77,192 research outputs found
Matrix eQTL: Ultra fast eQTL analysis via large matrix operations
Expression quantitative trait loci (eQTL) mapping aims to determine genomic
regions that regulate gene transcription. Expression QTL is used to study the
regulatory structure of normal tissues and to search for genetic factors in
complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL
dataset contains millions of SNPs and thousands of transcripts measured for
hundreds of samples. This makes the analysis computationally complex as it
involves independent testing for association for every transcript-SNP pair. The
heavy computational burden makes eQTL analysis less popular, often forces
analysts to restrict their attention to just a subset of transcripts and SNPs.
As larger genotype and gene expression datasets become available, the demand
for fast tools for eQTL analysis increases. We present a new method for fast
eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and
test for association using both linear regression and ANOVA models. The models
can include covariates to account for such factors as population structure,
gender, and clinical variables. It also supports testing of heteroscedastic
models and models with correlated errors. In our experiment on large datasets
Matrix eQTL was thousands of times faster than the existing popular software
for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages
and thus can easily be run on Windows, Mac OS, and Linux systems. The software
is freely available at the following address:
http://www.bios.unc.edu/research/genomic_software/Matrix_eQTLComment: 9 pages, 1 figur
An automatic adaptive method to combine summary statistics in approximate Bayesian computation
To infer the parameters of mechanistic models with intractable likelihoods,
techniques such as approximate Bayesian computation (ABC) are increasingly
being adopted. One of the main disadvantages of ABC in practical situations,
however, is that parameter inference must generally rely on summary statistics
of the data. This is particularly the case for problems involving
high-dimensional data, such as biological imaging experiments. However, some
summary statistics contain more information about parameters of interest than
others, and it is not always clear how to weight their contributions within the
ABC framework. We address this problem by developing an automatic, adaptive
algorithm that chooses weights for each summary statistic. Our algorithm aims
to maximize the distance between the prior and the approximate posterior by
automatically adapting the weights within the ABC distance function.
Computationally, we use a nearest neighbour estimator of the distance between
distributions. We justify the algorithm theoretically based on properties of
the nearest neighbour distance estimator. To demonstrate the effectiveness of
our algorithm, we apply it to a variety of test problems, including several
stochastic models of biochemical reaction networks, and a spatial model of
diffusion, and compare our results with existing algorithms
Multivariate Statistical Process Control Charts: An Overview
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data
Since most analysis software for genome-wide association studies (GWAS)
currently exploit only unrelated individuals, there is a need for efficient
applications that can handle general pedigree data or mixtures of both
population and pedigree data. Even data sets thought to consist of only
unrelated individuals may include cryptic relationships that can lead to false
positives if not discovered and controlled for. In addition, family designs
possess compelling advantages. They are better equipped to detect rare
variants, control for population stratification, and facilitate the study of
parent-of-origin effects. Pedigrees selected for extreme trait values often
segregate a single gene with strong effect. Finally, many pedigrees are
available as an important legacy from the era of linkage analysis.
Unfortunately, pedigree likelihoods are notoriously hard to compute. In this
paper we re-examine the computational bottlenecks and implement ultra-fast
pedigree-based GWAS analysis. Kinship coefficients can either be based on
explicitly provided pedigrees or automatically estimated from dense markers.
Our strategy (a) works for random sample data, pedigree data, or a mix of both;
(b) entails no loss of power; (c) allows for any number of covariate
adjustments, including correction for population stratification; (d) allows for
testing SNPs under additive, dominant, and recessive models; and (e)
accommodates both univariate and multivariate quantitative traits. On a typical
personal computer (6 CPU cores at 2.67 GHz), analyzing a univariate HDL
(high-density lipoprotein) trait from the San Antonio Family Heart Study
(935,392 SNPs on 1357 individuals in 124 pedigrees) takes less than 2 minutes
and 1.5 GB of memory. Complete multivariate QTL analysis of the three
time-points of the longitudinal HDL multivariate trait takes less than 5
minutes and 1.5 GB of memory
Three phase boost rectifier design
An electric power can be converted from one form to another form by using power
electronics devices. The function of power electronics circuits by using semiconductor
devices as switch is modifying or controlling a voltage. The goal of power electronics
circuits are to convert electrical energy from one form to another, from source to load
with highest efficiency, high availability and high reliability with the lowest cost,
smallest size and weight. The term rectification refers to the power circuit whose
function is to alter the ac characteristic of the line electric power to produce a
“rectified”ac power at the load side that contain the dc value In this project, a study has
done for the two types of rectifier topology of alternating current to direct current
voltage of a three-phase boost rectifier with pulse width modulation (PWM) and a threephase
boost
rectifier
with
active
power
filter
(APF).
Power
factor,
shape
distortion
and
voltage
can be increased as much as seen through two types of this topology if it is
connected to the non-linear loads in power systems. Three phase rectifier with pulsewidth
modulation (PWM) is one of controlled rectifier consist six pulses divides into
two groups which are top group and bottom group. For top group, IGBT with its
collector at the highest potential will conduct at one time. The other two will be
reversed. Thus for bottom group, IGBT with the its emitter at the lowest potential will
conduct. This project also observes the current, voltage waveform and the harmonics
component when the active power filter (AFC) placed in series with non-linear load.
Type of rectifier used is uncontrolled rectifier. In this work MATLAB/SIMULINK
power system toolbox is used to simulate the system Results of simulations carried out,
the advantages and disadvantages, the increase in voltage and waveform distortion for
the system under consideration can be show
- …