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Deep Learning Models for Cell Cycle Phase Prediction from Single-Cell RNA Sequencing Data

GSE293316 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2025/10/22 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 10 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
10 samples: 4 × REH leukemia cells, ATAC vs 4 × SUP-B15 leukemia cells, ATACGroups 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
Deep learning models for cell cycle phase prediction from single-cell RNA sequencing dataBriefings in bioinformatics · 2026
Summary (written by the submitters)
Accurate prediction of cell cycle phases is essential for mitigating confounding effects in single-cell RNA sequencing (scRNA-seq) analysis and for understanding how certain diseases (e.g. cancer) develop and respond to treatment. We evaluated traditional machine learning (AdaBoost, Random Forest, LightGBM) and deep learning approaches (DNN3, DNN5, CNN, hybrid models, ensembles) for cell cycle phase prediction from scRNA-seq data.
Read the full summary ↓
Models were trained using consensus phase labels derived from four established tools (CellCycleScore, ccAFv2, Revelio, and Tricycle) applied to single-cell gene expression datasets representing diverse biological contexts, including a human leukemia cell line (REH), peripheral blood mononuclear cells (10× PBMC), human pluripotent stem cells and their differentiated derivatives (GSE75748), 10 k embryonic mouse brain cells, and mouse hematopoietic stem and progenitor cells (GSE81682). Performance was evaluated on three independent datasets: human embryonic stem cells (GSE64016; 247 cells), human osteosarcoma cells (GSE146773; 1152 cells), and mouse embryonic stem cells (E-MTAB-2805; 288 cells), all with experimentally verified labels. REH-trained models achieved strong cross-dataset performance, reaching 74.35% accuracy on GSE146773 (Top 3 decision fusion (DF), and score fusion (SF)) and 72.9% on GSE64016, while attaining 56.9% on the cross-species Buettner mESC benchmark. SHapley Additive exPlanations (SHAP) analysis confirmed enrichment in cell-cycle-related gene sets for features. Notably, models trained on samples with lower recovery rates of cell cycle markers exhibited lower performance. These results highlight the strength of deep learning for robust, scalable cell cycle phase prediction from scRNA-seq data.
Published in
Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data
Akhter H, Piktel D, Gibson LF et al. · Briefings in bioinformatics 2026 · PMID 42437449 · doi:10.1093/bib/bbag342
This dataset
Open GSE293316 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 GSE293316_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 PRJNA1244008 and SRA study SRP575140. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42437449) ↗
Study design
4 × REH leukemia cells, ATAC vs 4 × SUP-B15 leukemia cells, ATAC

Supports a between-group comparison across 8 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 8 of them. 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
Datasets on a similar topic

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