Massively multiplex single-cell Hi-C
Single-cell RNA-seq · human · 6 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
- 6 samples: 4 × Combinatorial scHi-C Library ML vs 2 × Combinatorial scHi-C Library PLGroups 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.
Direct links to NCBI, 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.
Whole study · RAW.tar
Everything the authors deposited as one archive (GSE84920_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.
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 PRJNA335638 and SRA study SRP079988. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 6 samples.
How we read this: 2 replicated groups read from 6 sample titles; they account for 6 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.
- GSM2254215 Combinatorial scHi-C Library ML1 Combinatorial scHi-C Li…
- GSM2254216 Combinatorial scHi-C Library ML2 Combinatorial scHi-C Li…
- GSM2254217 Combinatorial scHi-C Library ML3 Combinatorial scHi-C Li…
- GSM2254218 Combinatorial scHi-C Library PL1 Combinatorial scHi-C Li…
- GSM2254219 Combinatorial scHi-C Library PL2 Combinatorial scHi-C Li…
- GSM2438426 Combinatorial scHi-C Library ML4 Combinatorial scHi-C Li…
- GSE300551 Massively multiplex multi-modal chemical screens at single-cell resolution with icCITE-plex 25 samples shares single-cell, massively, multiplex
- GSE80006 Single-cell Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition 246 samples shares single-cell, hi-c
- GSE150672 Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology 438 samples shares single-cell, massively
- GSE267117 scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [cellline_scHiCAR] 34 samples shares single-cell
- GSE67446 Multiplex Single Cell Profiling of Chromatin Accessibility by Combinatorial Cellular Indexing [ATAC-seq] 6 samples shares multiplex
- GSE65360 Single-cell chromatin accessibility data using scATAC-seq 1632 samples shares single-cell
- GSE262580 High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations 697 samples shares single-cell
- GSE246613 Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer 266 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.