Bulk RNA-seq time-course of human iPSc differentiation to thymic epithelial progenitors
RNA-seq (gene expression) · human · 33 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
- 33 samples: 7 conditions, mostly with about 4 replicates eachGroups 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
- Combinatory differentiation of human induced pluripotent stem cells generates functional thymic epithelium driving dendritic cell and CD4/CD8 T cell developmentNature communications · 2026
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 GSE304598_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 PRJNA1301874 and SRA study SRP606674. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 28 samples.
How we read this: 7 replicated groups read from 33 sample titles; they account for 28 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.
- GSM9153796 iPSc_rep1 iPSc
- GSM9153797 iPSc_rep2 iPSc
- GSM9153798 iPSc_rep3 iPSc
- GSM9153799 iPSc_to_TEP_J1_rep1
- GSM9153800 iPSc_to_TEP_J2_rep1
- GSM9153801 iPSc_to_TEP_J3_rep1 iPSc_to_TEP_J3
- GSM9153802 iPSc_to_TEP_J3_rep2 iPSc_to_TEP_J3
- GSM9153803 iPSc_to_TEP_J3_rep3 iPSc_to_TEP_J3
- GSM9153804 iPSc_to_TEP_J3_rep4 iPSc_to_TEP_J3
- GSM9153805 iPSc_to_TEP_J7_rep1 iPSc_to_TEP_J7
- GSM9153806 iPSc_to_TEP_J7_rep2 iPSc_to_TEP_J7
- GSM9153807 iPSc_to_TEP_J7_rep3 iPSc_to_TEP_J7
- GSM9153808 iPSc_to_TEP_J7_rep4 iPSc_to_TEP_J7
- GSM9153809 iPSc_to_TEP_J7_rep5 iPSc_to_TEP_J7
- GSM9153810 iPSc_to_TEP_J9_rep1 iPSc_to_TEP_J9
- GSM9153811 iPSc_to_TEP_J9_rep2 iPSc_to_TEP_J9
- GSM9153812 iPSc_to_TEP_J9_rep3 iPSc_to_TEP_J9
- GSM9153813 iPSc_to_TEP_J9_rep4 iPSc_to_TEP_J9
- GSM9153814 iPSc_to_TEP_J9_rep5 iPSc_to_TEP_J9
- GSM9153815 iPSc_to_TEP_J9_rep6 iPSc_to_TEP_J9
- GSM9153816 iPSc_to_TEP_J10_rep1 iPSc_to_TEP_J10
- GSM9153817 iPSc_to_TEP_J10_rep2 iPSc_to_TEP_J10
- GSM9153818 iPSc_to_TEP_J10_rep3 iPSc_to_TEP_J10
- GSM9153819 iPSc_to_TEP_J10_rep4 iPSc_to_TEP_J10
- GSM9153820 iPSc_to_TEP_J14_rep1 iPSc_to_TEP_J14
- GSM9153821 iPSc_to_TEP_J14_rep2 iPSc_to_TEP_J14
- GSM9153822 iPSc_to_TEP_J15_rep1
- GSM9153823 iPSc_to_TEP_J17_rep1
- GSM9153824 iPSc_to_TEP_J22_rep1
- GSM9153825 TEC_rep1 TEC
- GSM9153826 TEC_rep2 TEC
- GSM9153827 TEC_rep3 TEC
- GSM9153828 TEC_rep4 TEC
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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.