Package: RNAseqQC 0.2.0

Frederik Ziebell

RNAseqQC:Quality Control for RNA-Seq Data

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

Authors:Frederik Ziebell [cre], Frederik Ziebell [aut], GlaxoSmithKline Research & Development Limited [cph]

# InstallRNAseqQC in R:

install.packages('RNAseqQC',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

T47D - The T47D cell line data of RNA-seq experiment GSE89888
T47D_diff_testing - Differential expression results corresponding to the T47D data set.

Last updated 4 days agofrom:2594954b47

Frederik Ziebell

Last update: 2024-07-02
Started: 2022-03-17

Frederik Ziebell

Last update: 2024-07-02
Started: 2022-03-17

Help page	Topics
for a character vector x, check if all non-NA elements of x can be converted to numeric	all_numeric
Filter genes with low counts	filter_genes
Get all gene IDs in a DESeqDataSet for a given gene name.	get_gene_id
Make DESeqDataSet from counts matrix and metadata	make_dds
Create a mean-sd plot Make a scatterplot that shows for each gene its standard deviation versus mean.	mean_sd_plot
Plot number of counts per sample and biotype	plot_biotypes
Plot gene expression along a chromosome	plot_chromosome
Plot a gene	plot_gene
Plot number of detected genes for each sample	plot_gene_detection
Plot the library complexity	plot_library_complexity
Plot loadings of a principal component	plot_loadings
MA-plot of a differential testing result	plot_ma
Plot results of a principal component analysis	plot_pca
Plot matrix of PCA scatter plots	plot_pca_scatters
Plot clustering of samples in a distance heatmap	plot_sample_clustering
MA plots of samples	plot_sample_MAs
Plot total counts per sample	plot_total_counts
Plot correlations of samples within a level of a group	plot_within_level_sample_MAs
Save list of plots to PDF	save_plots_to_pdf
The T47D cell line data of RNA-seq experiment GSE89888	T47D
Differential expression results corresponding to the T47D data set.	T47D_diff_testing