Platform Design Info for The Manufacturer's Name miRNA-1_0.

The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix.

Platform Design Info for Affymetrix MoGene-2_0-st.

Platform Design Info for The Manufacturer's Name MG_U74C.

This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the sample by features table of peak intensities in addition to the sample and feature metadata (as a singl<e ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection.

Platform Design Info for The Manufacturer's Name Mouse430A_2.

Base annotation databases for pig, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

Store phastCons30way.UCSC.hg38 AnnotationHub Resource Metadata.

Platform Design Info for The Manufacturer's Name Citrus.

Platform Design Info for Affymetrix miRNA-4_0.

PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data.

Platform Design Info for The Manufacturer's Name Vitis_Vinifera.

This package provides a toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools.

Platform Design Info for Affymetrix Clariom_S_Human_HT.

Platform Design Info for The Manufacturer's Name RAE230A.

Platform Design Info for Affymetrix HuGene-1_0-st-v1.

This package provides a Bioconductor data package for the Stockholm dataset.

Platform Design Info for Affymetrix MarGene-1_1-st.

Platform Design Info for Affymetrix EleGene-1_0-st.

Platform Design Info for The Manufacturer's Name MG_U74Av2.

Platform Design Info for The Manufacturer's Name Canine_2.

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.

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 Affymetrix HuEx-1_0-st-v2.