Skip to main content

Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Fig. 1 | BMC Bioinformatics

Fig. 1

From: Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Fig. 1

Preprocessing workflow for large, heterogeneous RNA-Seq data sets, as applied to the GTEx data. The boxes on the right show the number of samples, genes, and tissue types at each step. First, samples were filtered using PCoA with Y-chromosome genes to test for correct annotation of the sex of each sample. PCoA was used to group or separate samples derived from related tissue regions. Genes were filtered to select a normalization gene set to preserve robust, tissue-dependent expression. Finally, the data were normalized using a global count distribution method to support cross-tissue comparison while minimizing within-group variability

Back to article page