Single-cell RNA sequencing of bone marrow aspirate samples from multiple myeloma and its precursor conditions
Single-cell RNA-seq · human · 19 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
- 19 samples: 6 × MGUS vs 5 × Healthy dono vs 4 × SMM vs 4 × Newly diagnosed multiple myelomaGroups 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 GSE271107_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 PRJNA1129864 and SRA study SRP517019. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 19 samples.
How we read this: 4 replicated groups read from 19 sample titles; they account for 19 of them. 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.
- GSM8369863 Healthy donor_1 Healthy dono
- GSM8369864 Healthy donor_2 Healthy dono
- GSM8369865 Healthy donor_3 Healthy dono
- GSM8369866 Healthy donor_4 Healthy dono
- GSM8369867 Healthy donor_5 Healthy dono
- GSM8369868 MGUS_1 MGUS
- GSM8369869 MGUS_2 MGUS
- GSM8369870 MGUS_3 MGUS
- GSM8369871 MGUS_4 MGUS
- GSM8369872 MGUS_5 MGUS
- GSM8369873 MGUS_6 MGUS
- GSM8369874 SMM_1 SMM
- GSM8369875 SMM_2 SMM
- GSM8369876 SMM_3 SMM
- GSM8369877 SMM_4 SMM
- GSM8369878 Newly diagnosed multiple myeloma _1 Newly diagnosed multipl…
- GSM8369879 Newly diagnosed multiple myeloma _2 Newly diagnosed multipl…
- GSM8369880 Newly diagnosed multiple myeloma _3 Newly diagnosed multipl…
- GSM8369881 Newly diagnosed multiple myeloma _4 Newly diagnosed multipl…
- 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.