GEO series

HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data

GSE302151 Homo sapiens (human) Single-cell RNA-seq 31 samples Submitted 2025/12/02 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 31 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 31 samples: 27 distinct titles, 1 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: HALO: hierarchical causal modeling for single cell multi-omics dataNature communications · 2025

Summary (written by the submitters)

Though open chromatin may promote active transcription, gene expression responses may not be directly coordinated with changes in chromatin accessibility. Most existing methods for single-cell multi-omics data focus only on learning stationary and shared information among these modalities, overlooking modality-specific information delineating cellular states and dynamics resulting from causal relations among modalities.

Read the full summary ↓

To account for this, the epigenome and transcriptome relationship can be characterized in relation to time as “coupled” (changing dependently) or “decoupled” (changing independently). We propose the framework HALO, which adopts a causal approach to model these temporal causal relations on two levels. On the representation level, HALO factorizes these two modalities into both coupled and decoupled latent representations, identifying the dynamic interplay between chromatin accessibility and transcription through temporal modulations in the latent space. On the individual gene level, HALO matches gene-peak pairs and characterizes changing dynamics between gene expression and local peaks with time. HALO reveals bipotency in a subset of AT2 cells that exhibit different decisions in lineage specification between systemic sclerosis (SSc) and normal conditions. We demonstrate that using coupled and decoupled information, HALO discovers analogous biological functions between modalities, distinguishes epigenetic factors for lineage specification, and identifies temporal cis-regulation interactions relevant to cellular differentiation and complex human diseases.

Published in

HALO: hierarchical causal modeling for single cell multi-omics data

Mao H, Jia M, Di M et al. · Nature communications 2025 · PMID 41057364 · doi:10.1038/s41467-025-63921-1

This dataset

Open GSE302151 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE302151_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1289074. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41057364) ↗

Study design

27 distinct titles, 1 of them repeated

SC ×5

How we read this: Read from 31 sample titles: 27 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM9097092 SSClungSC369 [scATAC-seq]
GSM9097093 SSClungSC381 [scATAC-seq]
GSM9097094 SSClungSC395 [scATAC-seq]
GSM9097095 SSClungSC402 [scATAC-seq]
GSM9097096 SSClungSC430 [scATAC-seq]
GSM9097097 SSClungSC449 [scATAC-seq]
GSM9097098 NORlungSC398 [scATAC-seq]
GSM9097099 NORlungSC421 [scATAC-seq]
GSM9097100 NORlungSC422 [scATAC-seq]
GSM9097101 NORlungSC453 [scATAC-seq]
GSM9097102 NORlungSC466 [scATAC-seq]
GSM9097103 NORlungSC486 [scATAC-seq]
GSM9097104 NORlungSC490 [scATAC-seq]
GSM9097105 SC294
GSM9097106 SC324
GSM9097107 SC316
GSM9097108 SC336
GSM9097109 SC346
GSM9097110 SSClungSC369 [scRNA-seq]
GSM9097111 SSClungSC381 [scRNA-seq]
GSM9097112 SSClungSC395 [scRNA-seq]
GSM9097113 SSClungSC402 [scRNA-seq]
GSM9097114 SSClungSC430 [scRNA-seq]
GSM9097115 SSClungSC449 [scRNA-seq]
GSM9097116 NORlungSC398 [scRNA-seq]
GSM9097117 NORlungSC421 [scRNA-seq]
GSM9097118 NORlungSC422 [scRNA-seq]
GSM9097119 NORlungSC453 [scRNA-seq]
GSM9097120 NORlungSC466 [scRNA-seq]
GSM9097121 NORlungSC486 [scRNA-seq]
GSM9097122 NORlungSC490 [scRNA-seq]

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