Gene expression profile at single cell level of human laryngeal normal, preneoplastic and tumorigenic states
Single-cell RNA-seq · human · 40 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
- 40 samples: 40 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.
- Paper
- Comprehensive Single-Cell RNA Atlas of Human Laryngeal Normal, Preneoplastic, and Tumorigenic StatesClinical cancer research : an official journal of the American Association for Cancer Research · 2025
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 GSE290927_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 PRJNA1230997 and SRA study SRP567636. 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 40 sample titles: 40 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.
- GSM8825774 VCP1, replicate 1, scRNAseq
- GSM8825775 VCP2, replicate 1, scRNAseq
- GSM8825776 VCP3, replicate 1, scRNAseq
- GSM8825777 VCP4, replicate 1, scRNAseq
- GSM8825778 VCP5, replicate 1, scRNAseq
- GSM8825779 VCP6, replicate 1, scRNAseq
- GSM8825780 VCP7, replicate 1, scRNAseq
- GSM8825781 VCP8, replicate 1, scRNAseq
- GSM8825782 VCP9, replicate 1, scRNAseq
- GSM8825783 VCP10, replicate 1, scRNAseq
- GSM8825784 VCL1, replicate 1, scRNAseq
- GSM8825785 VCL2, replicate 1, scRNAseq
- GSM8825786 VCL3, replicate 1, scRNAseq
- GSM8825787 VCL4, replicate 1, scRNAseq
- GSM8825788 VCL5, replicate 1, scRNAseq
- GSM8825789 VCL6, replicate 1, scRNAseq
- GSM8825790 VCL7, replicate 1, scRNAseq
- GSM8825791 VCL8, replicate 1, scRNAseq
- GSM8825792 VCL9, replicate 1, scRNAseq
- GSM8825793 VCL10, replicate 1, scRNAseq
- GSM8825794 LSCC1, replicate 1, scRNAseq
- GSM8825795 LSCC2, replicate 1, scRNAseq
- GSM8825796 LSCC3, replicate 1, scRNAseq
- GSM8825797 LSCC4, replicate 1, scRNAseq
- GSM8825798 LSCC5, replicate 1, scRNAseq
- GSM8825799 LSCC6, replicate 1, scRNAseq
- GSM8825800 LSCC7, replicate 1, scRNAseq
- GSM8825801 LSCC8, replicate 1, scRNAseq
- GSM8825802 LSCC9, replicate 1, scRNAseq
- GSM8825803 LSCC10, replicate 1, scRNAseq
- GSM8825804 LSCCP1, replicate 1, scRNAseq
- GSM8825805 LSCCP2, replicate 1, scRNAseq
- GSM8825806 LSCCP3, replicate 1, scRNAseq
- GSM8825807 LSCCP4, replicate 1, scRNAseq
- GSM8825808 LSCCP5, replicate 1, scRNAseq
- GSM8825809 LSCCP6, replicate 1, scRNAseq
- GSM8825810 LSCCP7, replicate 1, scRNAseq
- GSM8825811 LSCCP8, replicate 1, scRNAseq
- GSM8825812 LSCCP9, replicate 1, scRNAseq
- GSM8825813 LSCCP10, replicate 1, scRNAseq
- GSE296969 Gene expression at single cell level of stromal cells from the distal colon of normal sites of colorectal cancer and inflamed sites of ulcerative colitis. 13 samples shares normal, level
- GSE333857 Population-associated molecular variation in histologically normal breast tissue is context-dependent and associated with distinct transcriptional states 29 samples shares normal, states
- GSE264503 Gene expression profile of clinical ESCC tumors, matched normal tissue and lymph node metastatic tissue by RNA-seq analysis 51 samples shares normal
- GSE294300 Single-cell gene expression profiles of cells from primary colorectal cancer and adjacent normal tissue [scRNA-Seq] 36 samples shares normal
- GSE319878 Interrogating the mechanistic link between neighborhood deprivation and colorectal cancer risk through gene expression analysis of normal colorectal biopsies 171 samples shares normal
- GSE295784 Selective modulation of gene expression in activated normal human peripheral blood mononuclear cells by store-operated calcium entry blocker BTP2 40 samples shares normal
- GSE251807 Investigation of gene expression changes in normal human dermal fibroblasts after exposure to bone marrow mesenchymal stromal cell secretome 32 samples shares normal
- GSE300610 Growing iPSC-derived neuronal precursor cells (NPCs) of MT-ATP6 patients and of normal controls under physiological glucose concentrations of 4.5 mM with and without 1 µM or 10 µM Sildenafil for 6 or 24 hours. 192 samples shares normal
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.