countToFPKM") # Import the read count matrix data into R. counts <- as.matrix(read.csv(file.readcounts)) # Import feature annotations. # Assign feature lenght into a numeric vector. gene.annotations <- read.table(file.annotations, sep="\t", header=TRUE) featureLength <- gene.annotations$length # Import sample metrics. # Assign mean fragment length into a numeric vector. samples.metrics <- read.table(file.sample.metrics, sep="\t", header=TRUE) meanFragmentLength <- samples.metrics$meanFragmentLength # Return FPKM into a numeric matrix. fpkm_matrix <- fpkm (counts, featureLength, meanFragmentLength) # Plot log10(FPKM+1) heatmap of top 30 highly variable features fpkmheatmap(fpkm_matrix, topvar=30, showfeaturenames=TRUE, return_log = TRUE)
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