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MXM / Mens eX Machina
MXM is a flexible R package which offers feature selection algorithms for predictive or diagnostic models along with (Bayesian) network construction algorithms. State of the art feature selection algorithms include FBED and SES with the latter returning multiple sets of statistically equivalent variables (one of the few algorithms in the literature). The algortihms can handle many types of response variables, such as continuous, binary, multiclass, ordinal, (censored) time to event, repeated measurements, percentages etc.
LAMP / Limitless-Arity Multiple-testing Procedure
Counts the exact number of “testable” motif combinations and derives a tighter bound of family-wise error rate (FWER), allowing the calibration of the Bonferroni factor. LAMP is a branch-and-bound algorithm. The software can be used to provide an integrated analysis of heterogeneous biological data. It was applied to human breast cancer transcriptome data and permitted to find statistically significant combinations of up to eight motifs.
caret / Classification And REgression Training
Provides a set of functions that attempt to streamline the process for creating predictive models. caret is an R package that contains tools for (i) data splitting, (ii) pre-processing, (iii) feature selection, (iv) model tuning using resampling, and (v) variable importance estimation. The package started off as a way to provide a uniform interface the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance).
CircStat / Circular Statistics Toolbox
Provides methods for the descriptive and inferential statistical analysis of directional data. CircStat can be used to explore and summarize important properties of a sample of angular data such as central tendency, spread, symmetry or peakedness. The functions implemented in the software allow to test the popular question of circular uniformity, while other methods allow to investigate more specific hypothesis about the mean direction of one or multiple samples.
Allows multivariate data analysis. FactoMineR allows to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). It performs classical methods such as Principal Components Analysis (PCA), Correspondence analysis (CA), Multiple Correspondence Analysis (MCA) as well as more advanced methods.
Allows to evaluate and visualize the performance of scoring classifiers. ROCR features over 25 performance measures that can be freely combined to create two-dimensional performance curves. It uses standard methods for investigating trade-offs between specific performance measures, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. The tool allows for studying the intricacies inherent to many biological datasets and their implications on classifier performance.
BICAR / Bidirectional Independent Component Averaged Representation
Allows to obtain robust, reproducible pairs of temporal and spatial components at the individual subject level from concurrent electroencephalographic and functional magnetic resonance imaging data. BICAR is an algorithm which allows to find biologically relevant paired sources involved in visual processing, motor planning, execution, and attention, which are highly reproducible and present in multiple subjects. The algorithm ranks each joint source by a task-independent measure of reproducibility.
Predicts the survival of cancer patients from microarray data, and classifies obese and lean individuals from metagenomic data. pensim can be applied for high-dimensional feature selection and prediction of genomic data. The tool contains a function for generating synthetic high-dimensional data with time-to-event or binary outcome, and blocks of predictor variables defined by collinearity and association with outcome, with options for introducing labeling errors and for censoring of survival times.
MINERVA / Maximal Information-based Nonparametric Exploration R package for Variable Analysis
Provides the mine function allowing the computation of Maximal Information-based Nonparametric Exploration (MINE) statistics. Minerva allows native parallelization: based on the R package parallel, the number of cores can be passed as parameter to mine, whenever multi-core hardware is available. The main function mine takes the dataset and the parameter configuration as inputs and returns the four MINE statistics.
ACT / Aggregation and Correlation Toolbox
Analyzes continuous signal and discrete region tracks from high-throughput genomic experiments. ACT is able to generate aggregate profiles of a given track around a set of specified anchor points, such as transcription start sites. It correlates related tracks and analyzes them for saturation. The tool takes less than a minute to generate the plot for up to 30 input files each with a few thousand lines. It provides an option to compute the coverage of a random sample of the input file combinations.
Implements a number of efficient statistical methods developed for : (i) estimating subgroup treatment effects and gene–treatment interactions, (ii) exploiting the gene–treatment independence dictated by randomization, and (iii) including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model. TwoPhaseInd is an R package computationally scalable to genome-wide studies, as illustrated by an example from Women’s Health Initiative.
MP-LAMP / Massive Parallel Limitless-Arity Multiple-testing Procedure
Discovers significant combinations of alleles. MP-LAMP is parallelized to decrease time-consuming analysis. It allows users to traverse the search tree collectively without load unbalance. This tool is useful for genome wide association study (GWAS) analysis. It is based on the limitless arity multiple-testing procedure (LAMP) approach that aims to reduce the correction factor by a tighter bound of family-wise error rate (FWER).
easyROC / easy Receiver Operating Characteristics
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Offers a large variety of graphical tools for visual inspection of receiver operating characteristics (ROC) curves, including ROC curves, sensitivity and specificity curves and distribution plots. easyROC is a web-tool that combines several R packages. It delivers basic OCR statistics such as the AUC as well as its standard error, confidence interval and statistical significance.
OPATs / Omnibus P-value Association Tests
Permits P-value combinations by using popular analysis methods. OPATs enables a gene region to be extended upstream and downstream by a prespecified width. It can be used to identify genetic markers and marker sets associated with complex diseases and traits of interest. The tool does not require genotypic and phenotypic data in an analysis. It can be useful for analysis of P-values from different types of molecular markers in an omics study, family- and population-based association studies.
Contains a set of tools displaying, analyzing, smoothing and comparing receiver operating characteristic (ROC) curves. pROC proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve that allows proper ROC interpretation. It is based on U-statistics theory and asymptotic normality method to compare the areas under the curve (AUCs). The tool provides a consistent and user-friendly set of functions building and plotting a ROC curve, several methods smoothing the curve, computing the full or partial AUC over any range of specificity or sensitivity, as well as computing and visualizing various confidence intervals.
RAICAR / Ranking and Averaging Independent Component Analysis by Reproducibility
Improves the decomposition and interpretation of functional magnetic resonance imaging (fMRI) data with independent component analysis (ICA). RAICAR is an ICA method based on reproducibility. The software utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. It estimates the number of components, provides the order of the components, based on component reproducibility and leads to improved data decomposition by selectively averaging across ICA realizations.
RL-SKAT / Recalibrated Lightweight Sequence Kernel Association Test
Allows exact p-value calculation score test in heritability. RL-SKAT is a computational method that can be used in the case of a single variance component and constant response vector. This process permits to speed up the analysis by orders of magnitude. This software could also be employs to answer several questions, such as (i) estimation of the underlying heritability of a phenotype, (ii) estimating the uncertainty of such estimation, (iii) phenotype prediction, and many others.
RERT / Representative Regression Tree
Predicts the surgical/pathological stage of the disease in a large cohort of endometrial cancer (EC) patients. RERT was developed to preoperatively identify an advanced surgical FIGO stage. It uses sHE4 and sCA125 biomarkers together with other preoperatively available clinical and pathological variables such as covariates (age, body mass index (BMI), number of children, menopause status, contraception, hormone replacement therapy (HRT), hypertension, grading from biopsy, clinical stage).
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Assists users in manipulating high-throughput sequencing (HTS) data and formats. Picard is a Java toolkit that provides a set of command line scripts. It comprises Java-based utilities that manipulate SAM files, and a Java API for creating new programs that reads and writes SAM files. Both SAM text format and SAM binary (BAM) format are supported. It also works with next generation sequencing (NGS).
MIPReSt / Mixed ICA/PCA via Reproducibility Stability
Allows to assess component stability as the size of the data matrix changes, which can be used to determine the dimension of the non-gaussian subspace in a mixture. MIPReSt is an algorithm for mixed independent component analysis (ICA)/principal component analysis (PCA). The software uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple sub-samplings of the original data matrix.
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