Transcriptomic and proteomic multi-model global gene expression analysis reveals regulatory signatures during ischemia stroke (photothrombotic cerebral ischemia stroke)
RNA-seq (gene expression) · mouse · 18 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
- Mus musculus (mouse)
- Samples
- 18 samples: 18 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.
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 GSE133897_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 PRJNA552910 and SRA study SRP213256. 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 18 sample titles: 18 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.
- GSM3929411 ctr.1L
- GSM3929412 ctr.3L
- GSM3929413 ctr.5L
- GSM3929414 ctr.1R
- GSM3929415 ctr.2R
- GSM3929416 ctr.3R
- GSM3929417 hour4.2R
- GSM3929418 hour4.3R
- GSM3929419 hour4.6R
- GSM3929420 day1.1R
- GSM3929421 day1.3R
- GSM3929422 day1.4R
- GSM3929423 day3.1R
- GSM3929424 day3.2R
- GSM3929425 day3.8R
- GSM3929426 day10.1R
- GSM3929427 day10.2R
- GSM3929428 day10.5R
- GSE255212 Transcriptomic Signatures of WNT-Driven Pathways and Granulosa Cell-Oocyte Interaction during Primordial Follicle Activation 762 samples shares signatures
- GSE292261 A dynamic gene regulatory code drives synaptic development of hippocampal granule cells [SS2] 576 samples shares regulatory
- GSE290574 Gene Regulatory Networks Orchestrating Oocyte Fate Bifurcation in Primordial Follicles Revealed by Single-Cell Transcriptomics 386 samples shares regulatory
- GSE228457 IL-7 mediated immunoregulatory feedback loop promotes dampening of immune effector functions by hypoinflammatory monocytes. 305 samples shares regulatory
- GSE291420 Deciphering the Transcriptomic Signatures of Aging Across Organs in Mice 270 samples shares signatures
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares regulatory
- GSE239946 Signatures of IL1b, IL6 and TNFa in immune cell-types 209 samples shares signatures
- GSE300534 Brain Mecp2 Genetic Dosage and Gene Therapy Shape Multi-omics Signatures and Biomarkers in Rett Syndrome [1] 137 samples shares signatures
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.