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Related Experiment Video

Updated: Jul 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models.

Phillip B Nicol¹, Jeffrey W Miller¹

¹Department of Biostatistics, Harvard University.

Biorxiv : the Preprint Server for Biology

|May 10, 2023

Summary

This summary is machine-generated.

scGBM is a new method for single-cell RNA sequencing (scRNA-seq) data analysis. It improves dimensionality reduction by directly modeling counts, capturing biological variation, and quantifying uncertainty for better cell clustering.

Keywords:

Dimension reduction Single-cell RNA sequencing Uncertainty quantification

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Last Updated: Jul 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Published on: July 18, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Dimensionality reduction is essential for single-cell RNA sequencing (scRNA-seq) data analysis.
Standard methods like PCA can introduce artifacts and obscure true biological signals.
Existing count-based models are often computationally intensive and lack uncertainty quantification.

Conclusions:

scGBM offers a computationally efficient and statistically robust approach to scRNA-seq dimensionality reduction.
The method enhances the discovery of biological insights by accurately modeling count data and quantifying uncertainty.
scGBM represents a significant advancement for analyzing large-scale single-cell genomics datasets.