Low- and high-grade glioma-associated vascular cells differentially regulate tumor growth [project1]
RNA-seq (gene expression) · human · 15 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
- 15 samples: 15 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.
- Paper
- Low- and High-Grade Glioma-Associated Vascular Cells Differentially Regulate Tumor GrowthMolecular cancer research : MCR · 2024
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 GSE236568_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 PRJNA991581 and SRA study SRP447483. 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 15 sample titles: 15 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.
- GSM7554418 REC_GVC3 [3778TEC]
- GSM7554420 REC_GVC2 [3768TEC]
- GSM7554422 REC_GVC4 [3811TEC]
- GSM7554423 REC_GVC5 [3812TEC]
- GSM7554424 REC_GVC1 [3499TEC]
- GSM7554425 PRI_GVC4 [3815TEC]
- GSM7554426 PRI_GVC3 [3810TEC]
- GSM7554427 PRI_GVC2 [3383TEC]
- GSM7554428 PRI_GVC1 [3706TEC]
- GSM7554429 LGG_GVC1 [3707TEC]
- GSM7554431 LGG_GVC2 [3744TEC]
- GSM7554433 LGG_GVC4 [3761TEC]
- GSM7554434 LGG_GVC5 [3788TEC]
- GSM7554435 LGG_GVC3 [3746TEC]
- GSM7554436 HBEC [HBEC_S45]
- GSE236570 Low- and high-grade glioma-associated vascular cells differentially regulate tumor growth [project3] 12 samples shares glioma-associated, differentially, high-grade, vascular, regulate, growth
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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.