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Biology and Ontogeny of Human Small Airway Epithelium Club Cells Identified with Single Cell Transcriptome and Principle Component Gradient Analysis

GSE94819 Homo sapiens (human) Single-cell RNA-seq 157 samples Submitted 2024/06/05 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 157 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
157 samples: 67 × DGM-13206_Sample vs 63 × DGM-10161_Sample vs 27 × DGM-10500_SampleGroups 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.
Summary (written by the submitters)
Club (“Clara”) cells, dome shaped cells with disease cytoplasmic granules and microvilli, are the major secretory cell of the human small (>6 generation) airways. Little is known regarding the biology of club cells in the human airway, nor of the ontogeny of this cell type. Taking advantage of the SCGB1A1 as the marker for club cells and our ability to sample the normal small airway epithelium by bronchoscopy and brushing healthy volunteers, we defined the transcriptome of the normal human airway club cell using single cell transcriptome sequencing.
Read the full summary ↓
Analysis of the human small airway epithelial single cell transcriptome together with in vitro validation provides novel insights into the molecular phenotype and biological functions of the human club cell population and identifies the basal cell as the human progenitor cells for club cells.
This dataset
Open GSE94819 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE94819_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA374517 and SRA study SRP099422. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
67 × DGM-13206_Sample vs 63 × DGM-10161_Sample vs 27 × DGM-10500_Sample

Supports a between-group comparison across 157 samples.

How we read this: 3 replicated groups read from 157 sample titles; they account for 157 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.

Samples in this study

+ 117 more — browse all 157 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.