Transcriptome analyses of human corneal endothelial cell lines derived from patients with Fuchs Endothelial Corneal Dystrophy
RNA-seq (gene expression) · human · 11 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
- 11 samples: 11 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
- Transcriptome analyses of human corneal endothelial cell lines derived from patients with Fuchs endothelial corneal dystrophyScientific reports · 2025
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 GSE285203_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 PRJNA1201780 and SRA study SRP553252. 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 11 sample titles: 11 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.
- GSM8698095 SVN1-67F (OSa1-1)
- GSM8698096 SVN2-67F (OSa1-2)
- GSM8698097 SVF1-73F (OSa1-4)
- GSM8698098 SVF5-54F (OSa1-5)
- GSM8698099 SVF6-61M (OSa1-6)
- GSM8698100 SVN1-67F (OSa2-1)
- GSM8698101 SVN3-65M (OSa2-2)
- GSM8698102 SVF2-61F (OSa2-3)
- GSM8698103 SVF3-76M (OSa2-4)
- GSM8698104 SVF4-61F (OSa2-5)
- GSM8698105 SVF7-74F (OSa2-6)
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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.