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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
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A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

Shiquan Sun1,2,3,4, Yabo Chen1, Yang Liu1

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, People's Republic of China.

BMC Systems Biology
|April 7, 2019
PubMed
Summary
This summary is machine-generated.

We developed single-cell negative binomial matrix factorization (scNBMF), a fast and efficient method for analyzing single-cell RNA sequencing data. scNBMF accurately identifies cell types and is significantly faster than existing tools for large datasets.

Keywords:
Deep learningMatrix factorizationRead countSingle-cell RNA sequencing

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNAseq) data analysis faces challenges with unwanted variables masking true cell-type signals.
  • Existing matrix factorization tools for scRNAseq data often fail to model raw counts or are too slow for large datasets (n>500).

Purpose of the Study:

  • To develop a fast, efficient, and count-based matrix factorization method for inferring cell-type structure from scRNAseq data.
  • To address the limitations of existing methods in handling large-scale scRNAseq datasets.

Main Methods:

  • Developed single-cell negative binomial matrix factorization (scNBMF), a count-based matrix factorization method.
  • Utilized the TensorFlow framework for efficient computation.
  • Validated the method on three public scRNAseq datasets: brain, embryonic stem, and pancreatic islet.

Main Results:

  • scNBMF effectively infers low-dimensional structure for cell-type identification.
  • Experimental results demonstrate scNBMF's superior power in detecting cell types compared to existing bespoke tools.
  • scNBMF achieves 10-100 fold speed improvement on large scRNAseq datasets.

Conclusions:

  • scNBMF is a powerful and efficient tool for large-scale scRNAseq data analysis, particularly for cell-type detection.
  • The method is implemented in R and Python, with source code publicly available.
  • scNBMF offers a scalable solution for analyzing complex single-cell gene expression data.