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References: https://www.michaelgerth.net/news--blog/how-to-efficiently-bulk-download-ngs-data-from-sequence-read-databases Using SRA toolkit https://ncbi.github.io/sra-tools/install_config.html Install sratoolkit wget "http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/current/sratoolkit.current-centos_linux64.tar.gz" tar -xzf sratoolkit.current-centos_linux64.tar.gz Configure ./vdb-config -i Follow the instructions to change the download directory. The tools will download the data into /ncbi/public/sra/, wherever you have it set.

Set up environment library(EGAD) gene_set_enrichment <- function(genes, genes.labels, voc){ genes.names = rownames(genes.labels) labels.names = colnames(genes.labels) genes.counts = rowSums(genes.labels) labels.counts = colSums(genes.labels) # p m = match ( genes, genes.names ) filt.

Running DESeq2 For comprehensive tutorials, see here: Running edgeR Running basic DE calc_DE <- function(X, f.a, filt, group){ X = X[f.a,filt] group = group[filt] if( sum(group==1) < 2 ) { m.

RNA-sequencing is a popular means to detect the expression levels of genes. However, quality control remains challenging, requiring both extreme measures and rules which are set in stone from extensive previous analysis.

EGAD is an R package for the functional analysis of gene networks. It contains a series of tools to calculate functional properties in networks based on the guilt-by-association principle. The functions implemented here can be applied to gene networks constructed from a range of data types (e.

Install MetaNeighbor In R: if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MetaNeighbor") library(MetaNeighbor) Load data load("/data/XCGD/SCGC-GILL-JG-03_2/mono_seo.Rdata") seo1 = seo load("/data/XCGD/SCGC-GILL-JG-04_2/mono_seo.Rdata") seo2 = seo Merge data sets colData1 = seo1@colData colData2 = seo2@colData colData = rbind(colData1, colData2) data1 = seo1@assays[[1]] data2 = seo2@assays[[1]] m = match( rownames(data1), rownames(data2)) f.