Cellular origin and clonal evolution of human dedifferentiated liposarcoma
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. 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
- 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
- Cellular origin and clonal evolution of human dedifferentiated liposarcomaNature communications · 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 GSE221493_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 PRJNA914686 and SRA study SRP414286. 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.
- GSM6876510 Tumor patient 1 (DDLPS_WD), scRNAseq
- GSM6876511 Tumor patient 1 (DDLPS_DD), scRNAseq
- GSM6876512 Tumor patient 2 (DDLPS_sWD), scRNAseq
- GSM6876513 Tumor patient 2 (DDLPS_DD), scRNAseq
- GSM6876514 Tumor patient 3 (WDLPS), scRNAseq
- GSM6876515 Tumor patient 3 (peritumoral_fat), scRNAseq
- GSM6876516 Tumor patient 4 (DDLPS_WD), scRNAseq
- GSM6876517 Tumor patient 4 (DDLPS_DD), scRNAseq
- GSM6876518 Tumor patient 5 (WDLPS), scRNAseq
- GSM6876519 Tumor patient 6 (DDLPS_WD), scRNAseq
- GSM6876520 Tumor patient 6 (peritumoral_fat), scRNAseq
- GSM6876521 Tumor patient 7 (DDLPS_WD), scRNAseq
- GSM6876522 Tumor patient 7 (DDLPS_DD), scRNAseq
- GSM6876523 Tumor patient 7 (peritumoral_fat), scRNAseq
- GSM6876524 Tumor patient 8 (WDLPS), scRNAseq
- GSM6876525 Tumor patient 9 (WDLPS), scRNAseq
- GSM6876526 Tumor patient 10 (DDLPS_WD), scRNAseq
- GSM6876527 Tumor patient 10 (DDLPS_DD), scRNAseq
- GSM6876528 Tumor patient 11 (lipoma), scRNAseq
- GSM6876529 Tumor patient 12 (DDLPS_WD), scRNAseq
- GSM6876530 Tumor patient 12 (peritumoral_fat), scRNAseq
- GSM6876531 Tumor patient 13 (WDLPS), scRNAseq
- GSM6876532 Tumor patient 14 (DDLPS_WD), scRNAseq
- GSM6876533 Tumor patient 14 (DDLPS_DD), scRNAseq
- GSM6876534 Tumor patient 14 (DDLPS_DDK), scRNAseq
- GSM6876535 Tumor patient 15 (DDLPS_WD), scRNAseq
- GSM6876536 Tumor patient 15 (DDLPS_DD), scRNAseq
- GSM6876537 Tumor patient 16 (DDLPS_WD), scRNAseq
- GSM6876538 Tumor patient 16 (DDLPS_DD), scRNAseq
- GSM6876539 Tumor patient 17 (DDLPS_DD), scRNAseq
- GSM6876540 Tumor patient 17 (peritumoral_fat), scRNAseq
- GSE305751 Temporal and Clonal Resolution of Cellular Evolution Under Stress 42 samples shares evolution, cellular, clonal
- 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 evolution, clonal
- GSE300628 HER2 heterogeneous breast cancer models reveal novel therapeutic targets and subclonal dynamics during evolution to resistance to HER2-targeted therapies 60 samples shares evolution, clonal
- GSE296649 Clonal evolution permits outgrowth of chemoresistant LMPP-like early progenitors in relapsed NPM1c AML [scRNA-Seq] 58 samples shares evolution, clonal
- GSE314812 Divergent clonal evolution and tumor microenvironment remodeling shape gastric cancer peritoneal metastasis 55 samples shares evolution, clonal
- GSE318970 Metabolic Dependencies in Medulloblastoma Reflect Underlying Cellular Origins 33 samples shares cellular, origin
- GSE320212 Identifying Senescence and Immune Biomarkers Predictive of Benefit to Combined CDK4/6 and PD-1 Blockade in Dedifferentiated Liposarcoma: Single-Cell RNA-seq of Serial Tumor Biopsies 23 samples shares dedifferentiated, liposarcoma
- GSE296381 Clonal evolution permits outgrowth of chemoresistant LMPP-like early progenitors in relapsed NPM1c AML 20 samples shares evolution, clonal
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.