Differentiation time course for Mouse Epiblast Stem Cells
RNA-seq (gene expression) · mouse · 144 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
- Mus musculus (mouse)
- Samples
- 144 samples: 39 conditions, mostly in duplicateGroups 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.
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 GSE199114_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 PRJNA818527. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 142 samples.
How we read this: 39 replicated groups read from 144 sample titles; they account for 142 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.
- GSM5964535 EpiSC-5_WA_12H-1 EpiSC-5_WA_12H
- GSM5964536 EpiSC-5_WA_12H-2 EpiSC-5_WA_12H
- GSM5964537 EpiSC-5_WA_18H-1 EpiSC-5_WA_18H
- GSM5964538 EpiSC-5_WA_18H-2 EpiSC-5_WA_18H
- GSM5964539 EpiSC-5_WA_24H-1 EpiSC-5_WA_24H
- GSM5964540 EpiSC-5_WA_24H-2 EpiSC-5_WA_24H
- GSM5964541 EpiSC-5_WA_24H-3 EpiSC-5_WA_24H
- GSM5964542 EpiSC-5_WA_24H-4 EpiSC-5_WA_24H
- GSM5964543 EpiSC-5_WA_24H-5 EpiSC-5_WA_24H
- GSM5964544 EpiSC-5_WA_24H-6 EpiSC-5_WA_24H
- GSM5964545 EpiSC-5_WA_36H-1 EpiSC-5_WA_36H
- GSM5964546 EpiSC-5_WA_36H-2 EpiSC-5_WA_36H
- GSM5964547 EpiSC-5_WA_3H-1 EpiSC-5_WA_3H
- GSM5964548 EpiSC-5_WA_3H-2 EpiSC-5_WA_3H
- GSM5964549 EpiSC-5_WA_48H-1
- GSM5964550 EpiSC-5_WA_6H-1 EpiSC-5_WA_6H
- GSM5964551 EpiSC-5_WA_6H-2 EpiSC-5_WA_6H
- GSM5964552 EpiSC-5_WA_6H-3 EpiSC-5_WA_6H
- GSM5964553 EpiSC-5_WA_6H-4 EpiSC-5_WA_6H
- GSM5964554 EpiSC-5_WA_6H-5 EpiSC-5_WA_6H
- GSM5964555 EpiSC-5_WA_6H-6 EpiSC-5_WA_6H
- GSM5964556 EpiSC-5_WA_72H-1 EpiSC-5_WA_72H
- GSM5964557 EpiSC-5_WA_72H-2 EpiSC-5_WA_72H
- GSM5964558 EpiSC-5_WA_72H-3 EpiSC-5_WA_72H
- GSM5964559 EpiSC-5_WA_72H-4 EpiSC-5_WA_72H
- GSM5964560 EpiSC-5_WA_72H-5 EpiSC-5_WA_72H
- GSM5964561 EpiSC-5_WA_72H-6 EpiSC-5_WA_72H
- GSM5964562 EpiSC-5_WAFB_12H-1 EpiSC-5_WAFB_12H
- GSM5964563 EpiSC-5_WAFB_12H-2 EpiSC-5_WAFB_12H
- GSM5964564 EpiSC-5_WAFB_18H-1 EpiSC-5_WAFB_18H
- GSM5964565 EpiSC-5_WAFB_18H-2 EpiSC-5_WAFB_18H
- GSM5964566 EpiSC-5_WAFB_24H-1 EpiSC-5_WAFB_24H
- GSM5964567 EpiSC-5_WAFB_24H-2 EpiSC-5_WAFB_24H
- GSM5964568 EpiSC-5_WAFB_24H-3 EpiSC-5_WAFB_24H
- GSM5964569 EpiSC-5_WAFB_24H-4 EpiSC-5_WAFB_24H
- GSM5964570 EpiSC-5_WAFB_24H-5 EpiSC-5_WAFB_24H
- GSM5964571 EpiSC-5_WAFB_24H-6 EpiSC-5_WAFB_24H
- GSM5964572 EpiSC-5_WAFB_36H-1 EpiSC-5_WAFB_36H
- GSM5964573 EpiSC-5_WAFB_36H-2 EpiSC-5_WAFB_36H
- GSM5964574 EpiSC-5_WAFB_3H-1 EpiSC-5_WAFB_3H
+ 104 more — browse all 144 samples with per-sample file links →
- GSE319684 Timecourse of FACS sorted Ly6C+ monocytes, tissue resident alveolar macrophages (trAMs), monocyte derived alveolar macrophages (bmAMs) and epithelial cells from SARS-CoV-2 (maVie16) vs. IAV (PR8) infected mouse lungs 176 samples shares course, time
- GSE267650 Mouse kidney ischemia-reperfusion injury time course 57 samples shares course, time
- GSE319035 P21+TREM2+-Senescent Macrophages Fuel Inflammaging and Metabolic Dysfunction-Associated Steatotic Liver Disease [NAFLD_time_course] 77 samples shares course, time
- GSE254162 Dynamic fibroblast-immune interactions shape recovery after brain injury [snRNASeq_Timecourse] 12 samples shares course, time
- GSE309993 Effector differentiation by stem-like intraepithelial γδ T cells is required for host defense against infection 25 samples shares stem
- GSE314817 Multi-tissue, multi-time-point transcriptomic atlas of aging in mice and rats [mouse] 2432 samples shares time
- GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares stem
- GSE327304 Transcriptomic characterization of Wild type mouse Embryonic Stem Cells (mESCs), β-actin Knock out mESC and upon rescue with a nuclear localization signal β-actin 45 samples shares stem
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.