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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Oct 16, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.

Snehalika Lall¹, Sumanta Ray², Sanghamitra Bandyopadhyay¹

¹Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.

Plos Computational Biology

|October 19, 2021

Summary

This summary is machine-generated.

RgCop, a new gene selection method for single cell RNA sequencing (scRNA-seq) data, overcomes technical noise. It improves cell clustering, classification, and accurate cell annotation using robust dependency measures.

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Published on: May 28, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene selection is critical for analyzing large single cell RNA sequencing (scRNA-seq) datasets.
Existing methods using highly variable or highly expressed genes lack stability due to technical noise.

Purpose of the Study:

To develop a novel, robust method for gene selection in unannotated scRNA-seq data.
To improve the stability and predictive power of feature sets derived from scRNA-seq data.

Main Methods:

Proposed RgCop, a regularized copula-based method for gene selection.
Utilized copula correlation (Ccor) to capture multivariate gene dependencies.
Incorporated l1 regularization to penalize redundant gene coefficients, yielding non-redundant features.

Main Results:

RgCop demonstrated significant improvements in clustering and classification performance on real scRNA-seq data.
The method effectively captured dependencies in noisy data, enhancing stability due to copula's scale-invariant property.
Differentially expressed genes identified by RgCop accurately annotated cell clusters and unknown cells.

Conclusions:

RgCop offers a stable and effective approach for gene selection in large scRNA-seq datasets.
The method enhances downstream analysis tasks, including cell annotation, by identifying robust and non-redundant gene features.