5. Row-wise Aggregation of Methylation Sites

yame rowop provides fast, low-level operations applied row-by-row across all samples of a .cx file.
These operations are frequently used in single-cell methylation analysis, pseudobulk aggregation, QC, and exploratory co-methylation analyses.


Usage: yame rowop -o <operation> <in.cx> <out>

If <out> is omitted, results are written to stdout.

5.1 Common Use Case: Summing Binary Data to Create Pseudobulks

A very common operation is to sum binarized methylation calls across cells belonging to the same cluster.

For example, to generate a pseudobulk for cluster 1 from a single-cell .cg file:

yame subset -l cluster_1_id.txt single_cell.cg \
  | yame rowop -o binasum - > cluster1_pseudobulk.cg

What binasum does:

  • For binary formats (0/1): It counts how many cells have methylation = 1 vs 0.
  • For format 3 (M/U counts): It compares M vs U in each cell and votes for the majority state. Only rows with coverage ≥ -c mincov are counted.

The output is a format 3 .cg file where each position contains:

  • Total number of methylated votes (M)
  • Total number of unmethylated votes (U)

This is ideal for downstream:

  • Pseudobulk methylation beta estimates
  • Differential methylation analysis
  • Cluster-level QC

5.2 Available Operations

Below is a complete list of operations supported by -o.

1. binasum — Sum binarized methylation across samples 

yame rowop -o binasum input.cx output.cx
  • Works on formats 0, 1, and 3
  • Produces a new format 3 dataset
  • For format 3 inputs, rows with depth < -c (default 1) are ignored
  • Interprets each sample’s methylation as a binary vote

Useful for:

  • Pseudobulk construction
  • Collapsing replicates
  • Aggregating barcodes / technical partitions

2. musum — Sum M and U counts directly 

yame rowop -o musum input.cx output.cx
  • Works only on format 3

  • Directly adds up:

    • all M counts
    • all U counts
  • Produces a new .cx file in format 3

Useful when:

  • You want true aggregated read counts
  • You trust the original M/U values (not just binary interpretation)

3. mean — Per-row methylation mean and count 

yame rowop -o mean input.cx > mean.tsv

Output (tab-delimited):

beta_mean    number_of_valid_cells

Only samples with coverage ≥ -c contribute. Rows with no valid values output NA 0.

Useful for:

  • QC summaries
  • Computing single-cell methylation averages
  • Identifying globally variable CpGs

4. std — Per-row methylation standard deviation 

yame rowop -o std input.cx > std.tsv

Output columns:

std_dev    counts

Uses the same filtering as mean. Helpful for:

  • Measuring variability
  • Ranking variable CpGs
  • Identifying DNA methylation landmarks

5. binstring — Convert methylation profiles to binary strings 

yame rowop -o binstring -b 0.6 input.cx > binstrings.txt
  • Converts each sample to a binary string per row
  • Threshold -b decides methylated vs unmethylated (default 0.5)
  • Outputs one string per row

Example output:

0010110101
1100001110
...

Useful for:

  • Clustering by Hamming distance
  • Haplotype inference
  • Co-methylation motif discovery
  • Compression for machine learning models

6. cometh — Co-methylation of neighboring CpGs 

yame rowop -o cometh -w 5 input.cx > cometh.tsv

For each CpG i and neighbors i+1 … i+w, cometh outputs a vector encoding four categories:

  • U0U1
  • U0M1
  • M0U1
  • M0M1

Counts are stored in a compact 64-bit representation; use -v to print them explicitly:

i   U0U1-U0M1-M0U1-M0M1   U0U2-U0M2-M0U2-M0M2   ...

Additional behavior:

  • Only format 3 is allowed
  • Excludes intermediate methylation values (0.3–0.7)
  • Requires both CpGs to have depth ≥ -c mincov
  • -w sets window size (default: 5)

This is useful for:

  • Detecting locally coordinated methylation
  • Assessing haplotype methylation patterns
  • Fragment-level epiallele analysis
  • Identifying footprints or nucleosome-scale structure

5.3 Summary of Operations

Operation Output Type Input Requirement Purpose
binasum .cx (format 3) fmt 0/1/3 Pseudobulk / aggregation
musum .cx (format 3) fmt 3 True count summation
mean text fmt 3 Mean methylation per CpG
std text fmt 3 Standard deviation per CpG
binstring text fmt 3 Binary methylation strings
cometh text fmt 3 Local co-methylation patterns

5.4 Additional Notes

Minimum coverage control (-c)

Many operations ignore rows where:

M + U < mincov

Default is 1.

Verbose output (-v)

For cometh, verbose mode expands packed category counts into readable numbers.

Binarization threshold (-b)

Relevant only for binstring.

5.5 Help and Subcommand Documentation

For detailed usage:

yame rowop -h

Subcommand documentation: