Platform Design Info for The Manufacturer's Name Rat230_2.

Platform Design Info for NimbleGen feinberg_hg18_me_hx1.

PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation.

Demultiplexing and filtering utilities intended for reads with combinatorial barcodes (i.e. PETRI-seq and SPLiT-seq). The demultiplexer algorithm uses the position of the segments to extract and compare the barcodes with the reference (whitelist). A Shiny application is provided to interactively select cutoffs for which barcode combinations to keep.

preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line.

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 The Manufacturer's Name Sugar_Cane.

Platform Design Info for Affymetrix miRNA-4_0.

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

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

This package provides a Bioconductor data package for the Grasso (2012) Prostate Cancer dataset.

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.

Platform Design Info for NimbleGen charm_hg18_example.

Platform Design Info for Affymetrix Clariom_S_Human_HT.

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

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

Platform Design Info for The Manufacturer's Name DrosGenome1.

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

Platform Design Info for Affymetrix miRNA-3_1.

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

Platform Design Info for The Manufacturer's Name Mu11KsubB.

This package provides a DelayedArray interface for plink bed files. There is support for interfacing to plink genotype data via RangedSummarizedExperiment. Example data from the GEUVADIS project (internationalgenome.org) are used for demonstration.

Platform Design Info for The Manufacturer's Name Mouse430_2.

Platform Design Info for The Manufacturer's Name HT_HG-U133_Plus_PM.