Chlamydia serovars in uterus- scRNA-seq, CITE-seq and TCRab
Single-cell RNA-seq · mouse · 30 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 30 samples: 30 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.
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 GSE337460_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1489237 and SRA study SRP714843. 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 30 sample titles: 30 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.
- GSM9855806 I33H1_spleen_FBC
- GSM9855807 I33H1_spleen_GEX
- GSM9855808 I33H1_spleen_TCR
- GSM9855809 I33H10_uterus_CtrachomatisL2_secondaryD5_FBC
- GSM9855810 I33H10_uterus_CtrachomatisL2_secondaryD5_GEX
- GSM9855811 I33H10_uterus_CtrachomatisL2_secondaryD5_TCR
- GSM9855812 I33H2_uterus_FBC
- GSM9855813 I33H2_uterus_GEX
- GSM9855814 I33H2_uterus_TCR
- GSM9855815 I33H3_uterus_FBC
- GSM9855816 I33H3_uterus_GEX
- GSM9855817 I33H3_uterus_TCR
- GSM9855818 I33H4_uterus_CtrachomatisD_primaryD5_FBC
- GSM9855819 I33H4_uterus_CtrachomatisD_primaryD5_GEX
- GSM9855820 I33H4_uterus_CtrachomatisD_primaryD5_TCR
- GSM9855821 I33H5_uterus_CtrachomatisD_primaryD15_FBC
- GSM9855822 I33H5_uterus_CtrachomatisD_primaryD15_GEX
- GSM9855823 I33H5_uterus_CtrachomatisD_primaryD15_TCR
- GSM9855824 I33H6_uterus_CtrachomatisD_secondaryD3_FBC
- GSM9855825 I33H6_uterus_CtrachomatisD_secondaryD3_GEX
- GSM9855826 I33H6_uterus_CtrachomatisD_secondaryD3_TCR
- GSM9855827 I33H7_uterus_CtrachomatisD_secondaryD5_FBC
- GSM9855828 I33H7_uterus_CtrachomatisD_secondaryD5_GEX
- GSM9855829 I33H7_uterus_CtrachomatisD_secondaryD5_TCR
- GSM9855830 I33H8_uterus_CtrachomatisL2_primaryD5_FBC
- GSM9855831 I33H8_uterus_CtrachomatisL2_primaryD5_GEX
- GSM9855832 I33H8_uterus_CtrachomatisL2_primaryD5_TCR
- GSM9855833 I33H9_uterus_CtrachomatisL2_primaryD15_FBC
- GSM9855834 I33H9_uterus_CtrachomatisL2_primaryD15_GEX
- GSM9855835 I33H9_uterus_CtrachomatisL2_primaryD15_TCR
- GSE330911 Spleen and brain T cells in Toxoplasma gondii infection- scRNA-seq, CITE-seq and TCRab 30 samples shares scrna-seq, cite-seq, tcrab
- GSE330985 Positive and Negative Selection of abT cells (OT-I and OT-II) in the thymus 2 - scRNA-seq, CITE-seq and TCRab 21 samples shares scrna-seq, cite-seq, tcrab
- GSE311344 T cells in B16 tumor model with ACT and checkpoint blockade - scRNA-seq, CITE-seq and TCRab [i35] 15 samples shares scrna-seq, cite-seq, tcrab
- GSE274125 Timestamp tracing delineates thymic regulatory T cell ontogeny in native conditions [scRNA-seq] 20 samples shares scrna-seq
- GSE306035 scRNA-Seq of immune populations from nasal mucosa and lungs of C57BL/6 mice and BLT1 receptor deficient mice infected intranasally with MCMV K181 strain 20 samples shares scrna-seq
- GSE241770 ACSS2 mediates an epigenetic pathway to regulate maternal Beta-cell adaptation and progression of gestational diabetes (scRNA-Seq) 7899 samples shares scrna-seq
- GSE220685 CITE-Seq analysis of FACS-sorted live human CD45+ cells isolated from the ears of huNOG-EXL mice in a model of atopic dermatitis 12 samples shares cite-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-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.