BAFF-R Expression as a Predictive Biomarker for COVID-19 Vaccine Non-Responsiveness in Antibody-Deficient Patients
RNA-seq (gene expression) · human · 12 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
- 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
- BAFF-R expression as a potential biomarker associated with COVID-19 vaccine non-responsiveness in antibody-deficient patientsClinical and experimental immunology · 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 GSE298709_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 PRJNA1270988 and SRA study SRP589922. 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.
- GSM9021300 Blood, Control1, HighBAFFR, HC1
- GSM9021301 Blood, Control2, HighBAFFR, HC2
- GSM9021302 Blood, CVID Responder1, HighBAFFR, P5
- GSM9021303 Blood, CVID Responder2, HighBAFFR, P6
- GSM9021304 Blood, CVID Non Responder1, LowBAFFR, P10
- GSM9021305 Blood, SAD Responder1, HighBAFFR, P12
- GSM9021306 Blood, Hypogammaglobinaemia Responder1, HighBAFFR, P13
- GSM9021307 Blood, CVID Responder3, LowBAFFR, P14
- GSM9021308 Blood, SAD Responder2, LowBAFFR, P15
- GSM9021309 Blood, CVID Non Responder2, LowBAFFR, P16
- GSM9021310 Blood, SAD Responder3, LowBAFFR, P17
- GSM9021311 Blood, CVID Responder4, HighBAFFR, P18
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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.