Blood transcriptional response to treatment-resistant depression during electroconvulsive therapy
RNA-seq (gene expression) · human · 35 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
- 35 samples: 24 distinct titles, 1 of them repeatedGroups 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.
- Paper
- Blood transcriptional response to treatment-resistant depression during electroconvulsive therapyJournal of psychiatric research · 2021
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 GSE234669_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 PRJNA982735 and SRA study SRP442878. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 35 sample titles: 24 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.
- GSM7473078 PBMC RNA, control 1
- GSM7473079 PBMC RNA, control 2
- GSM7473080 PBMC RNA, control 3
- GSM7473081 PBMC RNA, control 4
- GSM7473082 PBMC RNA, control 5
- GSM7473083 PBMC RNA, control 6
- GSM7473084 PBMC RNA, control 7
- GSM7473085 PBMC RNA, control 8
- GSM7473086 PBMC RNA, control 9
- GSM7473087 PBMC RNA, control 10
- GSM7473088 PBMC RNA, control 11
- GSM7473089 PBMC RNA, control 12
- GSM7473090 PBMC RNA, ecf 1, 1st visit
- GSM7473091 PBMC RNA, ecf 2, 1st visit
- GSM7473092 PBMC RNA, ecf 3, 1st visit
- GSM7473093 PBMC RNA, ecf 4, 1st visit
- GSM7473094 PBMC RNA, ecf 1, 2nd visit
- GSM7473095 PBMC RNA, ecf 2, 2nd visit
- GSM7473096 PBMC RNA, ecf 3, 2nd visit
- GSM7473097 PBMC RNA, ecf 4, 2nd visit
- GSM7473098 PBMC RNA, ecf 5, 2nd visit
- GSM7473099 PBMC RNA, kat 1, 1st visit
- GSM7473100 PBMC RNA, kat 2, 1st visit
- GSM7473101 PBMC RNA, kat 3, 1st visit
- GSM7473102 PBMC RNA, kat 4, 1st visit
- GSM7473103 PBMC RNA, kat 1, 2nd visit
- GSM7473104 PBMC RNA, kat 2, 2nd visit
- GSM7473105 PBMC RNA, kat 3, 2nd visit
- GSM7473106 PBMC RNA, kat 4, 2nd visit
- GSM7473107 PBMC RNA, sri 1, 1st visit
- GSM7473108 PBMC RNA, sri 2, 1st visit
- GSM7473109 PBMC RNA, sri 1, 2nd visit
- GSM7473110 PBMC RNA, sri 2, 2nd visit
- GSM7473111 PBMC RNA, sri 3, 2nd visit
- GSM7473112 PBMC RNA, sri 4, 2nd visit
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- GSE309430 Blood-Based Gene Signatures Associated with Therapeutic Response to Anti-TNF Therapy in Rheumatoid Arthritis: A Combined Meta-Analytical and Machine Learning Approach 8 samples shares therapy, blood
- GSE300475 Single cell RNA sequencing for longitudinal human peripheral blood from HR+ breast cancer patients treated with immunotherapy 32 samples shares therapy, blood
- GSE235090 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue] 192 samples shares therapy
- GSE330098 Multi-tissue microphysiological system captures integrated responses to nutritional overload and metabolic therapy 108 samples shares therapy
- GSE254249 Remodeling of T and endothelial cells during total neoadjuvant therapy in rectal cancer 92 samples shares therapy
- GSE256141 Single cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells 90 samples shares treatment-resistant
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares therapy
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.