A metric for evaluating biological information in gene sets and its application to identify co-expressed gene clusters in PBMC.

ABSTRACT: Recent technological advances have made the gathering of comprehensive gene expression datasets a commodity. This has shifted the limiting step of transcriptomic studies from the accumulation of data to their analyses and interpretation. The main problem in analyzing transcriptomics data is that the number of independent samples is typically much lower (<100) than the number of genes whose expression is quantified (typically >14,000). To address this, it would be desirable to reduce the gathered data's dimensionality without losing information. Clustering genes into discrete modules is one of the most commonly used tools to accomplish this task. While there are multiple clustering approaches, there is a lack of informative metrics available to evaluate the resultant clusters' biological qu

SUBMITTER: Bennett J 

PROVIDER: S-EPMC8523066 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature