Single-Cell Omics Analysis of Human Basophils Reveals Two Transcriptionally Distinct Populations
Single-cell RNA-seq · human · 12 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 12 samples: 12 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
- Single-Cell Omics Analysis of Human Basophils Reveals Two Transcriptionally Distinct PopulationsAllergy · 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 GSE287976_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 PRJNA1215395. 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 12 sample titles: 12 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.
- GSM8756665 Donor A pbmcs Gene Expression
- GSM8756666 Donor A pbmcs Surface Protein
- GSM8756667 Donor B sorted basophils Gene Expression
- GSM8756668 Donor B sorted basophils Surface Protein
- GSM8756669 Donor C MACS basophils Gene Expression
- GSM8756670 Donor C MACS basophils_Surface_Protein
- GSM8756671 Donor D1 MACS basophils_Gene_Expression
- GSM8756672 Donor D1 MACS basophils_Surface_Protein
- GSM8756673 Donor D2 MACS basophils_Gene_Expression
- GSM8756674 Donor_D2_MACS_basophils_Surface_Protein
- GSM8756675 DonorC_MACS_basophils_Gene_Expression_Long_read
- GSM8756676 DonorD1_MACS_basophils_Gene_Expression_Long_read
- GSE278694 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC 88 samples shares single-cell, distinct
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, distinct
- GSE332630 Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma 22 samples shares single-cell, distinct
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares single-cell
- GSE332623 Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics 54 samples shares single-cell
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE253661 Longitudinal single-cell transcriptomics of monocytes, Tfh and gd Tcells following a control human malaria infection. 16 samples shares single-cell
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell
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.