Quality Control for RNA-Seq Data

Functions for semi-automated quality control of bulk RNA-seq data.


RNAseqQC

R-CMD-check

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)]

Reference manual

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install.packages("RNAseqQC")

0.2.1 by Frederik Ziebell, 2 years ago


https://github.com/frederikziebell/RNAseqQC


Report a bug at https://github.com/frederikziebell/RNAseqQC/issues


Browse source code at https://github.com/cran/RNAseqQC


Authors: Frederik Ziebell [cre] , Frederik Ziebell [aut] , GlaxoSmithKline Research & Development Limited [cph] (GlaxoSmithKline Research & Development Limited; registered address: 980 Great West Road , Brentford , Middlesex TW8 9GS , United Kingdom)


Documentation:   PDF Manual  


Apache License (>= 2) license


Imports AnnotationHub, AnnotationFilter, BiocGenerics, ensembldb, circlize, DESeq2, SummarizedExperiment, ComplexHeatmap, matrixStats, grid, ggpointdensity, ggrepel, ggplot2, cowplot, patchwork, purrr, dplyr, stringr, tidyr, tibble, tidyselect, magrittr

Suggests rmarkdown, knitr, recount3, apeglm, ggrastr, gghighlight


See at CRAN