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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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destiny: diffusion maps for large-scale single-cell data in R.

Philipp Angerer1, Laleh Haghverdi1, Maren Büttner1

  • 1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany and.

Bioinformatics (Oxford, England)
|December 16, 2015
PubMed
Summary
This summary is machine-generated.

The destiny R package provides an efficient diffusion map implementation for visualizing single-cell expression data, handling missing values and large datasets for advanced biological insights.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Single-cell Analysis

Background:

  • Diffusion maps are powerful for non-linear dimension reduction.
  • Their application to single-cell expression data aids visualization.
  • Existing implementations may struggle with large datasets or missing values.

Purpose of the Study:

  • To present destiny, an efficient R implementation of the diffusion map algorithm.
  • To introduce a single-cell specific noise model for handling missing and censored data.
  • To enable processing of large single-cell datasets and projection of new data.

Main Methods:

  • Implementation of the diffusion map algorithm in R.
  • Inclusion of a noise model for missing/censored single-cell data.
  • Development of an efficient nearest-neighbor approximation for scalability.

Main Results:

  • destiny offers an efficient R package for diffusion map analysis.
  • The package accommodates missing and censored data typical in single-cell experiments.
  • Scalability allows processing of hundreds of thousands of cells.
  • Functionality to project new data onto existing diffusion maps is provided.

Conclusions:

  • destiny provides a scalable and robust tool for single-cell data visualization.
  • The package's features facilitate advanced analysis of complex biological systems.
  • Demonstrated application on mass cytometry data highlights its utility in cellular reprogramming studies.