Showing 13 of total 13 results (show query)
bioc
muscat:Multi-sample multi-group scRNA-seq data analysis tools
`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
Maintained by Helena L. Crowell. Last updated 5 months ago.
immunooncologydifferentialexpressionsequencingsinglecellsoftwarestatisticalmethodvisualization
184 stars 10.74 score 686 scripts 1 dependents
graemeleehickey
joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).
Maintained by Graeme L. Hickey. Last updated 2 months ago.
armadillobiostatisticsclinical-trialscoxdynamicjoint-modelslongitudinal-datamultivariate-analysismultivariate-datamultivariate-longitudinal-datapredictionrcppregression-modelsstatisticssurvivalopenblascppopenmp
30 stars 8.93 score 146 scripts 1 dependents
theomichelot
moveHMM:Animal Movement Modelling using Hidden Markov Models
Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.
Maintained by Theo Michelot. Last updated 1 years ago.
38 stars 8.63 score 112 scripts
bioc
treeclimbR:An algorithm to find optimal signal levels in a tree
The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one.
Maintained by Charlotte Soneson. Last updated 3 months ago.
statisticalmethodcellbasedassays
20 stars 7.00 score 45 scriptsbioc
mnem:Mixture Nested Effects Models
Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.
Maintained by Martin Pirkl. Last updated 4 days ago.
pathwayssystemsbiologynetworkinferencenetworkrnaseqpooledscreenssinglecellcrispratacseqdnaseqgeneexpressioncpp
4 stars 6.81 score 15 scripts 4 dependentsbioc
FeatSeekR:FeatSeekR an R package for unsupervised feature selection
FeatSeekR performs unsupervised feature selection using replicated measurements. It iteratively selects features with the highest reproducibility across replicates, after projecting out those dimensions from the data that are spanned by the previously selected features. The selected a set of features has a high replicate reproducibility and a high degree of uniqueness.
Maintained by Tuemay Capraz. Last updated 2 months ago.
softwarestatisticalmethodfeatureextractionmassspectrometry
2 stars 4.48 score 3 scripts
jamesmurray7
gmvjoint:Joint Models of Survival and Multivariate Longitudinal Data
Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.
Maintained by James Murray. Last updated 6 months ago.
glmmjoint-modelslongitudinalmixed-modelsmodelpredictionsurvivalsurvival-analysisopenblascppopenmp
3 stars 3.78 score 20 scripts
vandenman
DstarM:Analyze Two Choice Reaction Time Data with the D*M Method
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Maintained by Don van den Bergh. Last updated 3 years ago.
3 stars 3.41 score 17 scripts
annavesely
sumSome:True Discovery Guarantee by Sum-Based Tests
It allows to quickly perform closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As main features, it produces permutation-based simultaneous lower confidence bounds for the proportion of active voxels in clusters for fMRI data, differentially expressed genes in pathways for gene expression data, and significant effects for multiverse analysis. Details may be found in Vesely at al. (2023) < doi:10.1093/jrsssb/qkad019> and Tian at al. (2022) <doi:10.1111/sjos.12614>.
Maintained by Anna Vesely. Last updated 2 months ago.
1 stars 2.70 score 3 scripts
cran
probout:Unsupervised Multivariate Outlier Probabilities for Large Datasets
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
Maintained by Chris Fraley. Last updated 3 years ago.
2.00 score
mkhondoker
optBiomarker:Estimation of Optimal Number of Biomarkers for Two-Group Microarray Based Classifications at a Given Error Tolerance Level for Various Classification Rules
Estimates optimal number of biomarkers for two-group classification based on microarray data.
Maintained by Mizanur Khondoker. Last updated 4 years ago.
2.00 score 1 scripts