Altered gene expression in multiple key pathways in MSCs from people with osteoporosis compared to people who do not have osteoporosis
RNA-seq (gene expression) · human · 14 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- 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.
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 GSE189524_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA783324. 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.
- GSM5703090 FO1 O
- GSM5703091 FO2 O
- GSM5703092 FO3 O
- GSM5703093 FO1D O
- GSM5703094 FO2D O
- GSM5703095 FO3D O
- GSM5703096 CF1 NO
- GSM5703097 CF2 NO
- GSM5703098 CF3 NO
- GSM5703099 CF4 NO
- GSM5703100 CF1D NO
- GSM5703101 CF2D NO
- GSM5703102 CF3D NO
- GSM5703103 CF4D NO
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- GSE305970 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 4 of 4: Illumina NovaSeq X; MARS_v2] 273 samples shares multiple
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares multiple
- GSE305968 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma [Part 2 of 4: Illumina NextSeq 500; MARS] 267 samples shares multiple
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.