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Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

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scDC: single cell differential composition analysis.

Yue Cao¹, Yingxin Lin¹, John T Ormerod¹

¹School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.

BMC Bioinformatics

|December 25, 2019

Summary

This summary is machine-generated.

We developed single cell differential composition (scDC) analysis to accurately estimate cell-type proportions and their uncertainty from single-cell RNA sequencing data. This method improves comparisons across subjects and conditions.

Keywords:

Composition analysis RNA-seq Single cell scRNA-seq

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Cell-type composition analysis is crucial for understanding biological variation.
Single-cell sequencing offers detailed insights but faces challenges in precise cell-type identification and sampling variability.
Accurate estimation of uncertainty in cell-type composition is necessary.

Purpose of the Study:

To develop a novel statistical method for differential cell-type composition analysis.
To address the challenges of uncertainty estimation in single-cell RNA sequencing data.
To provide a robust tool for comparing cell-type proportions across samples and conditions.

Main Methods:

Developed single cell differential composition (scDC) analysis.
Employed bootstrap resampling for uncertainty estimation.
Utilized bias-corrected and accelerated bootstrap confidence intervals.
Validated using simulated and publicly available single-cell datasets.

Main Results:

scDC accurately recovered true cell-type proportions in simulated datasets.
scDC demonstrated high concordance with reference compositions in synthetic datasets.
Confidence intervals enabled improved comparisons of compositional differences, especially with small sample sizes (2-5 subjects per condition).

Conclusions:

scDC is a novel statistical method for differential cell-type composition analysis in scRNA-seq data.
The method estimates standard errors and performs significance testing using GLM and GLMM models.
scDC is available as part of the scdney R package for the scientific community.