The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

Abstract excerpt

In genomics, we often impose the assumption that gene expression data follows a specific distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% o...

Topics

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• Algorithms
• Analysis of Variance
• False Positive Reactions
• Models, Statistical
• Oligonucleotide Array Sequence Analysis
• Reproducibility of Results
• Sample Size
• Sensitivity and Specificity

Identifiers and source

Literature Corpus work

b7b35403-6c65-574c-9aec-feb3e7bf63e3

DOI

10.1101/572693