「conductor」の検索結果

'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated

日本語の概要は準備中です。原文の説明を表示しています。

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

日本語の概要は準備中です。原文の説明を表示しています。

This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell.

日本語の概要は準備中です。原文の説明を表示しています。

Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many po

日本語の概要は準備中です。原文の説明を表示しています。

The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data.

日本語の概要は準備中です。原文の説明を表示しています。

Uses DESeq2 to estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

日本語の概要は準備中です。原文の説明を表示しています。

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.

日本語の概要は準備中です。原文の説明を表示しています。

Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the

日本語の概要は準備中です。原文の説明を表示しています。

Estimates differential gene expression for short read sequence count using methods appropriate for count data. If you have paired data you may also want to consider Tophat/Cufflinks. Input must be raw count data for each sequence arranged i

日本語の概要は準備中です。原文の説明を表示しています。

A tabular file with gene symbols in the first column, and a ranked statistic (e.g. t-statistic or log fold-change) in the second column

日本語の概要は準備中です。原文の説明を表示しています。

The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.11

日本語の概要は準備中です。原文の説明を表示しています。

This tool fetches microarray data directly from GEO database, based on the GEOQuery R package. Given a GSE accession ID, it returns an Rdata object containing the data and a text file (.cond file) summarizing the conditions of the experimen

日本語の概要は準備中です。原文の説明を表示しています。

This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).

日本語の概要は準備中です。原文の説明を表示しています。

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

日本語の概要は準備中です。原文の説明を表示しています。

Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stoc

日本語の概要は準備中です。原文の説明を表示しています。

Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or fo

日本語の概要は準備中です。原文の説明を表示しています。

performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.

日本語の概要は準備中です。原文の説明を表示しています。

The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.

日本語の概要は準備中です。原文の説明を表示しています。

Methods and models for handling zero-inflated single cell assay data.

日本語の概要は準備中です。原文の説明を表示しています。

The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi

日本語の概要は準備中です。原文の説明を表示しています。

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including

日本語の概要は準備中です。原文の説明を表示しています。

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

日本語の概要は準備中です。原文の説明を表示しています。

This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

日本語の概要は準備中です。原文の説明を表示しています。