Gene expression profile at single cell level of clinical mild to moderate COPD
Single-cell RNA-seq · human · 18 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
- 18 samples: 18 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.
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 GSE279570_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 PRJNA1173543 and SRA study SRP538886. 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 18 sample titles: 18 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.
- GSM8576142 CTL01,scRNAseq
- GSM8576143 CTL02,scRNAseq
- GSM8576144 CTL03,scRNAseq
- GSM8576145 CTL04,scRNAseq
- GSM8576146 CTL05,scRNAseq
- GSM8576147 CTL06,scRNAseq
- GSM8576148 CTL07,scRNAseq
- GSM8576149 COPD01,scRNAseq
- GSM8576150 COPD02,scRNAseq
- GSM8576151 COPD03,scRNAseq
- GSM8576152 COPD04,scRNAseq
- GSM8576153 COPD05,scRNAseq
- GSM8576154 COPD06,scRNAseq
- GSM8576155 COPD07,scRNAseq
- GSM8576156 COPD08,scRNAseq
- GSM8576157 COPD09,scRNAseq
- GSM8576158 COPD10,scRNAseq
- GSM8576159 COPD11,scRNAseq
- GSE161245 Comparison of bronchial epithelial cells obtained by bronchoscopy from mild asthma patients, moderate asthma patients, severe asthma patients and healthy controls 55 samples shares moderate, mild
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares copd
- GSE278206 Characterization of the transcriptional profile of circulating neutrophils from COPD patients [RNA-Seq] 77 samples shares copd
- GSE270667 Single-cell sequencing highlights increased atypical AT2 interacting with M1 macrophage in COPD 12 samples shares copd
- GSE306950 Dysregulated NK Cell Activation and Myeloid-Lymphoid Imbalance Underpin COPD Progression: Insights from High-Dimensional Immune Profiling and Smoking-Induced Immune Remodeling 35 samples shares copd
- GSE296912 scRNA-seq of normal and COPD lungs 7 samples shares copd
- GSE313090 Single-nucleus and spatial transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) 153 samples shares copd
- GSE254569 Gene expression profiles at single-cell level of human post-mortem brain tissue from the orbitofrontal cortex 92 samples shares level
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.