The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data.

Illumina Mouse Illumina expression annotation data (chip lumiMouseAll) assembled using data from public repositories.

This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.

lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation.

data from PMID 19096707; prototype for managing multiple NGS samples.

This package contains 30 Affymetrix CEL files for 7 Adenocarcinoma (AC) and 8 Squamous cell carcinoma (SCC) lung cancer samples taken at random from 3 GEO datasets (GSE10245, GSE18842 and GSE2109) and other 15 samples from a dataset produced by the organizers of the IMPROVER Diagnostic Signature Challenge available from GEO (GSE43580).

lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

This package includes mappings information between different types of Illumina IDs of Illumina Mouse chips and nuIDs. It also includes mappings of all nuIDs included in Illumina Mouse chips to RefSeq IDs with mapping qualities information.

Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages.

Quantification and differential analysis of mass-spectrometry proteomics data, with probabilistic recovery of information from missing values. Avoids the need for imputation. Estimates the detection probability curve (DPC), which relates the probability of successful detection to the underlying log-intensity of each precursor ion, and uses it to incorporate missing values into protein quantification and into subsequent differential expression analyses. The package produces objects suitable for downstream analysis in limma. The package accepts precursor (or peptide) intensities including missing values and produces complete protein quantifications without the need for imputation. The uncertainty of the protein quantifications is propagated through to the limma analyses using variance modeling and precision weights, ensuring accurate error rate control. The analysis pipeline can alternatively work with PTM or protein level data. The package name "limpa" is an acronym for "Linear Models for Proteomics Data".

The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa.

Data from three large lung cancer studies provided as ExpressionSets.

LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points.

This package provides a package containing the data to run LowMACA package.

loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB.

This is an external ExperimentData package for LRcell. This data package contains the gene enrichment scores calculated from scRNA-seq dataset which indicates the gene enrichment of each cell type in certain brain region. LRcell package is used to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. For more details, please visit: https://github.com/marvinquiet/LRcell.

LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks.

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Package includes functions to analyze and mask microarray expression data.

Store minor allele frequency data from the Exome Aggregation Consortium (ExAC release 1.0) for the human genome version hs37d5.

This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).

Affymetrix Affymetrix MG_U74Cv2 Array annotation data (chip mgu74cv2) assembled using data from public repositories.

Store minor allele frequency data from NHLBI TOPMed for the human genome version hg38.

Codelink Mouse Whole Genome Bioarray (~36 000 mouse gene targets) annotation data (chip mwgcod) assembled using data from public repositories.