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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: Dec 9, 2025

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.

Siamak Zamani Dadaneh¹, Paul de Figueiredo^2,3,4, Sing-Hoi Sze⁵

¹Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.

|September 9, 2020

Summary

We introduce a novel hierarchical gamma-negative binomial (hGNB) model for single-cell RNA sequencing (scRNA-seq) data analysis. This method effectively models complex biological data without zero-inflation bias, improving cell cluster and lineage discovery.

Keywords:

Bayesian Hierarchical modeling Single-cell RNA sequencing

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Last Updated: Dec 9, 2025

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution molecular profiling.
Analyzing scRNA-seq data reveals cellular heterogeneity crucial for understanding development and disease.
scRNA-seq data presents analytical challenges due to over-dispersion and zero counts (dropouts).

Purpose of the Study:

To propose a novel statistical model for scRNA-seq data analysis.
To address the limitations of zero-inflated models in scRNA-seq data.
To develop a method that naturally incorporates covariate effects and avoids pre-processing steps.

Main Methods:

Introduced a fully generative hierarchical gamma-negative binomial (hGNB) model.
Utilized novel data augmentation techniques for efficient Bayesian inference via conditional conjugacy.
Developed a model that accounts for gene and cell level covariate effects.

Main Results:

The hGNB model effectively models scRNA-seq data without explicit zero-inflation modeling.
The model naturally incorporates covariate effects at gene and cell levels.
No normalization pre-processing steps are required for hGNB model application.

Conclusions:

The hGNB model demonstrates strong performance in cell cluster discovery.
The hGNB model proves effective for cell lineage inference.
This approach offers a powerful tool for analyzing complex scRNA-seq datasets.