Best practices for differential accessibility analysis in single-cell epigenomics
Single-cell RNA-seq · mouse · 16 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 16 samples: 6 conditions, mostly in triplicateGroups 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
- Single-cell and spatial atlases of spinal cord injury in the Tabulae ParalyticaNature · 2024
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE230765_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA962539 and SRA study SRP434885. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 6 replicated groups read from 16 sample titles; they account for 16 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.
- GSM7233992 RNA_2M_18 RNA_2M
- GSM7233993 RNA_2M_2 RNA_2M
- GSM7233994 RNA_2M_7 RNA_2M
- GSM7233995 RNA_D7_1 RNA_D7
- GSM7233996 RNA_D7_17 RNA_D7
- GSM7233997 RNA_UN_1 RNA_UN
- GSM7233998 RNA_UN_20 RNA_UN
- GSM7233999 RNA_UN_6 RNA_UN
- GSM7234000 ATAC_2M_18 ATAC_2M
- GSM7234001 ATAC_2M_2 ATAC_2M
- GSM7234002 ATAC_2M_7 ATAC_2M
- GSM7234003 ATAC_D7_1 ATAC_D7
- GSM7234004 ATAC_D7_17 ATAC_D7
- GSM7234005 ATAC_UN_1 ATAC_UN
- GSM7234006 ATAC_UN_20 ATAC_UN
- GSM7234007 ATAC_UN_6 ATAC_UN
- GSE294772 Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging [snATAC-Seq] [10X Multiome] 142 samples shares single-cell, epigenomics
- GSE216371 A single-cell atlas of chromatin accessibility in mouse organogenesis 68 samples shares accessibility, single-cell
- GSE236450 SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes 24 samples shares differential
- GSE192687 Single-cell RNAseq analysis of renal endothelial cells in murine diabetic nephropathy 5464 samples shares single-cell
- GSE239477 Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution 72 samples shares single-cell
- GSE314068 Mapping the ISR Landscape in Cognitive Disorders via single-cell multi-omics 28 samples shares single-cell
- GSE333887 Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland [transplantations_OneCell_multiome] 14 samples shares single-cell
- GSE215963 Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma. 1497 samples shares single-cell
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.