Flow induced-endothelial phenotypic transition contributes to Intracranial Aneurysm
Single-cell RNA-seq · mouse · 9 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 9 samples: 3 × PSS vs 3 × HSS vs 3 × VortexGroups 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 GSE329653_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 PRJNA1460422 and SRA study SRP696390. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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.
- GSM9708343 PSS-1 PSS
- GSM9708344 PSS-2 PSS
- GSM9708345 PSS-3 PSS
- GSM9708346 HSS-1 HSS
- GSM9708347 HSS-2 HSS
- GSM9708348 HSS-3 HSS
- GSM9708349 Vortex-1 Vortex
- GSM9708350 Vortex-2 Vortex
- GSM9708351 Vortex-3 Vortex
- GSE299090 H4K16ac contributes to chromatin compartment reorganization during mitotic and meiotic transitions [RNA-seq] 21 samples shares contributes, transition
- GSE269166 Endothelial-to-mesenchymal transition contributes to accelerated atherosclerosis in Hutchinson–Gilford progeria syndrome 14 samples shares contributes, transition
- GSE209836 Cholestasis-induced neutrophil phenotypic transformation contributes to the immune escape of colorectal liver metastasis 12 samples shares contributes, phenotypic
- GSE304211 H4K16ac contributes to chromatin compartment reorganization during mitotic and meiotic transitions [CUTnTAG] 128 samples shares contributes, transition
- GSE229130 VSMC phenotypic transition 14 samples shares phenotypic, transition
- GSE294832 Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice [part10] 1000 samples shares phenotypic
- GSE294911 Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice [part14] 1000 samples shares phenotypic
- GSE294535 Modular neuroinflammatory network discovery from large-scale phenotypic screening in genetically heterogeneous mice[part5] 1000 samples shares phenotypic
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.