Can Geniml help with single-cell analysis?

Yes, Geniml includes the scEmbed module which is optimized for training embeddings and clustering cells based on single-cell chromatin accessibility data (scATAC-seq).

What is a 'universe' in Geniml?

In Geniml, a universe is a reference set of consensus peaks built from multiple BED files that serves as a standard coordinate system for tokenization and model training.

What type of data does Geniml work with?

Geniml is specifically designed for genomic interval data, primarily formatted as BED (Browser Extensible Data) files, and supports large collections of these files for machine learning.

Does Geniml integrate with other bioinformatics tools?

Yes, it is designed to work seamlessly with Scanpy for single-cell workflows and the BEDbase ecosystem for genomic data management.

Geniml Genomic Interval ML

Name: Geniml Genomic Interval ML
Author: ricable

byricable

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数据科学与机器学习

Streamlines machine learning workflows for genomic interval data using region embeddings, single-cell analysis, and consensus peak building.

关于

Geniml is a specialized toolkit for applying machine learning to genomic interval data, such as BED files. It provides sophisticated unsupervised methods for learning embeddings of genomic regions (Region2Vec), single cells (scEmbed), and experimental metadata (BEDspace), enabling high-dimensional data reduction, similarity searches, and clustering. This skill helps researchers and developers build reference universes, tokenize genomic features, and evaluate embedding quality, making it an essential tool for advanced bioinformatics tasks like scATAC-seq analysis and cross-modal genomic queries.

主要功能

scEmbed for single-cell chromatin accessibility analysis
Comprehensive evaluation metrics for genomic embeddings
Region2Vec for unsupervised genomic region embeddings
BEDspace for joint region and metadata embedding spaces
Statistical consensus peak (universe) building methods
1 GitHub stars

使用场景

Clustering and cell-type annotation of scATAC-seq datasets
Creating searchable genomic databases using metadata-aware embeddings
Generating feature vectors for downstream deep learning on BED file collections

关于

主要功能

scEmbed for single-cell chromatin accessibility analysis
Comprehensive evaluation metrics for genomic embeddings
Region2Vec for unsupervised genomic region embeddings
BEDspace for joint region and metadata embedding spaces
Statistical consensus peak (universe) building methods
1 GitHub stars

使用场景

Clustering and cell-type annotation of scATAC-seq datasets
Creating searchable genomic databases using metadata-aware embeddings
Generating feature vectors for downstream deep learning on BED file collections