Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologies
Single-cell RNA-seq · human · 52 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
- 52 samples: 34 distinct titles, 6 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
- Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologiesNAR genomics and bioinformatics · 2026
Read the full summary ↓
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 GSE295527_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 PRJNA1255131 and SRA study SRP581094. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 52 sample titles: 34 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.
- GSM8951933 Sorted T-cells from PBMC, GEM-X 5' GEX/WT, sample F1
- GSM8951934 Sorted T-cells from PBMC, GEM-X 5' GEX/WT, sample F2
- GSM8951935 Sorted T-cells from PBMC, GEM-X 5' GEX/WT, sample F4
- GSM8951936 Sorted T-cells from PBMC, GEM-X 5' GEX/WT, sample F5
- GSM8951937 Sorted T-cells from PBMC, NextGEM 5' GEX/WT, sample F1
- GSM8951938 Sorted T-cells from PBMC, NextGEM 5' GEX/WT, sample F2
- GSM8951939 Sorted T-cells from PBMC, NextGEM 5' GEX/WT, sample F4
- GSM8951940 Sorted T-cells from PBMC, NextGEM 5' GEX/WT, sample F5
- GSM8951941 Sorted T-cells from PBMC, Parse v2 GEX/WT, sample F1
- GSM8951942 Sorted T-cells from PBMC, Parse v2 GEX/WT, sample F2
- GSM8951943 Sorted T-cells from PBMC, Parse v2 GEX/WT, sample F4
- GSM8951944 Sorted T-cells from PBMC, Parse v2 GEX/WT, sample F5
- GSM8951945 Sorted T-cells from PBMC, GEM-X 5' VDJ/TCR, sample F1
- GSM8951946 Sorted T-cells from PBMC, GEM-X 5' VDJ/TCR, sample F2
- GSM8951947 Sorted T-cells from PBMC, GEM-X 5' VDJ/TCR, sample F4
- GSM8951948 Sorted T-cells from PBMC, GEM-X 5' VDJ/TCR, sample F5
- GSM8951949 Sorted T-cells from PBMC, NextGEM 5' VDJ/TCR, sample F1
- GSM8951950 Sorted T-cells from PBMC, NextGEM 5' VDJ/TCR, sample F2
- GSM8951951 Sorted T-cells from PBMC, NextGEM 5' VDJ/TCR, sample F4
- GSM8951952 Sorted T-cells from PBMC, NextGEM 5' VDJ/TCR, sample F5
- GSM8951953 Sorted T-cells from PBMC, Parse v2 VDJ/TCR, sample F1
- GSM8951954 Sorted T-cells from PBMC, Parse v2 VDJ/TCR, sample F2
- GSM8951955 Sorted T-cells from PBMC, Parse v2 VDJ/TCR, sample F4
- GSM8951956 Sorted T-cells from PBMC, Parse v2 VDJ/TCR, sample F5
- GSM8951957 PBMC, Flex, sample F1A
- GSM8951958 PBMC, Flex, sample F1B
- GSM8951959 PBMC, Flex, sample F5A
- GSM8951960 PBMC, Flex, sample F5B
- GSM8951961 PBMC, Fluent v4, sample F1A
- GSM8951962 PBMC, Fluent v4, sample F1B
- GSM8951963 PBMC, Fluent v4, sample F5A
- GSM8951964 PBMC, Fluent v4, sample F5B
- GSM8951965 PBMC, Fluent V, sample F1A
- GSM8951966 PBMC, Fluent V, sample F1B
- GSM8951967 PBMC, Fluent V, sample F5A
- GSM8951968 PBMC, Fluent V, sample F5B
- GSM8951969 PBMC, GEM-X 3', sample F1A
- GSM8951970 PBMC, GEM-X 3', sample F1B
- GSM8951971 PBMC, GEM-X 3', sample F5A
- GSM8951972 PBMC, GEM-X 3', sample F5B
+ 12 more — browse all 52 samples with per-sample file links →
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares single-cell
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares single-cell
- GSE291890 Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression 33 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE305078 Single-cell RNA sequencing of ICB samples from FUSCC 32 samples shares single-cell
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 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.