HKU Gastric Intestinal Metaplasia study: Single-cell RNA sequencing, transcriptome and methylation profiling
Single-cell RNA-seq · human · 199 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
- 199 samples: 189 distinct titles, 10 of them repeatedGroups 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
- Divergent lineage trajectories and genetic landscapes in human gastric intestinal metaplasia organoids associated with early neoplastic progressionGut · 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 GSE210995_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 PRJNA868480. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 199 sample titles: 189 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.
- GSM6443816 GX001-IM3O
- GSM6443817 GX001-IM3O differentiated
- GSM6443818 GX001-IM3O-spheroid
- GSM6443819 GX023-BO
- GSM6443820 GX023-BO differentiated
- GSM6443821 GX035-AOIMO
- GSM6443822 GX035-AOIMO differentiated
- GSM6443823 GX036-AO
- GSM6443824 GX036-AO differentiated
- GSM6443825 GX059-BO
- GSM6443826 GX059-BO differentiated
- GSM6443827 GX059-IMO
- GSM6443828 GX059-IMO differentiated
- GSM6443829 GX072-AO
- GSM6443830 GX072-AO differentiated
- GSM6443831 GX072-IMO
- GSM6443832 GX072-IMO differentiated
- GSM6443833 GX076-IMO
- GSM6443834 GX076-IMO differentiated
- GSM6443835 GX085-IMO
- GSM6443836 GX085-IMO differentiated
- GSM6443837 GX085-IMO-spheroid
- GSM6443838 GX091-AgOIMO
- GSM6443839 GX091-AgOIMO-spheroid
- GSM6443840 GX098-IMO
- GSM6443841 GX098-IMO differentiated
- GSM6443842 GX106-AL1c
- GSM6443843 H020-NileO
- GSM6443844 H020-NileO differentiated
- GSM6443942 GX001-IM3O [RNA-seq]
- GSM6443943 GX006-IM3O [RNA-seq]
- GSM6443944 GX014-IM2O [RNA-seq]
- GSM6443945 GX023-IM2O [RNA-seq]
- GSM6443946 GX023-IM3dysO [RNA-seq]
- GSM6443947 GX031-IMO [RNA-seq]
- GSM6443948 GX040-IMO [RNA-seq]
- GSM6443949 GX049-IMO [RNA-seq]
- GSM6443950 GX054-IM2O [RNA-seq]
- GSM6443951 GX059-IMO-P15 [RNA-seq]
- GSM6443952 GX060-IMO [RNA-seq]
+ 159 more — browse all 199 samples with per-sample file links →
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- 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 single-cell
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell
- GSE280982 Single-cell sequencing across radiation treatment timepoints in head and neck cancer 49 samples shares single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares single-cell
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares single-cell
- GSE291890 Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression 33 samples shares single-cell
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.