alma-classifier 0.2.0

Epigenomic diagnosis of acute leukemia (research use only)

ALMA Classifier

Epigenomic diagnosis of acute leukemia and prognosis of AML.

Models

1. ALMA Subtype: Classifies 27 subtypes of acute leukemia according to WHO 2022 + healthy control
2. AML Epigenomic Risk (v0.1.4 only): Predicts 5-year mortality probability for AML patients
3. 38CpG AML Signature: Risk stratification using targeted 38 CpG panel

What's New in v0.2.0

• ALMA Subtype v2:
  • New diagnostic model using autoencoder-transformer architecture
  • Near-perfect accuracy in both methylation arrays and nanopore epigenomes
  • v0.1.4 (from the publication) remains available in pip and docker.

Installation

Docker (recommended)

docker pull fmarchi/alma-classifier:0.2.0

Python 3.11

pip install alma-classifier

Usage

Docker

Run demo:

docker run --rm -v "$(pwd)":/work -w /work fmarchi/alma-classifier:0.2.0 \
  alma-classifier --demo

Run using your data:

# Transfer your input data to current working directory
docker run --rm -v "$(pwd)":/work -w /work fmarchi/alma-classifier:0.2.0 \
  alma-classifier -i /work/your_data.pkl

Python 3.11 (CLI)

Run demo:

alma-classifier --demo

Run using your data:

alma-classifier -i path/to/your_data.pkl

Input Formats

Illumina Methylation450k or EPIC

Prepare a .pkl (or csv.gz) dataset with the following structure:

• Rows: Samples
• Columns: CpG sites
• Values: Beta values (0-1)

Got .idat files? Use SeSAMe first.

Nanopore whole genome sequencing

Follow the standard bedMethyl format with these key columns:

• Column 1: chrom - Chromosome name
• Column 2: start_position - 0-based start position
• Column 4: modified_base_code - Single letter code for modified base
• Column 11: fraction_modified - Percentage of methylation (0-100)

Got .bam files? Use modkit first:

modkit pileup \
"$bam_file" \
"$bed_file" \
-t $threads \
--combine-strands \
--cpg \
--ignore h \
--ref ref/hg38.fna \
--no-filtering

CLI options

usage: alma-classifier [-h] [-i INPUT_DATA] [-o OUTPUT] [--download-models] [--demo] [--all_probs]

🩸🧬 ALMA Classifier – Epigenomic diagnosis of acute leukemia (research use only) 🧬🩸

options:
  -h, --help            show this help message and exit
  -i INPUT_DATA, --input_data INPUT_DATA
                        Input file: .pkl with β‑values, .csv/.csv.gz with β‑values, or .bed/.bed.gz nanopore file
  -o OUTPUT, --output OUTPUT
                        .csv output (default: alongside input data)
  --download-models     Download model weights from GitHub release
  --demo                Run demo with example dataset
  --all_probs           Include all subtype/class probabilities as separate columns in the output

Output

Results include subtype classification, risk prediction, and confidence scores.

Important limitations

• The diagnostic model does not currently recognize: AML with Down Syndrome, juvenile myelomonocytic leukemia, transient abnormal myelopoiesis, low-risk MDS, or lymphomas. We need reference methylation data for these patient populations.
• Follow preprocessing as instructed in "Input Formats" above. Different or erroneous preprocessing may lead to poor performance. This applies to bad wet-lab handling of samples.
• The models will attempt to work with missing CpGs. Ideally, use Methylation Array 450k,EPIC or WGS Nanopore Seq with >5x coverage. Anything below that may compromise performance.

Citation

Marchi, F., Shastri, V.M., Marrero, R.J. et al. Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia. Nat Commun 16, 6961 (2025). https://doi.org/10.1038/s41467-025-62005-4