scholarly journals stochprofML: stochastic profiling using maximum likelihood estimation in R

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lisa Amrhein ◽  
Christiane Fuchs
Keyword(s):  
Maximum Likelihood ◽  
Cell Fate ◽  
Likelihood Estimation ◽  
R Package ◽  
Cell Populations ◽  
Simulation Studies ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Molecular Expression ◽  
Mixed Samples

Abstract 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 uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm’s performance in simulation studies and present further application opportunities. Conclusion Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.

scholarly journals Classification from positive and unlabeled data based on likelihood invariance for measurement

2021 ◽  
Vol 25 (1) ◽  
pp. 57-79
Author(s):  
Takeshi Yoshida ◽  
Takashi Washio ◽  
Takahito Ohshiro ◽  
Masateru Taniguchi
Keyword(s):  
Maximum Likelihood ◽  
Single Molecule ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Target Object ◽  
Unlabeled Data ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Observation Process ◽  
Positive Class

We propose novel approaches for classification from positive and unlabeled data (PUC) based on maximum likelihood principle. These are particularly suited to measurement tasks in which the class prior of the target object in each measurement is unknown and significantly different from the class prior used for training, while the likelihood function representing the observation process is invariant over the training and measurement stages. Our PUCs effectively work without estimating the class priors of the unlabeled objects. First, we present a PUC approach called Naive Likelihood PUC (NL-PUC) using the maximum likelihood principle in a nontrivial but rather straightforward manner. The extended version called Enhanced Likelihood PUC (EL-PUC) employs an algorithm iteratively improving the likelihood estimation of the positive class. This is advantageous when the availability of the labeled positive data is limited. These characteristics are demonstrated both theoretically and experimentally. Moreover, the practicality of our PUCs is demonstrated in a real application to single molecule measurement.

MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Samara F. Kiihl ◽  
Maria Jose Martinez-Garrido ◽  
Arce Domingo-Relloso ◽  
Jose Bermudez ◽  
Maria Tellez-Plaza
Keyword(s):  
Dna Methylation ◽  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Analytical Form ◽  
R Package ◽  
Maximum Likelihood Estimates ◽  
Computational Time ◽  
Iterative Approximation ◽  
Sequencing Technologies ◽  
Combining Data

Abstract Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the three methods combination, we implemented both the iterative method by Qu et al. [Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): “Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation,” Bioinformatics, 29, 2645–2646.], and also a novel non iterative approximation using Lagrange multipliers. The newly proposed non iterative solutions greatly decrease computational time, common bottlenecks when processing high-throughput data. The MLML2R package is flexible as it takes as input both, preprocessed intensities from Infinium Methylation arrays and counts from Next Generation Sequencing technologies. The MLML2R package is freely available at https://CRAN.R-project.org/package=MLML2R.

Gauss’ law of error revisited in the framework of Sharma-Taneja-Mittal information measure

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Antonio Scarfone ◽  
Hiroki Suyari ◽  
Tatsuaki Wada
Keyword(s):  
Maximum Likelihood ◽  
Power Law ◽  
Information Measure ◽  
Asymptotic Power ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Error Distributions ◽  
Related Algebra

AbstractWe reformulate the Gauss’ law of error in presence of correlations which are taken into account by means of a deformed product arising in the framework of the Sharma-Taneja-Mittal measure. Having reviewed the main proprieties of the generalized product and its related algebra, we derive, according to the Maximum Likelihood Principle, a family of error distributions with an asymptotic power-law behavior.

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