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Differential Induction of Interferon-Stimulated Genes by Cell-Based Versus Egg-Based Quadrivalent Influenza Vaccines in Children During the 2018-2019 Season

GSE274606 Homo sapiens (human) RNA-seq (gene expression) 103 samples Submitted 2024/08/14 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 103 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
103 samples: 55 × PMBC_Day0 vs 48 × PMBC_Day7Groups 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.
Summary (written by the submitters)
Background: Cell-based quadrivalent-inactivated influenza vaccine has been shown to have higher vaccine effectiveness than traditional egg-based quadrivalent-inactivated influenza vaccine. This is observed despite similar levels of serum hemagglutinin antibodies induced by each vaccine. Results: Cell-based influenza vaccine induced greater interferon-stimulated and innate immune gene activation compared with egg-based influenza vaccine.
Read the full summary ↓
Participants who seroconverted had increased interferon-signaling activation versus those who did not seroconvert. Conclusions: These data suggest that cell-based influenza vaccine stimulates immune activation differently from egg-based influenza vaccine, shedding light on reported differences in vaccine effectiveness.
This dataset
Open GSE274606 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE274606_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1147275 and SRA study SRP525950. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
55 × PMBC_Day0 vs 48 × PMBC_Day7

Supports a between-group comparison across 103 samples.

How we read this: 2 replicated groups read from 103 sample titles; they account for 103 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.

Samples in this study

+ 63 more — browse all 103 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.