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

Platform Design Info for The Manufacturer's Name MG_U74C.

Platform Design Info for Affymetrix RaGene-1_1-st-v1.

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

Platform Design Info for The Manufacturer's Name HG-U133_Plus_2.

Platform Design Info for The Manufacturer's Name NuGO_Mm1a520177.

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

Platform Design Info for NimbleGen feinberg_mm8_me_hx1.

Platform Design Info for The Manufacturer's Name Hu6800.

Platform Design Info for Affymetrix Clariom_S_Human_HT.

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.

Platform Design Info for NimbleGen charm_hg18_example.

Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis.

Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR.

Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively.

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

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

Platform Design Info for The Manufacturer's Name RG_U34A.

Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins.

This package provides QDNAseq bin annotations for the human genome build hg19.

The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats.

Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data.

Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.

This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data.