pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R.

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

Platform Design Info for The Manufacturer's Name X_laevis_2.

Platform Design Info for Affymetrix MoGene-2_1-st.

Platform Design Info for The Manufacturer's Name Barley1.

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 miRNA-4_0.

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

Platform Design Info for NimbleGen charm_hg18_example.

This package provides methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets.

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.

PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks.

Platform Design Info for Affymetrix Clariom_S_Rat.

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

This package contains data required to run examples in prebs package. The data files include: 1) Small sample bam files for demonstration purposes 2) Probe sequence mappings for Custom CDF (taken from http://brainarray.mbni.med.umich.edu/brainarray/Database/CustomCDF/genomic_curated_CDF.asp) 3) Probe sequence mappings for manufacturer's CDF (manually created using bowtie).

Regularization and score distributions for position count matrices.

Platform Design Info for The Manufacturer's Name Porcine.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Poplar\_probe\_tab.

This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

The ProteinGymR package provides analysis-ready data resources from ProteinGym, generated by Notin et al., 2023, as well as built-in functionality to visualize the data. ProteinGym comprises a collection of benchmarks for evaluating the performance of models predicting the effect of point mutations. This package provides access to 1. deep mutational scanning (DMS) scores from 217 assays measuring the impact of all possible amino acid substitutions across 186 proteins, 2. model performance metrics and prediction scores from 79 variant prediction models in the zero-shot setting and 12 models in the semi-supervised setting.

Platform Design Info for The Manufacturer's Name Ecoli.

Platform Design Info for The Manufacturer's Name HT_HG-U133A.

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

Platform Design Info for The Manufacturer's Name Canine_2.