Random forest based similarity learning for single cell RNA sequencing data.

ABSTRACT:

Motivation

Genome-wide transcriptome sequencing applied to single cells (scRNA-seq) is rapidly becoming an assay of choice across many fields of biological and biomedical research. Scientific objectives often revolve around discovery or characterization of types or sub-types of cells, and therefore, obtaining accurate cell-cell similarities from scRNA-seq data is a critical step in many studies. While rapid advances are being made in the development of tools for scRNA-seq data analysis, few approaches exist that explicitly address this task. Furthermore, abundance and type of noise present in scRNA-seq datasets suggest that application of generic methods, or of methods developed for bulk RNA-seq data, is likely suboptimal.

Results

Here, we present RAFSIL, a random forest bas

SUBMITTER: Pouyan MB 

PROVIDER: S-EPMC6022547 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature