Single-cell RNA sequencing analysis of the course of Mycobacterium avium complex pulmonary disease
Single-cell RNA-seq · human · 21 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
- 21 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.
- Paper
- Single-cell transcriptomics by clinical course of Mycobacterium avium complex pulmonary diseaseScientific reports · 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 GSE226099_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 PRJNA938502 and SRA study SRP424502. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 21 samples.
How we read this: 5 replicated groups read from 21 sample titles; they account for 21 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.
- GSM7063752 C-2_#1 C-2_#
- GSM7063753 C-2_#2 C-2_#
- GSM7063754 C-x_#1 C-x_#
- GSM7063755 C-1_#1 C-1_#
- GSM7063756 C-x_#2 C-x_#
- GSM7063757 C-x_#3 C-x_#
- GSM7063758 C-1_#2 C-1_#
- GSM7063759 B_#1 B_#
- GSM7063760 B_#2 B_#
- GSM7063761 A_#1 A_#
- GSM7063762 C-1_#3 C-1_#
- GSM7063763 B_#3 B_#
- GSM7063764 B_#4 B_#
- GSM7063765 B_#5 B_#
- GSM7063766 A_#2 A_#
- GSM7063767 B_#6 B_#
- GSM7063768 B_#7 B_#
- GSM7063769 C-2_#3 C-2_#
- GSM7063770 A_#3 A_#
- GSM7063771 A_#4 A_#
- GSM7063772 A_#5 A_#
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- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares pulmonary
- 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
- GSE285196 Pathogenesis of Graves' Disease Using Single-Cell Sequencing with Thyroid Autoantigens Peptides Stimulation in B Cells 10 samples shares single-cell
- GSE209781 A single-cell transcriptomic atlas characterizes the kidney in the diabetic kidney disease 6 samples shares single-cell
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 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.