Dynamic changes of lung homeostasis from development-to-aging single-cell atlas
Single-cell RNA-seq · human · 10 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
- 10 samples: 10 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
- A single-cell atlas of lung homeostasis reveals dynamic changes during development and agingCommunications biology · 2024
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 GSE260769_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 PRJNA1083452 and SRA study SRP493154. 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 10 sample titles: 10 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.
- GSM8124163 T1, replicate1, scRNAseq
- GSM8124164 T1, replicate2, scRNAseq
- GSM8124165 T2, replicate1, scRNAseq
- GSM8124166 T2, replicate2, scRNAseq
- GSM8124167 T2, replicate3, scRNAseq
- GSM8124168 T3, replicate1, scRNAseq
- GSM8124169 T4, replicate1, scRNAseq
- GSM8124170 T4, replicate2, scRNAseq
- GSM8124171 T5, replicate1, scRNAseq
- GSM8124172 T5, replicate2, scRNAseq
- 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, dynamic, changes
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell, lung
- 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
- GSE330029 Temporal changes in metabolism guide oligodendrocyte precursor cell dynamics in aging and multiple sclerosis [BulkRNAseq] 108 samples shares dynamic, changes
- GSE271406 Spatial-Temporal Analysis Revealed the Dynamic Changes of Leukemia Neighborhood After Receiving Pembrolizumab and Decitabine 55 samples shares dynamic, changes
- GSE254315 Single-cell transcriptome analysis reveals the molecular mechanisms underlying age-related changes in human testis 25 samples shares single-cell, changes
- GSE281740 single-cell RNA-sequence data of clinical small cell lung cancer samples 19 samples shares single-cell, lung
- 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
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.