Spatially resolved multi-omic profiling of human hippocampus reveals region-specific alterations in major depressive disorder [RNA-seq]
RNA-seq (gene expression) · human · 13 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
- 13 samples: 13 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 GSE214138_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 PRJNA884108 and SRA study SRP399514. 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 13 sample titles: 13 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.
- GSM6600043 CTRL_1A
- GSM6600044 CTRL_2A
- GSM6600045 CTRL_3A
- GSM6600046 CTRL_4A
- GSM6600047 CTRL_4B
- GSM6600048 CTRL_5A
- GSM6600049 CTRL_6A
- GSM6600050 MDD_1A
- GSM6600051 MDD_3A
- GSM6600052 MDD_3B
- GSM6600053 MDD_4A
- GSM6600054 MDD_4B
- GSM6600055 MDD_5B
- GSE325054 Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA Vaccination [RNA-Seq] 187 samples shares multi-omic, resolved
- GSE272820 Multi-omics datasets for finer-scale dissection of chromatin architecture alterations in radiation induced malignant transformation of bronchial epithelial cells [Micro-C] 12 samples shares alterations, multi-omic
- GSE319130 Multi-omic analysis reveals molecular subtypes with distinct immunological phenotypes in SLE [RNA-Seq] 804 samples shares multi-omic
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares multi-omic
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omic
- GSE282511 Multi-omics analysis in primary T cells elucidates mechanisms behind disease associated genetic loci [RNA-seq] 128 samples shares multi-omic
- GSE269335 Single-cell multimodal profiling of monocytes reveals diverse phenotypes and alterations linked to cardiovascular disease risks 118 samples shares alterations
- GSE289256 Multi-omic landscape of squamous cell lung cancer 116 samples shares multi-omic
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.