An information-theoretic approach to single cell sequencing analysis.
Michael J CaseyJörg FliegeRubén J Sánchez-GarcíaBen D MacArthurPublished in: BMC bioinformatics (2023)
Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.
Keyphrases
- single cell
- rna seq
- gene expression
- high throughput
- machine learning
- induced apoptosis
- poor prognosis
- dna methylation
- cell cycle arrest
- primary care
- healthcare
- genome wide
- deep learning
- big data
- electronic health record
- copy number
- quality improvement
- artificial intelligence
- oxidative stress
- genome wide identification
- long non coding rna
- pi k akt