Mapping Ischemia Reperfusion Inflammatory Responses in Human Lung Transplantation at Single-Cell Resolution
Single-cell RNA-seq · human · 12 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
- 12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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
- Ischemia-reperfusion responses in human lung transplants at the single-cell resolutionAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024
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 GSE220797_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 PRJNA911408. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 12 sample titles: 12 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.
- GSM6820277 patient_1_cit
- GSM6820278 patient_1_2hr
- GSM6820279 patient_2_cit
- GSM6820280 patient_2_2hr
- GSM6820281 patient_3_cit
- GSM6820282 patient_3_2hr
- GSM6820283 patient_4_cit
- GSM6820284 patient_4_2hr
- GSM6820285 patient_5_cit
- GSM6820286 patient_5_2hr
- GSM6820287 patient_6_cit
- GSM6820288 patient_6_2hr
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell, lung
- GSE281740 single-cell RNA-sequence data of clinical small cell lung cancer samples 19 samples shares single-cell, lung
- GSE253355 Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics 12 samples shares single-cell, mapping
- GSE309834 Massively multiplex multi-modal chemical screens at single-cell resolution with DOGMA-plex 140 samples shares single-cell, resolution
- GSE285515 NKG2D blockade impairs cytotoxic CD8+ tissue resident memory T cell accumulation and attenuates severity of chronic lung allograft dysfunction after transplantation 15 samples shares transplantation, lung
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 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
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 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.