Cellular origin and clonal evolution of human dedifferentiated liposarcoma
Single-cell RNA-seq · human · 53 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
- 53 samples: 53 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 GSE221492_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 PRJNA914687 and SRA study SRP414269. 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 53 sample titles: 53 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.
- GSM6876457 Tumor patient 1 (DDLPS_DD), RNAseq
- GSM6876458 Tumor patient 1 (DDLPS_WD), RNAseq
- GSM6876459 Tumor patient 2 (DDLPS_DD), RNAseq
- GSM6876460 Tumor patient 2 (DDLPS_sWD), RNAseq
- GSM6876461 Tumor patient 3 (peritumoral_fat), RNAseq
- GSM6876462 Tumor patient 3 (WDLPS), RNAseq
- GSM6876463 Tumor patient 4 (DDLPS_WD), RNAseq
- GSM6876464 Tumor patient 5 (WDLPS), RNAseq
- GSM6876465 Tumor patient 6 (peritumoral_fat), RNAseq
- GSM6876466 Tumor patient 7 (peritumoral_fat), RNAseq
- GSM6876467 Tumor patient 8 (peritumoral_fat), RNAseq
- GSM6876468 Tumor patient 8 (WDLPS), RNAseq
- GSM6876469 Tumor patient 10 (DDLPS_DD), RNAseq
- GSM6876470 Tumor patient 10 (DDLPS_WD), RNAseq
- GSM6876471 Tumor patient 11 (lipoma), RNAseq
- GSM6876472 Tumor patient 12 (DDLPS_WD), RNAseq
- GSM6876473 Tumor patient 13 (WDLPS), RNAseq
- GSM6876474 Tumor patient 14 (DDLPS_DD), RNAseq
- GSM6876475 Tumor patient 15 (DDLPS_DD), RNAseq
- GSM6876476 Tumor patient 15 (DDLPS_WD), RNAseq
- GSM6876477 Tumor patient 16 (DDLPS_DD), RNAseq
- GSM6876478 Tumor patient 17 (DDLPS_DD), RNAseq
- GSM6876479 Tumor patient 18 (DDLPS_DD), RNAseq
- GSM6876480 Tumor patient 19 (lipoma), RNAseq
- GSM6876481 Tumor patient 20 (DDLPS_DD), RNAseq
- GSM6876482 Tumor patient 20 (DDLPS_WD), RNAseq
- GSM6876483 Tumor patient 21 (DDLPS_DD), RNAseq
- GSM6876484 Tumor patient 21 (DDLPS_WD), RNAseq
- GSM6876485 Tumor patient 22 (Hibernoma), RNAseq
- GSM6876486 Tumor patient 23 (lipoma), RNAseq
- GSM6876487 Tumor patient 24 (lipoma), RNAseq
- GSM6876488 Tumor patient 25 (DDLPS_WD), RNAseq
- GSM6876489 Tumor patient 26 (DDLPS_DD), RNAseq
- GSM6876490 Tumor patient 26 (DDLPS_WD), RNAseq
- GSM6876491 Tumor patient 27 (DDLPS_DD), RNAseq
- GSM6876492 Tumor patient 27 (DDLPS_WD), RNAseq
- GSM6876493 Tumor patient 28 (DDLPS_DD), RNAseq
- GSM6876494 Tumor patient 28 (DDLPS_WD), RNAseq
- GSM6876495 Tumor patient 29 (DDLPS_DD), RNAseq
- GSM6876496 Tumor patient 30 (DDLPS_WD), RNAseq
+ 13 more — browse all 53 samples with per-sample file links →
- 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.