Catactor: a pipeline for consensus scATAC-seq analysis using meta-analytic marker genes

• Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Rohit Tripathy, Peter K. Koo, Jesse Gillis. Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility. bioRxiv, 2021.
• Catactor is a general pipeline for meta scATAC-seq analysis and works as a wrapper of Scanpy.

Requirement

• Scanpy
• Python (>= 3.6)
• Seurat v3 (Optional)
• BBKNN (Optional)
• LassoVariants (Optional, included)

Download

Catactor (mini version) is available via pip to compute meta-analytic marker gene signals and pseudo-bulk profiles based on either annotation or gene activity profiles.

pip install Catactor

To access all source codes used in our study, including a comprehensive assessment of cell-type prediction by machine learning and joint clustering methods, download all files as follows.

git clone https://github.com/carushi/Catactor

Tutorial

• example/mini_catactor_tutorial.ipynb for mini Catactor
• example/preprocessing.ipynb and tutorial.ipynb for processing all datasets used in this study

References

Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Rohit Tripathy, Peter K. Koo, Jesse Gillis. Exploiting marker genes for robust classification and characterization of single-cell chromatin accessibility. bioRxiv, 2021.

Dataset

• BRAIN Initiative Cell Census Network (BICCN), et al. A multimodal cell census and atlas of the mammalian primary motor cortex. bioRxiv, 2020.
• Preissl, S., et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nature neuroscience, 21(3):432-439 2018.
• Cusanovich DA., et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. Cell, 23;174(5):1309-1324.e18 2018.
• Lareau, CA., et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nature Biotechnology 37(8):916-924 2019.
• Chen, S., et al. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nature biotechnology, 37(12):1452-1457 2019.
• Spektor, R., et al. Single cell atac-seq identifies broad changes in neuronal abundance and chromatin accessibility in down syndrome. bioRxiv, 2019.
• Zhu, C., et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nature Structural and Molecular Biology, 2019.

Marker set

• SF and SC marker sets
  • MetaMarkers
  • Fischer S., et al. Meta-analytic markers reveal a generalizable description of cortical cell types. bioRxiv, 2021.
  • BRAIN Initiative Cell Census Network (BICCN), et al. A multimodal cell census and atlas of the mammalian primary motor cortex. bioRxiv, 2020.
• CU marker set
  • Cusanovich, DA., et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. Cell, 23;174(5):1309-1324.e18 2018.
• TA marker set
  • Tasic, B., et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nature neuroscience, 19(2):335-346 2016.
• TN marker set
  • Tasic, B., et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature, 563(7729):72-78 2018.
• Others
  • Spektor, R., et al. Single cell atac-seq identifies broad changes in neuronal abundance and chromatin accessibility in down syndrome. bioRxiv, 2019.
  • Chen, S., et al. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nature biotechnology, 37(12):1452-1457 2019.

Method

• Wolf, FA, et al. Scanpy: large-scale single-cell gene expression data analysis. Genome biology, 19(1):15 2018.
• Polanski, K., et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics, 36(3):964-965 2019.
• Butle, A., et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5):411-420 2018.
• Hara, S., Maehara, T. Finding alternate features in lasso. arXiv preprint, arXiv:1611.05940 2016.