Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM) Convert counts to Fragments Per Kilobase of transcript per Million mapped reads (FPKM)

Description

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

Usage

fpkm (counts, featureLength, meanFragmentLength)

Arguments

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)

Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM) A FPKM heatmap plot of the highly variable features in RNA-Seq dataset

Description

fpkmheatmap() function returns a heatmap plot of the highly variable features in RNA-Seq dataset.

Usage

fpkmheatmap(fpkm_matrix, topvar=30, showfeaturenames=TRUE, return_log = TRUE)

Arguments

Details

The fpkmheatmap() function provides the user with a quick and reliable way to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset. It takes an FPKM numeric matrix which can be obtained using the 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 then uses the var() function to identify the highly variable features to create the heatmap plot using the Heatmap() function from the 'ComplexHeatmap' package.

Value

A FPKM heatmap plot of the highly variable features in RNA-Seq dataset.

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.

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)

# Plot log10(FPKM+1) heatmap of top 30 highly variable features
fpkmheatmap(fpkm_matrix, topvar=30, showfeaturenames=TRUE, return_log = TRUE)