Exploring Platelet-Covered and Naked Circulating Tumor Cells: A Single-Cell Transcriptomic Perspective [scRNA-seq 10x_Poznan]
Single-cell RNA-seq · human · 16 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
- 16 samples: 16 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 GSE337705_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 PRJNA1490639 and SRA study SRP715636. 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 16 sample titles: 16 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.
- GSM9859752 HGSOC 01 PBMC/buffy coat single-cell RNA-seq
- GSM9859753 BENIGN CTRL 01 PBMC/buffy coat single-cell RNA-seq
- GSM9859754 HGSOC 02 PBMC/buffy coat single-cell RNA-seq
- GSM9859755 BENIGN CTRL 02 PBMC/buffy coat single-cell RNA-seq
- GSM9859756 BENIGN CTRL 03 PBMC/buffy coat single-cell RNA-seq
- GSM9859757 HGSOC 03 PBMC/buffy coat single-cell RNA-seq
- GSM9859758 HGSOC 04 PBMC/buffy coat single-cell RNA-seq
- GSM9859759 BENIGN CTRL 04 PBMC/buffy coat single-cell RNA-seq
- GSM9859760 HGSOC 05 PBMC/buffy coat single-cell RNA-seq
- GSM9859761 HGSOC 06 PBMC/buffy coat single-cell RNA-seq
- GSM9859762 HGSOC 07 PBMC/buffy coat single-cell RNA-seq
- GSM9859763 HGSOC 08 PBMC/buffy coat single-cell RNA-seq
- GSM9859764 HGSOC 09 PBMC/buffy coat single-cell RNA-seq
- GSM9859765 HGSOC 10 PBMC/buffy coat single-cell RNA-seq
- GSM9859766 HGSOC 11 PBMC/buffy coat single-cell RNA-seq
- GSM9859767 HGSOC 12 PBMC/buffy coat single-cell RNA-seq
- GSE294300 Single-cell gene expression profiles of cells from primary colorectal cancer and adjacent normal tissue [scRNA-Seq] 36 samples shares single-cell, scrna-seq
- GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares single-cell, scrna-seq
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, scrna-seq
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 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
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 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
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.