Tissue-specific non-heritable influence drive uterine immune system variation (RNA-seq)
RNA-seq (gene expression) · human · 48 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
- 48 samples: 48 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
- Tissue-specific nonheritable influences drive endometrial immune system variationScience immunology · 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 GSE225041_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 PRJNA934581 and SRA study SRP422566. 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 48 sample titles: 48 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.
- GSM7037647 1_MZ10_A_MB_NK
- GSM7037648 2_MZ10_A_MB_CD8T
- GSM7037649 3_MZ10_B_MB_NK
- GSM7037650 4_MZ10_B_MB_CD8T
- GSM7037651 5_MZ10_A_PB_NK
- GSM7037652 6_MZ10_A_PB_CD8T
- GSM7037653 7_MZ10_B_PB_NK
- GSM7037654 8_MZ10_B_PB_CD8T
- GSM7037655 9_MZ12_A_MB_NK
- GSM7037656 10_MZ12_A_MB_CD8T
- GSM7037657 11_MZ12_B_MB_NK
- GSM7037658 12_MZ12_B_MB_CD8T
- GSM7037659 13_MZ12_A_PB_NK
- GSM7037660 14_MZ12_A_PB_CD8T
- GSM7037661 15_MZ12_B_PB_NK
- GSM7037662 16_MZ12_B_PB_CD8T
- GSM7037663 17_MZ05_A_MB_NK
- GSM7037664 18_MZ05_A_MB_CD8T
- GSM7037665 19_MZ05_B_MB_NK
- GSM7037666 20_MZ05_B_MB_CD8T
- GSM7037667 21_MZ05_A_PB_NK
- GSM7037668 22_MZ05_A_PB_CD8T
- GSM7037669 23_MZ05_B_PB_NK
- GSM7037670 24_MZ05_B_PB_CD8T
- GSM7037671 25_ENDO5_t1_MB_NK
- GSM7037672 26_ENDO5_t1_MB_CD8T
- GSM7037673 29_ENDO5_t2_MB_NK
- GSM7037674 30_ENDO5_t2_MB_CD8T
- GSM7037675 31_ENDO5_t1_PB_NK
- GSM7037676 32_ENDO5_t1_PB_CD8T
- GSM7037677 35_ENDO5_t2_PB_NK
- GSM7037678 36_ENDO5_t2_PB_CD8T
- GSM7037679 37_MB056_t1_MB_NK
- GSM7037680 38_MB056_t1_MB_CD8T
- GSM7037681 39_MB056_t2_MB_NK
- GSM7037682 40_MB056_t2_MB_CD8T
- GSM7037683 41_MB056_t1_PB_NK
- GSM7037684 42_MB056_t1_PB_CD8T
- GSM7037685 43_MB056_t2_PB_NK
- GSM7037686 44_MB056_t2_PB_CD8T
+ 8 more — browse all 48 samples with per-sample file links →
- GSE225042 Tissue-specific non-heritable influence drive uterine immune system variation 96 samples shares tissue-specific, non-heritable, influence, variation, uterine, system, drive
- GSE225040 Tissue-specific non-heritable influence drive uterine immune system variation (ATAC-seq) 48 samples shares tissue-specific, non-heritable, influence, variation, uterine, system, drive
- GSE235999 Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases [RNA-seq] 12 samples shares variation, rna-seq, drive
- GSE208732 Mapping functional to morphological variation reveals the basis of regional extracellular matrix subversion and nerve invasion in pancreatic cancer [RNA-seq] 102 samples shares variation, rna-seq
- GSE324430 Promoter mutagenesis and a massively parallel reporter screen of the MAPT locus identifies cis-regulatory elements and genetic variation effects [RNA-seq] 88 samples shares variation, rna-seq
- GSE273325 Application of RNA-seq for single nucleotide variation identification patients with hereditary diseases: analysis of trasncriptomic data and perspective use 54 samples shares variation, rna-seq
- GSE294057 Unveiling Tissue-Specific Transcriptional Adaptations in iPSC-Derived Fibroblasts via Co-Culture Systems 27 samples shares tissue-specific, system
- GSE164053 Mapping origins of variation in neural trajectories of human pluripotent stem cells [SOX21 KO RNA-seq] 18 samples shares variation, rna-seq
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.