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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
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Untangling biological factors influencing trajectory inference from single cell data.
Mohammed Charrout1, Marcel J T Reinders1, Ahmed Mahfouz1
1Delft Bioinformatics Lab, Delft University of Technology, Delft 2628 XE, The Netherlands.
NAR Genomics and Bioinformatics
|February 12, 2021
Summary
Single-cell RNA sequencing reveals cell identity through transcriptional state. This study shows how to filter cell cycle variations to accurately infer cell differentiation trajectories.
Area of Science:
- Genomics
- Computational Biology
- Cell Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) offers high resolution for studying cellular heterogeneity and differentiation.
- Interpreting scRNA-seq data requires distinguishing true biological variation from technical or confounding factors.
- The cell cycle is a major source of variation that can obscure true biological signals in scRNA-seq data.
Purpose of the Study:
- To investigate the impact of confounding biological variation, such as the cell cycle, on cell differentiation trajectory inference.
- To develop a method for factorizing scRNA-seq data to isolate core regulatory factors for trajectory analysis.
- To improve the accuracy of inferred differentiation trajectories by filtering out non-relevant sources of variation.
Main Methods:
- Factorization of single-cell data into distinct sources of variation.
- Identification and selection of key factors driving cellular identity and differentiation.
- Filtering of confounding factors, including cell cycle-related variations.
Main Results:
- Confounding biological variations, particularly the cell cycle, can significantly distort inferred cell differentiation trajectories.
- Factorization enables the selection of core regulatory factors essential for accurate trajectory inference.
- Filtering out confounding factors like the cell cycle improves the robustness and reliability of trajectory analysis.
Conclusions:
- Accurate inference of cell differentiation dynamics from scRNA-seq data requires careful consideration and removal of confounding biological variations.
- The proposed factorization approach allows for the isolation of biologically relevant signals, leading to more precise understanding of cell fate decisions.
- This method enhances the utility of scRNA-seq for dissecting complex biological processes.
