33. predict_sex

33.1. Overview

predict_sex predicts biological sex from X-chromosome DNA methylation using the semi-methylation (SM) ratio.

The method uses the observation that X-chromosome inactivation produces a higher proportion of semi-methylated CpGs in samples with two X chromosomes.

For each sample, X-linked CpGs are divided into three Beta-value ranges:

Category	Beta-value range	Interpretation
Low	`0.0 <= beta <= 0.2`	Low methylation.
Mid	`0.3 <= beta <= 0.7`	Semi-methylation range.
High	`0.8 <= beta <= 1.0`	High methylation.

The score is calculated as:

\[\log_2(\mathrm{SM\ ratio}) = \log_2\left( \frac{N_{\mathrm{mid}}} {N_{\mathrm{low}} + N_{\mathrm{high}}} \right)\]

where N_mid, N_low, and N_high are the numbers of X-linked CpGs falling in the corresponding Beta-value ranges.

33.2. Prediction Rule

With cutoff \(c\):

log2_SM_ratio > c -> Female
log2_SM_ratio < c -> Male
log2_SM_ratio == c -> Unknown

The default cutoff is 0.0.

The prediction is also Unknown when the SM ratio is undefined, for example when there are no CpGs in the mid range or when the combined low/high count is zero.

33.3. Input Files

33.3.1. Beta-value matrix

The input must be a tab-separated Beta-value matrix with CpGs in rows and samples in columns.

Example:

CpG_ID   Sample_01   Sample_02   Sample_03
cg_001   0.831035    0.878022    0.794427
cg_002   0.249544    0.209949    0.234294
cg_003   0.845065    0.843957    0.840184

Requirements:

CpG IDs must be unique.
Sample IDs must be unique.
At least one sample must be present.

Non-numeric values are converted to missing values and ignored independently for each sample.

33.3.2. X-chromosome CpG file

The X-probe file contains one X-chromosome CpG ID per line.

Example:

cg00000029
cg00000108
cg00000165

Blank lines and lines beginning with # are ignored. Duplicate probe IDs are removed automatically.

At least one supplied X-linked CpG must also be present in the Beta-value matrix.

33.4. Usage

Basic usage:

predict_sex \
    -i test_10.tsv.gz \
    -x chrX_CpGs.txt.gz \
    -o output

Use a custom cutoff:

predict_sex \
    -i test_10.tsv.gz \
    -x chrX_CpGs.txt.gz \
    --cut 0.25 \
    -o output

Available options are:

-i, --input_file – tab-separated Beta-value matrix
-x, --xprobe – X-chromosome CpG-ID file
-c, --cut – log2 SM-ratio cutoff (default: 0.0)
-o, --output – output prefix
--version – show the CpGtools version

Display all options with:

predict_sex -h

33.5. Output

For output prefix output, the command writes:

output.predicted_sex.tsv

The output contains one row per sample:

Column	Description
`Sample_ID`	Sample identifier from the input matrix.
`log2_SM_ratio`	Log2 semi-methylation ratio.
`Predicted_sex`	`Male`, `Female`, or `Unknown`.
`X_CpGs_used`	Number of finite X-linked Beta-values available for that sample.
`Low_beta_CpGs`	Number of X-linked CpGs with Beta-values in [0.0, 0.2].
`Mid_beta_CpGs`	Number of X-linked CpGs with Beta-values in [0.3, 0.7].
`High_beta_CpGs`	Number of X-linked CpGs with Beta-values in [0.8, 1.0].

Undefined numeric ratios are written as NaN.

33.6. Example Data

33.7. Evaluation

In the original CpGtools evaluation using Illumina HumanMethylation450 BeadChip data from GSE105018, the classifier was reported to correctly classify 832 male and 826 female samples.