GEO series

Signal recovery in single cell batch integration

GSE280883 Mus musculus (mouse) Single-cell RNA-seq 17 samples Submitted 2024/11/08 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 17 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 17 samples: 15 distinct titles, 2 of them repeatedGroups 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)

Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration involves a lot of guesswork: Cells across batches should be aligned to be “appropriately” mixed, while preserving “main cell type clusters”.

Read the full summary ↓

We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation and introducing distortion. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, that harnesses experimental design to explicitly recover biological signals that are erased during single cell data integration. CellANOVA utilizes a “pool-of-controls” design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where it recovered subtle biological signals are can be validated and replicated by external data.

This dataset

Open GSE280883 in finder → Find more mouse 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 GSE280883_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 PRJNA1180965 and SRA study SRP542646. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

15 distinct titles, 2 of them repeated

ll_epi_d3_control ×2 ll_epi_d3_s ×2

How we read this: Read from 17 sample titles: 15 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.

Samples in this study

GSM8606944 cy_epi_d2_control
GSM8606945 cy_lp_d2_control
GSM8606946 cy_epi_d2_sr
GSM8606947 cy_lp_d2_sr
GSM8606948 ll_epi_d3_control_1
GSM8606949 ll_epi_d3_control_2
GSM8606950 ll_epi_d3_sr_1
GSM8606951 ll_epi_d3_sr_2
GSM8606952 cy_epi_d10_control
GSM8606953 cy_lp_d10_control
GSM8606954 cy_epi_d10_sr
GSM8606955 cy_lp_d10_sr
GSM8606956 ll_epi_d10_sr
GSM8606957 cy_epi_d20_control
GSM8606958 cy_lp_d20_control
GSM8606959 cy_epi_d20_sr
GSM8606960 cy_lp_d20_sr

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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.