Provides a batch version of a neural learning algorithm for Independent Component Analysis (ICA). fastICA is an R package based on a fixed-point method. It was introduce using a very simple yet highly efficient fixed-point iteration scheme for finding the local extrema of the kurtosis of a linear combination of the observed variables. The computations can be performed either in batch mode or semi-adaptively.
Finds associations in large datasets. IHW is based on the Benjamini-Hochberg procedure and uses weights derived from the data. It divides the tests into groups based on the covariate. This tool assigns low weight to covariate-groups with low signal. It is capable of avoiding loss of false discovery rate (FDR) control by employing randomization in the form of hypothesis splitting into k-folds.
Compiles multiple features selection algorithms for predictive or diagnostic models along with (Bayesian) network construction algorithms. MXM is a package including statistically equivalent signatures (SES) and an extension of the orthogonal matching pursuit (OMG) method. It can handle multiple response variables, such as continuous, binary, multiclass, ordinal, left censored or proportions, repeated measurements and more.
Allows a variety of statistical tests. G*Power can analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. It can also be used to compute effect sizes and to display graphically the results of power analyses.
Fixes the rejection region in multiple hypothesis testing adjustment. Myriads uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. It assists users to detect true effects jointly with the reasonable proportion of false discoveries one should assume.
Assists in analysing longitudinal and growth curve data. MASAL is based on a high-dimensional smoothing technique and does not impose functional restrictions on time and covariates a priori. It can be used as a guide before using the other models or as a benchline validation. The software is applicable provided that a within-subject covariance matrix is supplied.
Allows users to specify a broad range of models involving continuous parameters by coding their log posteriors up to a proportion. Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. This resource provides full Bayesian inference for posterior expectations including parameter estimation and posterior predictive inference by defining appropriate derived quantities of interest.
Assists users in conceptualization, visualization and manipulation of datasets available on the DiscoveryDB database. DiscoverySpace supports all possible data models with only minimal configuration on the part of the database administrator. It aims to expose the content and power of the underlying database while abstracting away its low-level complexity. This tool permits users to traverse multiple biological databases.
Allows users to upload their own data and easily create Principal Component Analysis (PCA) plots and heatmaps. Data can be uploaded as a file or by copy-pasteing it to the text box. Data format is shown under "Help" tab. Several R packages are used internally, including shiny, ggplot2, pheatmap, RColorBrewer, FactoMineR, pcaMethods, shinyBS and others.
Computes the statistics for sparse primate infection data. VacMan is useful to calculate an rms standard deviation, which ranks by merit secondary titrations designed, and the non-infection probability at different viral doses, which permits minimal challenge dose (MCD) estimation. It can serve to test P values, which decide whether a treatment is efficient.
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.
Allows users to perform adjustment for confounding (AC) variation and dimension reduction simultaneously. AC-PCA provides a standalone software that can be applied to various genomics data for classifying, for instance, yeast mutants using metabolic foot printing or immune cells using DNA methylome. It was tested on a human brain development exon array dataset, a model organism ENCODE RNA sequencing dataset and simulated data.
Provides functions to conducting univariate and multivariate meta-analysis using a Structural Equation Modelling (SEM) approach. metaSEM is an R package that implements a two-stage structural equation modeling (TSSEM) approach to conducting fixed- and random-effects meta-analytic structural equation modeling (MASEM) on correlation/covariance matrices. Many of the techniques available in this SEM package can be easily extended to meta-analysis.
Performs Bayesian clustering using a Dirichlet process mixture model. PReMiuM allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. This tool supplies several functions for post-processing of different outputs. Moreover, it assists users to determine which covariates actively drive the mixture components.
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).
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.
Executes simple and partial Mantel tests. zt is a command-line software, capable of managing very large matrices, that seeks the correlation between two matrices and eliminates the non-valid ones by controlling the effect of a third one. This tool can be used to determine distance between genetic and environmental subjects.
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.
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).
Assists users in fitting Structural Equation Models (SEM). OpenMx is an open source application that can estimates maximum likelihood parameters for models with multivariate outcomes given an observed covariance matrix. It allows users to specify matrix algebra calculations as part of his model. It also allows the definition of boundary constraints with respect to constants and with respect to other parameters.