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Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis

GSE160269 Homo sapiens (human) Single-cell RNA-seq 128 samples Submitted 2020/10/29 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 128 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
128 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)
Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we decipher for the first time the full compositions of ESCC tumor based on analyzing 208,659 single-cell transcriptomes in ESCC derived from 60 individuals.
Read the full summary ↓
We identify 8 essential expression programs from malignant epithelial cells and discovered 43 cell types including 26 immune cell and 17 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and also explicate the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we have linked the cancer cell transcriptomes to the somatic mutations and identified several markers significantly associated with survival time in patients, which may be relevant to precision cares of ESCC patients. These results renew our understanding of ESCC and provide a valuable example and resource for investigating other types of solid tumor.
This dataset
Open GSE160269 in finder → Find more human RNA-seq datasets → Esophageal 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 GSE160269_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 PRJNA672851 and SRA study SRP327758. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 34489433) ↗
Samples in this study

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