Universal preprocessing of single-cell genomics data

A Sina Booeshaghi; Delaney K Sullivan; Lior Pachter

doi:10.1101/2023.09.14.543267

Universal preprocessing of single-cell genomics data

bioRxiv [Preprint]. 2023 Sep 15:2023.09.14.543267. doi: 10.1101/2023.09.14.543267.

Authors

A Sina Booeshaghi¹, Delaney K Sullivan^{1

2}, Lior Pachter^{1

3}

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
² UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³ Department of Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.

Abstract

We describe a workflow for preprocessing a wide variety of single-cell genomics data types. The approach is based on parsing of machine-readable seqspec assay specifications to customize inputs for kb-python, which uses kallisto and bustools to catalog reads, error correct barcodes, and count reads. The universal preprocessing method is implemented in the Python package cellatlas that is available for download at: https://github.com/cellatlas/cellatlas/.

Publication types

Preprint

Abstract

Publication types

Grants and funding