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RNAseqQC

The goal of RNAseqQC is to aid quality control of RNAseq data by providing a collection of data visualization functions. It allows identification of samples with unwanted biological or technical effects and to explore differential testing results.

Installation

You can install the released version of RNAseqQC from CRAN with:

install.packages("RNAseqQC")

Example

This is a basic example in which we make a library complexity plot and then compare some samples to the median reference of their respective group:

library(RNAseqQC)
library("DESeq2")

dds <- makeExampleDESeqDataSet(n=10000, m=30)
plot_library_complexity(dds)
vsd <- vst(dds)
dds$condition
plot_sample_MAs(vsd, group = "condition")[c(1,2,16,17)]

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Install

install.packages('RNAseqQC')

Monthly Downloads

266

Version

0.2.1

License

Apache License (>= 2)

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Repository

https://github.com/frederikziebell/RNAseqQC

Maintainer

Frederik Ziebell

Last Published

July 15th, 2024

Functions in RNAseqQC (0.2.1)

plot_sample_MAs

MA plots of samples

plot_within_level_sample_MAs

Plot correlations of samples within a level of a group

save_plots_to_pdf

Save list of plots to PDF

Make DESeqDataSet from counts matrix and metadata

Filter genes with low counts

Plot number of counts per sample and biotype

RNAseqQC-package

RNAseqQC: Quality Control for RNA-Seq Data

plot_chromosome

Plot gene expression along a chromosome

for a vector x, check if all non-NA elements of x can be converted to numeric

Plot results of a principal component analysis

plot_pca_scatters

Plot matrix of PCA scatter plots

plot_sample_clustering

Plot clustering of samples in a distance heatmap

MA-plot of a differential testing result

plot_total_counts

Plot total counts per sample

Get all gene IDs in a DESeqDataSet for a given gene name.

plot_library_complexity

Plot the library complexity

Create a mean-sd plot Make a scatterplot that shows for each gene its standard deviation versus mean.

Plot loadings of a principal component

plot_gene_detection

Plot number of detected genes for each sample

The T47D cell line data of RNA-seq experiment GSE89888

T47D_diff_testing

Differential expression results corresponding to the T47D data set.