stochprofML: stochastic profiling using maximum likelihood estimation in R

stochprofML: stochastic profiling using maximum likelihood estimation in R

Background: Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. Results: We present the R package stochprofML which u...

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Bibliographic Details
Main Authors: Amrhein, L. (Author), Fuchs, C. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Cell culture
Cell fates
cell lineage
Cell Lineage
Cell populations
Cell proliferation
Cells
Cell-to-cell heterogeneity
computer simulation
Computer Simulation
Deconvolution
De-mixing
Gene expression
human
Humans
Likelihood Functions
Markov chain
Maximum likelihood estimation
Maximum likelihood Principle
Mixed samples
Mixture models
Parameterizing
R
Simulation studies
Single cells
statistical model
Stochastic Processes
Stochastic profiling
Stochastic systems
StochprofML
Tissue
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