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Optimizing murine sample sizes for RNA-seq studies revealed from large-scale comparative analysis

GSE272152 Mus musculus (mouse) RNA-seq (gene expression) 479 samples Submitted 2025/02/11 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 479 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
Mus musculus (mouse)
Samples
479 samples: 8 conditions, mostly with about 54 replicates eachGroups 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)
In order to determine the N, the usual approach is to perform a power calculation, which involves understanding the variability between samples and the expected effect size. Here, we focused on bulk RNA-seq experiments, which have become ubiquitous in biology, but which have many unknown or difficult to estimate parameters, and so the required analyses to determine the minimum N is typically lacking.
Read the full summary ↓
We therefore performed two N=30 profiling studies between wild-type mice and mice in which one copy of a gene had been deleted, to determine how many mice would be required to minimize false positives and to maximize true discoveries found in the N of 30 experiment
This dataset
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.

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 GSE272152_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1135223 and SRA study SRP519601. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly with about 54 replicates each
kidney.Het ×55 heart.Het ×54 heart.WT ×54 liver.Het ×54 kidney.WT ×53 liver.WT ×53 lung.WT ×39 lung.Het ×38

Supports a between-group comparison across 400 samples.

How we read this: 8 replicated groups read from the first 400 of 479 sample titles; they account for 400 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

+ 439 more — browse all 479 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.