Showing 5 of total 5 results (show query)
bioc
edge:Extraction of Differential Gene Expression
The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.
Maintained by John D. Storey. Last updated 5 months ago.
multiplecomparisondifferentialexpressiontimecourseregressiongeneexpressiondataimport
21 stars 7.77 score 62 scripts
nelson-gon
manymodelr:Build and Tune Several Models
Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.
Maintained by Nelson Gonzabato. Last updated 13 days ago.
analysis-of-varianceanovacorrelationcorrelation-coefficientgeneralized-linear-modelsgradient-boosting-decision-treesknn-classificationlinear-modelslinear-regressionmachine-learningmissing-valuesmodelsr-programmingrandom-forest-algorithmregression-models
2 stars 5.78 score 50 scriptsbioc
scShapes:A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data
We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.
Maintained by Malindrie Dharmaratne. Last updated 5 months ago.
rnaseqsinglecellmultiplecomparisongeneexpression
8 stars 4.90 score 6 scripts
maple-health-group
easysurv:Simplify Survival Data Analysis and Model Fitting
Inspect survival data, plot Kaplan-Meier curves, assess the proportional hazards assumption, fit parametric survival models, predict and plot survival and hazards, and export the outputs to 'Excel'. A simple interface for fitting survival models using flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), flexsurvcure::flexsurvcure(), and survival::survreg().
Maintained by Niall Davison. Last updated 10 months ago.
4 stars 4.60 score 7 scripts
sustainscapes
AICcPermanova:Model Selection of PERMANOVA Models Using AICc
Provides tools for model selection and model averaging of PerMANOVA models using Akaike Information Criterion corrected for small sample sizes (AICc) and Information Theoretic criteria principles. The package is built around the PERMANOVA analysis from the 'vegan' package and provides a streamlined workflow for generating and comparing models, obtaining model weights, and summarizing results using model averaging approaches. The methods implemented in this package are based on the practical information- theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002) (<doi:10.1007/b97636>).
Maintained by Derek Corcoran. Last updated 1 years ago.
3.70 score 9 scripts