fpkm: Convert Counts to Fragments per Kilobase of Transcript per...

In countToFPKM: Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM)

Description

fpkm() function returns a numeric matrix normalized by library size and feature length.

Usage

fpkm (counts, featureLength, meanFragmentLength)

Arguments

counts	A numeric matrix of raw feature counts
featureLength	A numeric vector with feature lengths which can be obtained using 'biomaRt' package. The length of items should be as the same of rows in read count matrix.
meanFragmentLength	A numeric vector with mean fragment lengths, which can be calculated using 'CollectInsertSizeMetrics(Picard)' tool. The length of items should be as the same of columns in read count matrix.

Details

Implements the algorithm described in Trapnell,C. et al. (2010). "Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation". Nat. Biotechnol., 28, 511-515. doi: 10.1038/nbt.1621. This function takes a matrix of read feature counts of RNA-seq, a numeric vector with feature lengths which can be retrieved using the 'biomaRt' package, and a numeric vector with mean fragment length which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. Please see the original manuscript for further details.

Value

A data matrix normalized by library size and feature length.

References

Trapnell,C. et al. (2010) Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511-515. doi: 10.1038/nbt.1621.

Lior Pachter. Models for transcript quantification from RNA-Seq. arXiv:1104.3889v2.

Examples

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 length 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)

Example output

Loading required package: ComplexHeatmap
Loading required package: grid
========================================
ComplexHeatmap version 2.6.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================

Loading required package: circlize
========================================
circlize version 0.4.11
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

countToFPKM documentation built on May 1, 2019, 8:06 p.m.