Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas (non-diseased controls, bulkRNAseq)
Single-cell RNA-seq · human · 22 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
- 22 samples: 10 × bulkRNA-seq, HF vs 8 × bulkRNA-seq, HMGroups 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.
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 GSE295449_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 PRJNA1254698 and SRA study SRP580858. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 18 samples.
How we read this: 2 replicated groups read from 22 sample titles; they account for 18 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.
- GSM8949912 bulkRNA-seq, 1HM-6058-3
- GSM8949913 bulkRNA-seq, 2HM-1547-3
- GSM8949914 bulkRNA-seq, 3HF-5670-3
- GSM8949915 bulkRNA-seq, 4HF-6302-3
- GSM8949916 bulkRNA-seq, HF1499 bulkRNA-seq, HF
- GSM8949917 bulkRNA-seq, HF1706 bulkRNA-seq, HF
- GSM8949918 bulkRNA-seq, HF187438 bulkRNA-seq, HF
- GSM8949919 bulkRNA-seq, HF187440 bulkRNA-seq, HF
- GSM8949920 bulkRNA-seq, HF187441 bulkRNA-seq, HF
- GSM8949921 bulkRNA-seq, HF5077 bulkRNA-seq, HF
- GSM8949922 bulkRNA-seq, HF5445 bulkRNA-seq, HF
- GSM8949923 bulkRNA-seq, HF5446 bulkRNA-seq, HF
- GSM8949924 bulkRNA-seq, HF5566 bulkRNA-seq, HF
- GSM8949925 bulkRNA-seq, HF5621 bulkRNA-seq, HF
- GSM8949926 bulkRNA-seq, HM163 bulkRNA-seq, HM
- GSM8949927 bulkRNA-seq, HM1674 bulkRNA-seq, HM
- GSM8949928 bulkRNA-seq, HM187439 bulkRNA-seq, HM
- GSM8949929 bulkRNA-seq, HM187442 bulkRNA-seq, HM
- GSM8949930 bulkRNA-seq, HM187443 bulkRNA-seq, HM
- GSM8949931 bulkRNA-seq, HM5715 bulkRNA-seq, HM
- GSM8949932 bulkRNA-seq, HM5787 bulkRNA-seq, HM
- GSM8949933 bulkRNA-seq, HM5818 bulkRNA-seq, HM
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- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares microenvironment, single-cell
- GSE237876 Deciphering metastatic route-specific signals and their microenvironment interactions in peritoneal metastasis of gastric cancer 66 samples shares microenvironment, interactions
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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.