Single-cell dissection of ex vivo human heart tissue reveals pro-inflammatory microvascular changes in the right atrium in ischemic heart disease and heart failure
Single-cell RNA-seq · human · 15 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
- 15 samples: 5 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.
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 GSE214731_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 PRJNA886963. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 15 samples.
How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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.
- GSM6615057 HAEC, RNA, control, rep1 HAEC, RNA, control,
- GSM6615058 HAEC, RNA, control, rep2 HAEC, RNA, control,
- GSM6615059 HAEC, RNA, control, rep3 HAEC, RNA, control,
- GSM6615060 HAEC, RNA, IL1b, 2h, rep1 HAEC, RNA, IL1b, 2h,
- GSM6615061 HAEC, RNA, IL1b, 2h, rep2 HAEC, RNA, IL1b, 2h,
- GSM6615062 HAEC, RNA, IL1b, 2h, rep3 HAEC, RNA, IL1b, 2h,
- GSM6615063 HAEC, RNA, IL1b, 8h, rep1 HAEC, RNA, IL1b, 8h,
- GSM6615064 HAEC, RNA, IL1b, 8h, rep2 HAEC, RNA, IL1b, 8h,
- GSM6615065 HAEC, RNA, IL1b, 8h, rep3 HAEC, RNA, IL1b, 8h,
- GSM6615066 HAEC, RNA, IL1b, 14h, rep1 HAEC, RNA, IL1b, 14h,
- GSM6615067 HAEC, RNA, IL1b, 14h, rep2 HAEC, RNA, IL1b, 14h,
- GSM6615068 HAEC, RNA, IL1b, 14h, rep3 HAEC, RNA, IL1b, 14h,
- GSM6615069 HAEC, RNA, IL1b, 32h, rep1 HAEC, RNA, IL1b, 32h,
- GSM6615070 HAEC, RNA, IL1b, 32h, rep2 HAEC, RNA, IL1b, 32h,
- GSM6615071 HAEC, RNA, IL1b, 32h, rep3 HAEC, RNA, IL1b, 32h,
- 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, changes
- GSE254315 Single-cell transcriptome analysis reveals the molecular mechanisms underlying age-related changes in human testis 25 samples shares single-cell, changes
- GSE220774 Side- and disease-dependent changes in human aortic valve cell population and transcriptomic heterogeneity determined by single-cell RNA sequencing 15 samples shares single-cell, changes
- GSE277524 Single-cell dissection of multifocal bladder cancer reveals malignant and immune cells variation between primary and recurrence tumor lesions 10 samples shares single-cell, dissection
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares single-cell, changes
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 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
- 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.