Multimodal measurement of the transcriptome and proteome in single cells using nanoSPLITS
Single-cell RNA-seq · human · 192 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
- 192 samples: 16 conditions, mostly with about 12 replicates eachGroups 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.
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 GSE247519_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 PRJNA1039282 and SRA study SRP471342. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 192 samples.
How we read this: 16 replicated groups read from 192 sample titles; they account for 192 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.
- GSM7891193 Chip2A1 Chip2A
- GSM7891194 Chip2A2 Chip2A
- GSM7891195 Chip2A3 Chip2A
- GSM7891196 Chip2A4 Chip2A
- GSM7891197 Chip2A5 Chip2A
- GSM7891198 Chip2A6 Chip2A
- GSM7891199 Chip2A7 Chip2A
- GSM7891200 Chip2A8 Chip2A
- GSM7891201 Chip2A9 Chip2A
- GSM7891202 Chip2A10 Chip2A
- GSM7891203 Chip2A11 Chip2A
- GSM7891204 Chip2A12 Chip2A
- GSM7891205 Chip2B1 Chip2B
- GSM7891206 Chip2B2 Chip2B
- GSM7891207 Chip2B3 Chip2B
- GSM7891208 Chip2B4 Chip2B
- GSM7891209 Chip2B5 Chip2B
- GSM7891210 Chip2B6 Chip2B
- GSM7891211 Chip2B7 Chip2B
- GSM7891212 Chip2B8 Chip2B
- GSM7891213 Chip2B9 Chip2B
- GSM7891214 Chip2B10 Chip2B
- GSM7891215 Chip2B11 Chip2B
- GSM7891216 Chip2B12 Chip2B
- GSM7891217 Chip2C1 Chip2C
- GSM7891218 Chip2C2 Chip2C
- GSM7891219 Chip2C3 Chip2C
- GSM7891220 Chip2C4 Chip2C
- GSM7891221 Chip2C5 Chip2C
- GSM7891222 Chip2C6 Chip2C
- GSM7891223 Chip2C7 Chip2C
- GSM7891224 Chip2C8 Chip2C
- GSM7891225 Chip2C9 Chip2C
- GSM7891226 Chip2C10 Chip2C
- GSM7891227 Chip2C11 Chip2C
- GSM7891228 Chip2C12 Chip2C
- GSM7891229 Chip2D1 Chip2D
- GSM7891230 Chip2D2 Chip2D
- GSM7891231 Chip2D3 Chip2D
- GSM7891232 Chip2D4 Chip2D
+ 152 more — browse all 192 samples with per-sample file links →
- GSE269335 Single-cell multimodal profiling of monocytes reveals diverse phenotypes and alterations linked to cardiovascular disease risks 118 samples shares multimodal
- GSE295725 Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors 98 samples shares multimodal
- GSE228127 Multimodal single-cell profiling reveals the enhanced antitumor activity of FGFR4/CD276 BiCisCAR T cells in rhabdomyosarcoma 12 samples shares multimodal
- GSE262793 Integrative transcriptome and proteome profiling of insulin-resistant kidney cell models and patient biopsies reveals common and cell-type-specific mechanisms underpinning Diabetic Kidney Disease 80 samples shares proteome
- GSE298777 Transcriptome-Proteome analysis of human naive and memory B cell subsets reveal isotype and subclass-specific phenotypes 59 samples shares proteome
- GSE242202 Age-related changes in human skeletal muscle transcriptome and proteome are more affected by chronic inflammation and physical inactivity than primary aging 59 samples shares proteome
- GSE302890 Discovery of kidney disease targets using multimodal human podocyte injury models 48 samples shares multimodal
- GSE260842 Multimodal single cell analyses reveal mechanisms of perianal fistula in diverse Crohn’s disease [scRNA-seq] 34 samples shares multimodal
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.