SnapATAC (Single Nucleus Analysis Pipeline for ATAC-seq) is a fast and accurate method for analyzing single cell ATAC-seq datasets. SnapATAC 1) overcomes the limitation of reliance on population-level peak annotation, 2) improves the clustering accuracy by integrating "off-peak" reads, 3) controls for the major bias using a regression-based normalization method and 4) substantially outperforms current methods in scalability.
- SnapATAC enables other dimentionality reduction (LSA, LSA-logTF, LDA)
- SnapATAC enables clustering using leiden algorithm
- SnapATAC enables batch effect correction
- SnapATAC enables motif analysis using chromVAR
- How to run SnapATAC on 10X dataset?
- I already ran CellRanger, can I use its output for SnapATAC?
- How can I analyze combine multiple samples together?
- How to group reads from any subset of cells?
- What is a snap file anyway?
- How to create a snap file from fastq file?
- Linux/Unix
- Python (>= 2.7) (SnapTools)
- R (>= 3.4.0) (SnapATAC)
Rongxin Fang, Sebastian Preissl, Xiaomeng Hou, Jacinta Lucero, Xinxin Wang, Amir Motamedi, Andrew K. Shiau, Eran A. Mukamel, Yanxiao Zhang, M. Margarita Behrens, Joseph Ecker, Bing Ren. Fast and Accurate Clustering of Single Cell Epigenomes Reveals Cis-Regulatory Elements in Rare Cell Types. bioRxiv 615179; doi: https://doi.org/10.1101/615179
SnapATAC has two components: Snaptools and SnapATAC.
- SnapTools - a python module for pre-processing and working with snap file.
- SnapATAC - a R package for the clustering, annotation, motif discovery and downstream analysis.
Install snaptools from PyPI. See how to install snaptools on FAQs.
$ pip install snaptoolsInstall SnapATAC R pakcage (development version).
$ R
> library(devtools)
> install_github("r3fang/SnapATAC")
