Single-cell RNA-seq profiling of AML patient-derived xenografts across serial transplantation
Single-cell RNA-seq · human · 14 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
- 14 samples: 14 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.
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 GSE329946_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 PRJNA1462256 and SRA study SRP697634. 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 14 sample titles: 14 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.
- GSM9713505 TUH06 P0 scRNAseq
- GSM9713506 TUH06 P1 scRNAseq
- GSM9713507 TUH06 P2 scRNAseq
- GSM9713508 TUH07 P0 scRNAseq
- GSM9713509 TUH32 P0 scRNAseq
- GSM9713510 TUH76 P0 scRNAseq
- GSM9713516 TUH76 P2 scRNAseq
- GSM9713531 TUH84 P0 scRNAseq
- GSM9713546 TUH84 P1 scRNAseq
- GSM9713561 TUH84 P2 scRNAseq
- GSM9713576 TUH102 P0 scRNAseq
- GSM9713582 TUH102 P1 scRNAseq
- GSM9713583 TUH102 P2 scRNAseq
- GSM9713584 TUH109 P0 scRNAseq
- GSE318490 Single-cell RNA-seq of tumor samples from six advanced HGSOC patients across treatment stages (pre-NACT, post-NACT and platinum-sensitive recurrence) 10 samples shares single-cell, rna-seq, across
- GSE327386 RNA-Seq of paired diagnosis and relapse AML specimens with healthy controls 130 samples shares aml, rna-seq
- GSE327167 Single-cell RNA-seq profiling of pleural mesothelioma and comparator pleural specimens 113 samples shares single-cell, rna-seq
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares single-cell, rna-seq
- GSE227835 Single-cell RNA-seq data of human PBMC from Myasthenia Gravis patients 40 samples shares single-cell, rna-seq
- 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, 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, rna-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, rna-seq
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.