Quantifying the phenotypic information in mRNA abundance.
Evan MaltzRoy WollmanPublished in: Molecular systems biology (2022)
Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single-cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca 2+ signaling network and the dynamic Ca 2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca 2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca 2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.
Keyphrases
- genome wide identification
- genome wide
- single cell
- genome wide analysis
- health information
- dna methylation
- poor prognosis
- transcription factor
- binding protein
- bioinformatics analysis
- copy number
- antibiotic resistance genes
- rna seq
- gene expression
- mesenchymal stem cells
- stem cells
- bone marrow
- microbial community
- cell therapy