Inference of secreted protein signaling activities in intercellular communication
Single-cell RNA-seq · mouse · 10 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 10 samples: 2 × Ly86_1 vs 2 × Vector_1 vs 2 × Vector_2Groups 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 GSE291028_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 PRJNA1231421 and SRA study SRP567916. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 6 samples.
How we read this: 3 replicated groups read from 10 sample titles; they account for 6 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.
- GSM8827787 Ly86_1_1 Ly86_1
- GSM8827788 Ly86_1_2 Ly86_1
- GSM8827789 Ly86_2_1
- GSM8827790 Ly86_3_1
- GSM8827791 Ly86_4
- GSM8827792 Vector_1_1 Vector_1
- GSM8827793 Vector_1_2 Vector_1
- GSM8827794 Vector_2_1 Vector_2
- GSM8827795 Vector_2_2 Vector_2
- GSM8827796 Vector_3_1
- GSE324005 Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal 12 samples shares communication, inference
- GSE291091 MSC-dependent intercellular communication: an interactome for skeletal muscle homeostasis and regeneration 8 samples shares intercellular, communication
- GSE235047 Single Cell Transcriptomic Analysis in a Mouse Model of Barth Syndrome Reveals Cell-Specific Alterations in Gene Expression and Intercellular Communication 8 samples shares intercellular, communication
- GSE285364 Intercellular communication between hepatic stellate cells and myofibroblasts mediated by osteopontin and FGF18 3 samples shares intercellular, communication
- GSE249259 Cell-in-cell mediated intercellular communication exacerbates the pro-inflammatory progression in asthma 2 samples shares intercellular, communication
- GSE242731 Class switch recombination regulation by extracellular vesicles mediated intercellular epigenomic signaling 48 samples shares intercellular
- GSE303655 Defective LCN2–FN1 signaling impairs stromal–immune communication and underlies recurrent implantation failure 7 samples shares communication
- GSE274685 Nociceptor to macrophage communication through CGRP/RAMP1 signaling drives endometriosis-associated pain and lesion growth 6 samples shares communication
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.