Statistical Methods for Overdispersion in mRNA-Seq Count Data
نویسندگان
چکیده
منابع مشابه
Statistical Methods for Overdispersion in mRNA-Seq Count Data
Recent developments in Next-Generation Sequencing (NGS) technologies have opened doors for ultra high throughput sequencing mRNA (mRNA-seq) of the whole transcriptome. mRNA-seq has enabled researchers to comprehensively search for underlying biological determinants of diseases and ultimately discover novel preventive and therapeutic solutions. Unfortunately, given the complexity of mRNA-seq dat...
متن کاملStatistical 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...
متن کاملModeling Zero-Inflated Count Data with Underdispersion and Overdispersion
A common problem in modeling count data is underdispersion or overdispersion. This paper discusses the distinction between overdispersion due to excess zeros and overdispersion due to values that are greater than 0. It shows how to use exploratory data analysis to determine the dispersion patterns and that the dispersion patterns can change depending on the predictors and the subpopulation that...
متن کاملModelling count data with overdispersion and spatial effects
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson distribution are addressed whe...
متن کاملstatistical 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Open Bioinformatics Journal
سال: 2013
ISSN: 1875-0362
DOI: 10.2174/1875036201307010034