GEO series

Method of moments approach for generalized differential gene expression analysis (Perturb-seq)

GSE274751 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2024/10/24 Platform GPL34281

At a glance

Single-cell RNA-seq · human · 24 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: Homo sapiens (human)
Samples: 24 samples: 5 conditions, mostly in duplicateGroups 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: Method of moments framework for differential expression analysis of single-cell RNA sequencing dataCell · 2024

Summary (written by the submitters)

Differential expression analysis of scRNA-seq data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remains challenging.

Read the full summary ↓

Here, we introduce memento, a tool designed to address these limitations by providing statistically robust and computationally efficient differential expression analysis of the mean, variability, and gene correlation from scRNA-seq. We applied memento to analyze 70,000 tracheal epithelial cells to identify interferon response genes with distinct variability and correlation patterns, 160,000 T cells perturbed with CRISPR-Cas9 to reconstruct gene-regulatory networks that control T cell activation, 1.2 million PMBCs to map cell-type-specific cis expression quantitative trait loci (eQTLs), and arbitrary cell groups within the entire 50 million cell CELLxGENE Discover data corpus. In all cases, memento identified more significant and reproducible differences in mean expression but also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by cytokines, genetic perturbations, and natural genetic variation. These results demonstrate that memento is a first-in-class method for the quantitative analysis of scRNA-seq data, scalable to millions of cells and thousands of samples.

Published in

Method of moments framework for differential expression analysis of single-cell RNA sequencing data

Kim MC, Gate R, Lee DS et al. · Cell 2024 · PMID 39454576 · doi:10.1016/j.cell.2024.09.044

This dataset

Open GSE274751 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 GSE274751_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: 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 PRJNA1147758 and SRA study SRP527092. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39454576) ↗

Study design

5 conditions, mostly in duplicate

CD4 CROP-seq well ×16 HTEC HTO sample 1 ×2 HTEC HTO sample 2 ×2 HTEC cDNA sample 1 ×2 HTEC cDNA sample 2 ×2

Supports a between-group comparison across 24 samples.

How we read this: 5 replicated groups read from 24 sample titles; they account for 24 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

GSM8457382 CD4 CROP-seq well1 CD4 CROP-seq well
GSM8457383 CD4 CROP-seq well2 CD4 CROP-seq well
GSM8457384 CD4 CROP-seq well3 CD4 CROP-seq well
GSM8457385 CD4 CROP-seq well4 CD4 CROP-seq well
GSM8457386 CD4 CROP-seq well5 CD4 CROP-seq well
GSM8457387 CD4 CROP-seq well6 CD4 CROP-seq well
GSM8457388 CD4 CROP-seq well7 CD4 CROP-seq well
GSM8457389 CD4 CROP-seq well8 CD4 CROP-seq well
GSM8457390 CD4 CROP-seq well9 CD4 CROP-seq well
GSM8457391 CD4 CROP-seq well10 CD4 CROP-seq well
GSM8457392 CD4 CROP-seq well11 CD4 CROP-seq well
GSM8457393 CD4 CROP-seq well12 CD4 CROP-seq well
GSM8457394 CD4 CROP-seq well13 CD4 CROP-seq well
GSM8457395 CD4 CROP-seq well14 CD4 CROP-seq well
GSM8457396 CD4 CROP-seq well15 CD4 CROP-seq well
GSM8457397 CD4 CROP-seq well16 CD4 CROP-seq well
GSM8511644 HTEC HTO sample 1-1 HTEC HTO sample 1
GSM8511645 HTEC HTO sample 1-2 HTEC HTO sample 1
GSM8511646 HTEC HTO sample 2-1 HTEC HTO sample 2
GSM8511647 HTEC HTO sample 2-2 HTEC HTO sample 2
GSM8511648 HTEC cDNA sample 1-1 HTEC cDNA sample 1
GSM8511649 HTEC cDNA sample 1-2 HTEC cDNA sample 1
GSM8511650 HTEC cDNA sample 2-1 HTEC cDNA sample 2
GSM8511651 HTEC cDNA sample 2-2 HTEC cDNA sample 2

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