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Platform Design Info for The Manufacturer's Name miRNA-1_0.
Platform Design Info for Affymetrix FinGene-1_0-st.
This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data.
Package for the position related analysis of quantitative functional genomics data.
Platform Design Info for Affymetrix MedGene-1_1-st.
PWMEnrich pre-compiled background objects for Drosophila melanogaster and MotifDb D. melanogaster motifs.
Platform Design Info for Affymetrix FinGene-1_1-st.
The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC).
Platform Design Info for Affymetrix EquGene-1_0-st.
Platform Design Info for Affymetrix PorGene-1_1-st.
Platform Design Info for The Manufacturer's Name HG_U95D.
PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10.
Platform Design Info for The Manufacturer's Name Mu11KsubA.
The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.
Platform Design Info for Affymetrix MTA-1_0.
PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them.
Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.
Platform Design Info for The Manufacturer's Name NuGO_Mm1a520177.
Platform Design Info for The Manufacturer's Name RG_U34A.
Platform Design Info for Affymetrix CyRGene-1_0-st.
Platform Design Info for Affymetrix RJpGene-1_1-st.
Platform Design Info for Affymetrix RaGene-2_0-st.
Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements.
Regularization and score distributions for position count matrices.