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Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics]

GSE134520 Homo sapiens (human) Single-cell RNA-seq 13 samples Submitted 2019/07/20 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 13 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)
Topic
Gastric cancerThis series is part of our curated briefing on it.
Samples
13 samples
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)
Intestinal-type gastric cancer is preceded by premalignant lesions including chronic atrophic gastritis and intestinal metaplasia. In this study, we performed a scRNA-seq survey of 56,440 cells from thirteen gastric antral mucosa biopsies from nine patients with Non-atrophic gastritis (NAG), CAG, IM or early gastric cancer (EGC), and constructed a single-cell transcriptome atlas for gastric premalignant and early-malignant lesions.
Read the full summary ↓
The thirteen biopsies, including three wild superficial gastritis (NAG) ones, three CAG ones, six IM ones and one EGC , spanned the cascade from gastritis to early gastric cancer.For each biopsy, we isolated single cells without prior selection for cell types and utilized the 10x Chromium platform to generate RNA-seq data. After removing low-quality cells (Methods), a total of 32, 332 cells that passed the quality control were retained for subsequent analysis, which yielded a median of 1941 detected genes per cell.
This dataset
Open GSE134520 in finder → Find more human RNA-seq datasets → Gastric cancer landscape →
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 GSE134520_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 PRJNA555477 and SRA study SRP215370. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 31067475) ↗
Samples in this study

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+ 13 more — browse all 13 samples with per-sample file links →

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