Malignant epithelial states drive immune dysfunction in ampulla of Vater carcinoma
Single-cell RNA-seq · human · 75 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 75 samples: 75 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.
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 GSE335070_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 PRJNA1476553 and SRA study SRP708037. 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 75 sample titles: 75 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.
- GSM9805445 AoV-S1-N (scRNA-seq)
- GSM9805446 AoV-S1-T (scRNA-seq)
- GSM9805447 AoV-S2-N (scRNA-seq)
- GSM9805448 AoV-S2-T (scRNA-seq)
- GSM9805449 AoV-S3-N1 (scRNA-seq, biological replicate 1)
- GSM9805450 AoV-S3-N2 (scRNA-seq, biological replicate 2)
- GSM9805451 AoV-S3-T (scRNA-seq)
- GSM9805452 AoV-S4-T (scRNA-seq)
- GSM9805453 AoV-S5-T (scRNA-seq)
- GSM9805454 AoV-S6-N (scRNA-seq)
- GSM9805455 AoV-S6-T (scRNA-seq)
- GSM9805456 AoV-S7-T (scRNA-seq)
- GSM9805457 AoV-S8-T (scRNA-seq)
- GSM9805458 AoV-B1 (RNA-seq)
- GSM9805459 AoV-B2 (RNA-seq)
- GSM9805460 AoV-B3 (RNA-seq)
- GSM9805461 AoV-B4 (RNA-seq)
- GSM9805462 AoV-B5 (RNA-seq)
- GSM9805463 AoV-B6 (RNA-seq)
- GSM9805464 AoV-B7 (RNA-seq)
- GSM9805465 AoV-B8 (RNA-seq)
- GSM9805466 AoV-B9 (RNA-seq)
- GSM9805467 AoV-B10 (RNA-seq)
- GSM9805468 AoV-B11 (RNA-seq)
- GSM9805469 AoV-B12 (RNA-seq)
- GSM9805470 AoV-B13 (RNA-seq)
- GSM9805471 AoV-B14 (RNA-seq)
- GSM9805472 AoV-B15 (RNA-seq)
- GSM9805473 AoV-B16 (RNA-seq)
- GSM9805474 AoV-B17 (RNA-seq)
- GSM9805475 AoV-B18 (RNA-seq)
- GSM9805476 AoV-B19 (RNA-seq)
- GSM9805477 AoV-B20 (RNA-seq)
- GSM9805478 AoV-B21 (RNA-seq)
- GSM9805479 AoV-B22 (RNA-seq)
- GSM9805480 AoV-B23 (RNA-seq)
- GSM9805481 AoV-B24 (RNA-seq)
- GSM9805482 AoV-B25 (RNA-seq)
- GSM9805483 AoV-B26 (RNA-seq)
- GSM9805484 AoV-B27 (RNA-seq)
+ 35 more — browse all 75 samples with per-sample file links →
- GSE275647 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC RNA-seq] 216 samples shares epithelial, malignant
- GSE276503 Myeloid-derived IL1B drives epithelial and stromal cell remodeling during chronic rhinosinusitis and nasal polyps [scRNA-seq] 33 samples shares epithelial, drive
- GSE267788 Mechanical stretch induces senescence of alveolar epithelial cells and drives fibroblast activation by paracrine mechanisms 25 samples shares epithelial, drive
- GSE298139 Therapy-resistance in AT/RT is driven by epithelial-mesenchymal transition, extracellular matrix changes, and enrichment of CD1A+ CD207+ dendritic cells [snRNA-seq] 16 samples shares epithelial, drive
- GSE275648 Role of Cancer-Associated Fibroblasts in Prognostic Stratification of Advanced-Stage Gastric Cancer through Interacting with Endothelial and Malignant Epithelial Cells [AGC scRNA-seq] 11 samples shares epithelial, malignant
- GSE306201 Single-cell RNA sequencing reveals different cellular states in malignant cells and the tumor microenvironment in primary and metastatic ER-positive breast cancer 22 samples shares malignant, states
- GSE272820 Multi-omics datasets for finer-scale dissection of chromatin architecture alterations in radiation induced malignant transformation of bronchial epithelial cells [Micro-C] 12 samples shares epithelial, malignant
- GSE316079 SLF2 and SMC5 dysfunction drives HSC aging and predisposes to MDS, defining a new inherited bone marrow failure syndrome [ATAC-seq] 6 samples shares dysfunction, drive
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.