bioRxivBioinformaticsPreprint
Universal preprocessing of single-cell genomics data
A. Sina BooeshaghiiD, Delaney K. SullivaniD, Lior S. PachteriD
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/.
- assay: scRNA-seq
- assay: scATAC-seq
- assay: multiome
- research focus: batch correction / integration
- research focus: dimensionality reduction
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 3
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