Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype
Single-cell RNA-seq · human · 31 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
- Kidney cancerThis series is part of our curated briefing on it.
- Samples
- 31 samples: 31 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
- Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotypeCommunications biology · 2024
Read the full summary ↓
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 GSE242299_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 PRJNA1013059 and SRA study SRP458589. 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 31 sample titles: 31 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.
- GSM7757833 patient 2 tumor, emulsion 1, scRNA-seq
- GSM7757834 patient 2 tumor, emulsion 2, scRNA-seq
- GSM7757835 patient 3 tumor, emulsion 1, scRNA-seq
- GSM7757836 patient 3 tumor, emulsion 2, scRNA-seq
- GSM7757837 patient 4 tumor, emulsion 1, scRNA-seq
- GSM7757838 patient 5 tumor, emulsion 1, scRNA-seq
- GSM7757839 patient 5 tumor, emulsion 2, scRNA-seq
- GSM7757840 patient 6 tumor, emulsion 1, scRNA-seq
- GSM7757841 patient 6 tumor, emulsion 2, scRNA-seq
- GSM7757842 patient 6 tumor, emulsion 3, scRNA-seq
- GSM7757843 patient 7 tumor, emulsion 1, scRNA-seq
- GSM7757844 patient 7 tumor, emulsion 2, scRNA-seq
- GSM7757845 patient 7 tumor, emulsion 3, scRNA-seq
- GSM7757846 patient 8 tumor, emulsion 1, scRNA-seq
- GSM7757847 patient 8 tumor, emulsion 2, scRNA-seq
- GSM7757848 patient 8 tumor, emulsion 3, scRNA-seq
- GSM7757849 patient 9 tumor, emulsion 1, scRNA-seq
- GSM7757850 patient 9 tumor, emulsion 2, scRNA-seq
- GSM7757851 patient 9 tumor, emulsion 3, scRNA-seq
- GSM7757852 patient 1 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757853 patient 2 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757854 patient 2 healthy-adjacent kidney, emulsion 2, scRNA-seq
- GSM7757855 patient 3 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757856 patient 3 healthy-adjacent kidney, emulsion 2, scRNA-seq
- GSM7757857 patient 4 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757858 patient 4 healthy-adjacent kidney, emulsion 2, scRNA-seq
- GSM7757859 patient 5 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757860 patient 6 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757861 patient 7 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757862 patient 8 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSM7757863 patient 9 healthy-adjacent kidney, emulsion 1, scRNA-seq
- GSE216494 Impact of sunitinib resistance on clear cell renal cell carcinoma therapeutic sensitivity in vitro 12 samples same disease briefing · shares clear, renal
- GSE193006 Transcriptome RNA sequencing data of sarcomatoid renal cell carcinoma cell line SRCC and its subclones with different phenotypes. 5 samples same disease briefing · shares phenotype, renal
- GSE216495 Nonclinical in-vitro evaluation of the transcriptomic and metabolic effects of immune checkpoint inhibitors on sunitinib-resistant renal cell carcinoma 36 samples same disease briefing · shares renal
- GSE174740 Drug repurposing identifies a synergistic high-order drug combination to treat sunitinib-resistant renal cell carcinoma 11 samples same disease briefing · shares renal
- GSE19982 Gene expression discriminates chromophobe renal cell carcinoma and oncocytoma 30 samples same disease briefing · shares renal
- GSE17895 Somatic Mutation Screen of Clear Cell RCC 160 samples same disease briefing · shares clear
- GSE15641 Gene signatures of progression and metastasis in renal cell cancer 92 samples same disease briefing · shares renal
- GSE214635 The E3 ligase RBCK1 reduces the sensitivity of ccRCC to sunitinib through the ANKRD35-MITD1-ANXA1 axis 18 samples same disease briefing
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.