This package provides an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

This package identifies regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling.

This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison through each base position across the genome region of interest. The difference is inferred by Fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region.

This package provides a differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test, a Kruskal-Wallis test, a generalized linear model, or a correlation test. All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs.

This is an R package for doublet annotation of single cell RNA sequencing data. scds provides methods to annotate doublets in scRNA-seq data computationally.

This package provides a collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.

This package provides many functions for computing the nonparametric maximum likelihood estimator (NPMLE) for censored and truncated data.

The package contains 8 BAM files, 1 per sequencing run. Each BAM file was obtained by aligning the reads (paired-end) to the full hg19 genome with TopHat2, and then subsetting to keep only alignments on chr14. See accession number E-MTAB-1147 in the ArrayExpress database for details about the experiment, including links to the published study (by Zarnack et al., 2012) and to the FASTQ files.

CopywriteR extracts DNA copy number information from targeted sequencing by utilizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools.

This package provides tools for normalizing and analyzing of GeneChip Mapping 100K and 500K Set. Affymetrix GeneChip Human Mapping 100K and 500K Set allows the DNA copy number mea- surement of respectively 2× 50K and 2× 250K SNPs along the genome. Their high density allows a precise localization of genomic alterations and makes them a powerful tool for cancer and copy number polymorphism study.

The package detects extended diffuse and compact blemishes on microarray chips. Harshlight marks the areas in a collection of chips (affybatch objects). A corrected AffyBatch object will result. The package replaces the defected areas with N/As or the median of the values of the same probe. The new version handles the substitute value as a whole matrix to solve the memory problem.

The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.

This package provides a convenient way to analyze and visualize PICRUSt2 output with pre-defined plots and functions. It allows for generating statistical plots about microbiome functional predictions and offers customization options. It features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. It streamlines the PICRUSt2 analysis and visualization process.

This package provides the output of running Salmon on a set of 24 RNA-seq samples from Alasoo, et al. "Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response", published in Nature Genetics, January 2018.

This package provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.

This package provides routines for parsing Affymetrix data files based upon file format information. The primary focus is on accessing the CEL and CDF file formats.

This is a package for significance analysis of Microarrays for differential expression analysis, RNAseq data and related problems.

This package provides a package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, it can use BAM or BigWig files as input.

The package AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to:

• analyze germline epimutations in the context of multi-generational mutation accumulation lines;

• analyze somatic epimutations in the context of plant development and aging.

The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool.

Gcrma adjusts for background intensities in Affymetrix array data which include optical noise and non-specific binding (NSB). The main function gcrma converts background adjusted probe intensities to expression measures using the same normalization and summarization methods as a Robust Multiarray Average (RMA). Gcrma uses probe sequence information to estimate probe affinity to NSB. The sequence information is summarized in a more complex way than the simple GC content. Instead, the base types (A, T, G or C) at each position along the probe determine the affinity of each probe. The parameters of the position-specific base contributions to the probe affinity is estimated in an NSB experiment in which only NSB but no gene-specific binding is expected.

This package implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.