Last updated: 2022-09-15
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Knit directory: chromap_vs_cellranger_scATAC_exploration_10x/
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This site contains the code and results of my comparative analysis of chromap v0.1 vs cellranger-atac v2.0.0 in the processing and analysis of 10x scATAC-seq data.
For illustration purposes, I utilize publicly-accessible 10x scATAC-seq data from PBMCs and HGMM (GM12878 B-lymphocytes).
All the code and results of this analysis are available from GitHub at https://github.com/jeremymsimon/chromap_vs_cellranger_scATAC_exploration_10x.
Follow the links below to access different pieces of the analysis.
My personal preference is to support open-source software whenever possible, while being cognizant of computational efficiency. Cellranger is rather slow (runtimes included in the relevant chapter below), in part because it does more downstream analysis than I desire (clustering, marker gene/region detection, etc). I also like my approaches to remain flexible and modular such that new versions and tools can be easily incorporated as they are introduced.
Chromap is one such new approach to align and pre-process scATAC-seq (and other) data very quickly. It by default outputs a fragments file akin to cellranger, which contains the chromosome, start, and end coordinates of aligned fragments, along with the cellular barcode associated with that fragment, and the number of read pairs associated with that fragment. The associated manuscript states it is over 10 times (ironically, 10x, lol) faster than traditional approaches e.g. cellranger. Their paper does a great job already of comparing the alignments and resulting clusters to conventional approaches, so that should be your starting point before diving into what I’ve done here. The reason for my duplicating some of those efforts here is because A) their approach utilized MAESTRO as an intermediate for all processing, and B) they did not utilize a dataset with multiple conditions or replicates. Although MAESTRO is itself a very useful tool, I wished to take either the cellranger or chromap outputs and take a more direct path to R. Here, I utilize the Signac/Seurat framework, but the logic should be generalizable to other ecosystems.
Though to my knowledge this hasn’t been fully demonstrated in the literature, I also wish to incorporate an atlas of accessible chromatin from 222 human cell types. The rationale here is that peak-calling on one entire sample at once can miss accessible regions specific to rare cell types or that may be condition specific. As an analogy, for a differential splicing analysis, we may utilize a database of known as well as de novo detected splice junctions unique to our data. This is my thinking here - that we can increase our power to detect important regulatory elements by utilizing genomic loci known to play a regulatory role alongside regions that may be important in our own data. This sort of approach is supported in MAESTRO with --custompeak, so other groups may have a similar philosophy.
There are certainly other ways of aligning scATAC-seq reads, quantifying signal across the genome, and identifying critical regulatory elements for downstream analysis using methods not used or mentioned here. The comparative analysis presented here is not meant to be comprehensive or definitive, nor will I claim my approach is the best or the right-est or the efficient-est.
The analysis presented here merely serves as a proof-of-principle that a cellranger-independent approach is feasible, that chromap seems like a viable alternative, and that this framework can serve as the foundation for future improvements in peak-calling and feature selection.