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Global transcriptional profiles of hiPSC-derived hypothalamus-pituitary organoids predicted to fail or succeed by machine learning model

GSE301692 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/07/03 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 4 × Pituitary_organoid_day9_fail vs 4 × Pituitary_organoid_day9_success vs 4 × Pituitary_organoid_day15_fail vs 4 × Pituitary_organoid_day15_successGroups 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
Prediction of the hypothalamus-pituitary organoid formation using machine learningCell reports methods · 2025
Summary (written by the submitters)
We have established a machine learning-based model capable of predicting the successful hypothalamus-pituitary organoid formation from human iPS cells, based solely on phase-contrast images captured at the early timing of differentiation.
Published in
Prediction of the hypothalamus-pituitary organoid formation using machine learning
Matsumoto R, Suga H, Takahashi Y et al. · Cell reports methods 2025 · PMID 40763741 · doi:10.1016/j.crmeth.2025.101119
This dataset
Open GSE301692 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE301692_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1285990 and SRA study SRP598188. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40763741) ↗
Study design
4 × Pituitary_organoid_day9_fail vs 4 × Pituitary_organoid_day9_success vs 4 × Pituitary_organoid_day15_fail vs 4 × Pituitary_organoid_day15_success

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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.

Samples in this study
Datasets on a similar topic

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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.