File Formats
Base level methylation counts
ALLCools
Both ALLC and MCDS files are generated by ALLCools. Read more details about ALLCools here.
ALLC File
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.
Columns in ALLC file
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)
Tabix of ALLC file
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
MCDS File
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.

Example of using MCDS
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.
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