Description Usage Arguments Details Value References Examples
View source: R/countToFPKM-internal.R
fpkmheatmap() function returns a heatmap plot of the highly variable features in RNA-Seq dataset.
1 | fpkmheatmap(fpkm_matrix, topvar=30, showfeaturenames=TRUE, return_log = TRUE)
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fpkm_matrix |
A data matrix normalized by library size and feature length. |
topvar |
Number of highly variable features to show in heatmap plot. |
showfeaturenames |
whether to show the name of features in heatmap plot.
The default value is |
return_log |
whether to use log10 transformation of (fpkm+1).
The default value is |
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.
A FPKM heatmap plot of the highly variable features in RNA-Seq dataset.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | 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)
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