Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies Among Observations

Statistical Modeling of RNA-Seq Data.

Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest. This paper introduces a statistical model for estimating isoform ...

STATISTICAL MODELING OF RNA-SEQ DATA By

Statistical Analysis Methods for the fMRI Data

Functional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. This method can measure little metabolism changes that occur in active part of the brain. We process the fMRI data to be able to find the parts of br...

Supplier selection among alternative scenarios by Data envelopment analysis

A considerable problem in competitive trade world is choosing the best supply chain. As a result in much more serious circumstances of competitions looking for the best supplier for manufacturing, for preparing raw material, is very significant. Meantime suppliers have different scenarios to be fulfilled, such as changing selection variables like lead-time, transportation cost and transportatio...

A Statistical Framework for the Analysis of ChIP-Seq Data.

Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its incr...