This package provides a Bioconductor data package for the Taylor et al (2010) dataset.

This package provides functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.

Platform Design Info for The Manufacturer's Name Ecoli_ASv2.

PWMEnrich pre-compiled background objects for H. sapiens (human) and MotifDb H. sapiens motifs.

Annotated HPLC-ESI-MS lipid data in positive ionization mode from an experiment in which cultures of the marine diatom Phaeodactylum tricornutum were treated with various concentrations of hydrogen peroxide (H2O2) to induce oxidative stress. The experiment is described in Graff van Creveld, et al., 2015, "Early perturbation in mitochondria redox homeostasis in response to environmental stress predicts cell fate in diatoms," ISME Journal 9:385-395. PtH2O2lipids consists of two objects: A CAMERA xsAnnotate object (ptH2O2lipids$xsAnnotate) and LOBSTAHS LOBSet object (ptH2O2lipids$xsAnnotate$LOBSet). The LOBSet includes putative compound assignments from the default LOBSTAHS database. Isomer annotation is recorded in three other LOBSet slots.

Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.

This package performs tests for paired high-throughput data.

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

Platform Design Info for The Manufacturer's Name HG-U219.

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

Reads files exported from QX Manager or QuantaSoft containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall.

Platform Design Info for The Manufacturer's Name Poplar.

This package provides functions to Analyze Microarray (Gene Expression) Data.

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

Platform Design Info for The Manufacturer's Name MG_U74C.

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

Platform Design Info for Affymetrix GenomeWideSNP_6.

The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines.

Affymetrix Affymetrix Porcine Array annotation data (chip porcine) assembled using data from public repositories.

Platform Design Info for The Manufacturer's Name HG-U133B.

This package provides sample files and data for the vignettes of pepStat and Pviz as well as peptide collections for HIV and SIV.

Platform Design Info for Affymetrix HTA-2_0.

This package provides a shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed).

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