Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma
Single-cell RNA-seq · human · 2105 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)
- Topic
- Multiple myelomaThis series is part of our curated briefing on it.
- Samples
- 2105 samples
- 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 GSE305971_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 PRJNA1308544 and SRA study SRP610185. Searching any of these in the dataset finder brings you back here.
The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Sample titles run AB10577 … AB14522 and carry no group information.
- GSM9188740 AB10577
- GSM9188741 AB10578
- GSM9188742 AB10579
- GSM9188743 AB10580
- GSM9188744 AB10581
- GSM9188745 AB10582
- GSM9188746 AB10583
- GSM9188747 AB10584
- GSM9188748 AB10585
- GSM9188749 AB10586
- GSM9188750 AB10587
- GSM9188751 AB10588
- GSM9188752 AB10589
- GSM9188753 AB10590
- GSM9188754 AB10591
- GSM9188755 AB10592
- GSM9188756 AB10593
- GSM9188757 AB10594
- GSM9188758 AB10595
- GSM9188759 AB10596
- GSM9188760 AB10597
- GSM9188761 AB10598
- GSM9188762 AB10599
- GSM9188763 AB10600
- GSM9188764 AB10601
- GSM9188765 AB10602
- GSM9188766 AB10603
- GSM9188767 AB10604
- GSM9188768 AB10605
- GSM9188769 AB10606
- GSM9188770 AB10607
- GSM9188771 AB10608
- GSM9188772 AB10609
- GSM9188773 AB10610
- GSM9188774 AB10611
- GSM9188775 AB10612
- GSM9188776 AB10613
- GSM9188777 AB10614
- GSM9188778 AB10615
- GSM9188779 AB10616
+ 2065 more — browse all 2105 samples with per-sample file links →
- GSE161195 Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments 213 samples same disease briefing · shares multiple, myeloma
- GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
- GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma
- GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma
- GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma
- GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
- GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma
- GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma
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.