Package 'tcc' Title Tcc: Differential Expression Analysis for Tag Count Data with Robust Normalization Strategies
نویسندگان
چکیده
April 26, 2017 Type Package Title TCC: Differential expression analysis for tag count data with robust normalization strategies Version 1.16.0 Author Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer Jianqiang Sun , Tomoaki Nishiyama Description This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. Depends R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports samr Suggests RUnit, BiocGenerics Enhances snow License GPL-2 Copyright Authors listed above biocViews Sequencing, DifferentialExpression, RNASeq NeedsCompilation no
منابع مشابه
Title Tcc: Differential Expression Analysis for Tag Count Data with Robust Normalization Strategies
December 22, 2016 Type Package Title TCC: Differential expression analysis for tag count data with robust normalization strategies Version 1.14.0 Author Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer Jianqiang Sun , Tomoaki Nishiyama Description This package provides a series of functions for performing ...
متن کاملTCC: Differential expression analysis for tag count data with robust normalization strategies
The R/Bioconductor package, TCC, provides users with a robust and accurate framework to perform differential expression (DE) analysis of tag count data. We recently developed a multi-step normalization method (TbT; Kadota et al., 2012 [3]) for two-group RNA-seq data. The strategy (called DEGES) is to remove data that are potential differentially expressed genes (DEGs) before performing the data...
متن کاملارزیابی ارزش تشخیصی روش AgNOR در افتراق ضایعات خوشخیم از بدخیم در سیتولوژی ادرار
This cross-sectional study was carried out to evaluate the usefulness of AgNOR technique in differentiation between benign and malignant cells in urine cytology and to determine the expression of Argyrophilic Nucleolar Organizer Region(AgNOR) proteins as a marker of proliferative activity in benign and malignant urothelial cells. Quantification of AgNORs which was stained by the sil...
متن کاملارتباط میزان بیان پروتئین P53 با درجه بافت شناسی کارسینوم سلول ترانزیشنال مثانه
Background and Aim: Transitional cell carcinoma of bladder (TCC) is a relatively common cancer among the males. The tumor progression is associated with expression or modulation of several gene products that control apoptosis and proliferation. The aim of this study was to assess the relationship between tumor expression of p53 and TCC histologic grade. Materials and Methods: In this prospecti...
متن کاملRNA-Seq data analysis using mulitple statistical algorithms with metaseqR
During the past few years, a lot of R/Bioconductor packages have been developped for the analysis of RNA-Seq data, introducing several approaches. For example, packages using the negative binomial distribution to model the null hypotheses (DESeq, edgeR, NBPSeq) or packages using Bayesian statistics (baySeq, EBSeq). In addition, packages specialized to RNA-Seq data normalization have also been d...
متن کامل