Decoding Heterogenous Single-cell Perturbation Responses
Single-cell RNA-seq · human · 20 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
- 20 samples: 16 × ChannelGroups 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
- Decoding Heterogenous Single-cell Perturbation ResponsesbioRxiv : the preprint server for biology · 2023
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 GSE247601_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 PRJNA1039796 and SRA study SRP471602. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 1 replicated groups read from 20 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.
- GSM7897840 10 clones, pooled scRNA-seq
- GSM7897841 Jurkat HIV, stimulated cells, GFP+
- GSM7897842 Jurkat HIV, stimulated cells, GFP-
- GSM7897843 Jurkat HIV, unstimulated cells
- GSM7951413 Channel 1 Channel
- GSM7951414 Channel 2 Channel
- GSM7951415 Channel 3 Channel
- GSM7951416 Channel 4 Channel
- GSM7951417 Channel 5 Channel
- GSM7951418 Channel 6 Channel
- GSM7951419 Channel 7 Channel
- GSM7951420 Channel 8 Channel
- GSM7951421 Channel 9 Channel
- GSM7951422 Channel 10 Channel
- GSM7951423 Channel 11 Channel
- GSM7951424 Channel 12 Channel
- GSM7951425 Channel 13 Channel
- GSM7951426 Channel 14 Channel
- GSM7951427 Channel 15 Channel
- GSM7951428 Channel 16 Channel
- GSE279945 OP3: single-cell multimodal dataset in PBMCs for perturbation prediction benchmarking 102 samples shares perturbation, single-cell
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
- GSE295214 Global cell-state and gene-program representations reveal conserved and context-specific perturbation responses of cells 85 samples shares perturbation
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE264667 Transcriptome-wide characterization of genetic perturbations 224 samples shares perturbation
- 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.