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patnik
CIM:Compositional Impact of Migration
Produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) <doi:10.1080/00324728.2017.1416155> for technical details.
Maintained by Nikos Patias. Last updated 6 years ago.
58.3 match 2.00 score 4 scripts
bioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 4 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
6.8 match 182 stars 13.71 score 1.3k scripts 22 dependents
kbroman
qtl:Tools for Analyzing QTL Experiments
Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Maintained by Karl W Broman. Last updated 7 months ago.
5.3 match 80 stars 12.79 score 2.4k scripts 29 dependents
cran
ufs:A Collection of Utilities
This is a new version of the 'userfriendlyscience' package, which has grown a bit unwieldy. Therefore, distinct functionalities are being 'consciously uncoupled' into different packages. This package contains the general-purpose tools and utilities (see the 'behaviorchange' package, the 'rosetta' package, and the soon-to-be-released 'scd' package for other functionality), and is the most direct 'successor' of the original 'userfriendlyscience' package. For example, this package contains a number of basic functions to create higher level plots, such as diamond plots, to easily plot sampling distributions, to generate confidence intervals, to plan study sample sizes for confidence intervals, and to do some basic operations such as (dis)attenuate effect size estimates.
Maintained by Gjalt-Jorn Peters. Last updated 1 years ago.
3.3 match 2.95 score 3 dependents
vincentgarin
mppR:Multi-Parent Population QTL Analysis
Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.
Maintained by Vincent Garin. Last updated 1 years ago.
1.3 match 2 stars 5.35 score 28 scriptscran
mixKernel:Omics Data Integration Using Kernel Methods
Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.
Maintained by Nathalie Vialaneix. Last updated 1 years ago.
2.3 match 2.78 score 9 scripts