GEO series

Cell type-specific weighting-factors to solve solid organs-specific limitations of single cell RNA-sequencing

GSE263816 Mus musculus (mouse) Single-cell RNA-seq 30 samples Submitted 2024/10/09 Platform GPL19057

At a glance

Single-cell RNA-seq · mouse · 30 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: 30 samples: 30 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: Cell type-specific weighting-factors to solve solid organs-specific limitations of single cell RNA-sequencingPLoS genetics · 2024

Summary (written by the submitters)

While single-cell RNA-sequencing (scRNA-seq) is a popular method to analyze gene expression and cellular composition at single-cell resolution, it harbors shortcomings: The failure to account for cell-to-cell variations of transcriptome-size (i.e., the total number of transcripts per cell) and also cell dissociation/processing-induced cryptic gene expression.

Read the full summary ↓

This is particularly a problem when analyzing highly heterogeneous solid tissues/organs, which requires cell dissociation for the analysis. As a result, there exists a discrepancy between bulk RNA-seq result and virtually reconstituted bulk RNA-seq result using its composite scRNA-seq data. To fix this problem, we propose a computationally calculated coefficient, “cell type-specific weighting-factor (cWF)”. Here, we introduce a concept and a method of its computation and report cWFs for 76 cell-types across 10 solid organs. Their fidelity is validated by more accurate reconstitution and deconvolution of bulk RNA-seq data of diverse solid organs using the scRNA-seq data and the cWFs of their composite cells. Furthermore, we also show that cWFs effectively predict aging-progression, implicating their diagnostic applications and also their association with aging mechanism. Our study provides an important method to solve critical limitations of scRNA-seq analysis of complex solid tissues/organs. Furthermore, our findings suggest a diagnostic utility and biological significance of cWFs.

Published in

Cell type-specific weighting-factors to solve solid organs-specific limitations of single cell RNA-sequencing

Tejima K, Kozawa S, Sato TN · PLoS genetics 2024 · PMID 39556600 · doi:10.1371/journal.pgen.1011436

This dataset

Open GSE263816 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 GSE263816_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 PRJNA1099284 and SRA study SRP501288. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39556600) ↗

Study design

30 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 30 sample titles: 30 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

GSM8199603 11wks_WT_2__Brain
GSM8199604 11wks_WT_2__Fat
GSM8199605 11wks_WT_2__Heart
GSM8199606 11wks_WT_2__Kidney
GSM8199607 11wks_WT_2__Liver
GSM8199608 11wks_WT_2__Lung
GSM8199609 11wks_WT_2__Pancreas
GSM8199610 11wks_WT_2__Skin
GSM8199611 11wks_WT_2__SkMuscle
GSM8199612 11wks_WT_2__Spleen
GSM8241738 11wks_WT_3__Brain
GSM8241739 11wks_WT_3__Fat
GSM8241740 11wks_WT_3__Heart
GSM8241741 11wks_WT_3__Kidney
GSM8241742 11wks_WT_3__Liver
GSM8241743 11wks_WT_3__Lung
GSM8241744 11wks_WT_3__Pancreas
GSM8241745 11wks_WT_3__Skin
GSM8241746 11wks_WT_3__SkMuscle
GSM8241747 11wks_WT_3__Spleen
GSM8241748 11wks_WT_4__Brain
GSM8241749 11wks_WT_4__Fat
GSM8241750 11wks_WT_4__Heart
GSM8241751 11wks_WT_4__Kidney
GSM8241752 11wks_WT_4__Liver
GSM8241753 11wks_WT_4__Lung
GSM8241754 11wks_WT_4__Pancreas
GSM8241755 11wks_WT_4__Skin
GSM8241756 11wks_WT_4__SkMuscle
GSM8241757 11wks_WT_4__Spleen

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