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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

Universal preprocessing of single-cell genomics data.

A Sina Booeshaghi¹, Delaney K Sullivan^1,2, Lior Pachter^1,3

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.

Biorxiv : the Preprint Server for Biology

|September 25, 2023

Summary

This summary is machine-generated.

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This study introduces a universal workflow for single-cell genomics data preprocessing. It uses seqspec, kb-python, kallisto, and bustools for efficient read cataloging and error correction.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell genomics generates large, complex datasets.
Standardized preprocessing is crucial for accurate analysis.
Existing workflows may lack versatility across data types.

Conclusions:

The developed workflow offers a standardized and efficient approach to single-cell genomics data preprocessing.
This method facilitates more reliable downstream analyses across different single-cell assays.
The cellatlas package democratizes access to advanced preprocessing tools.

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