File Formats
Base level methylation counts
The ALLC (ALL Cytosine) format is a tab-separated table contain base level methylation and coverage counts. ALLC format is originally defined by methylpy, a python package developed in our lab for bulk WGBS-seq data analysis. Each row in an ALLC file corresponds to one cytosine in the genome. An ALLC file contains 7 mandatory columns and no header. YAP uses ALLCools to generate ALLC files compressed and indexed by
bgzip and tabix from the htslib.The ALLC file generated by YAP only contains information from a single cell, while the ALLC file can also be merged from multiple single cells (by cluster, etc.) as a bulk-level methylation table.
index | column name | example | note |
1 | chromosome | chr12 | The same as genome FASTA |
2 | position | 18283342 | 1-based |
3 | strand | + | either + or - |
4 | sequence context | CGT | can be more than 3 bases, used to determine mC type |
5 | mc | 1 | count of reads supporting methylation |
6 | cov | 2 | read coverage, cov >= mc |
7 | methylated | 1 | indicator of significant methylation (1 if no test is performed) |
Using
bgzip and tabix, we can compress the ALLC file while allowing region query. This is done by YAP (using ALLCools) automatically, but here is an example of the exact command to do so:# bgzip is similar to gzip,
# but it allow tabix to generate index from the compressed file.
$ bgzip CELL_ID.allc.tsv
# tabix generate an "CELL_ID.allc.tsv.gz.tbi" file of this ALLC file.
$ tabix -b 2 -e 2 -s 1 CELL_ID.allc.tsv.gz
# you can then query specific region from this ALLC file.
$ tabix CELL_ID.allc.tsv.gz chr1:100000000-101000000 | head
chr1 100016605 + CCT 0 1 1
chr1 100016606 + CTT 0 1 1
chr1 100016614 + CCG 0 1 1
chr1 100016615 + CGC 1 1 1
chr1 100016617 + CCC 0 1 1
chr1 100016618 + CCA 0 1 1
chr1 100016619 + CAC 0 1 1
chr1 100016621 + CCT 0 1 1
chr1 100016622 + CTC 0 1 1
chr1 100016624 + CGG 1 1 1
# see tabix and bgzip documentation for more information
$ tabix -h
I am updating ALLCools and its documentation, once done, will provide a link here. Right now this is a minimum introduction of MCDS.
ALLC file records all the methylation raw counts, but for clustering analysis, we need to do some "binning" to get the feature-level (genomic-region-level) raw counts. After mapping, the
allcools mcds function is used to aggregate all the single-cell ALLC files into a cell-by-feature dataset, called MCDS file.Unlike the ALLC file that has fixed format, the MCDS file is a flexible dataset storing all different kinds of feature counts (gene, genomic bins at different length) at different methylation types (CpH, CpG) in a single netCDF4 file.
MCDS file is generated and manipulated by the python package
xarray.Dataset, which allows easy combination, selection, and transformation of the multi-dimensional raw count array to cell-by-feature 2-D array for clustering. See xarray's documentation for more information.
MCDS file contains all kinds of feature-level methylation counts in a single netCDF4 file.
Here I provide a toy dataset in MCDS format and some example code of reading it using xarray. For more details about MCDS and further clustering steps, please go to the ALLCools documentation.
Last modified 2yr ago