arXiv subjects

Rohmatul Fajriyah

Publications and source records attributed to Rohmatul Fajriyah.

3 recordsLinked to original sources

arXiv2015

Generalized beta convolution model of the true intensity for the Illumina BeadArrays

Microarray data come from many steps of production and have been known to contain noise. The pre-processing is implemented to reduce the noise, where the background is corrected. Prior to further analysis, many Illumina BeadArrays users had applied the convolution model, a model which had been adapted from when it was first developed on the Affymetrix platform, to adjust the intensity value: corrected background intensity value. Several models based on different underlying distributions and or parameters estimation methods have been proposed and applied. For instance : the exponential-gamma, the normal-gamma and the exponential-normal convolutions with a maximum likelihood estimation, non-parametric, Bayesian and moment methods of the parameters estimation, including two recent exponential-lognormal and gamma-lognormal convolutions. In this paper, we propose models and derive the corrected background intensity based on the generalized betas and the generalized beta-normal convolutions as a generalization of the existing models.

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arXiv2015

An alternatif test to the two independent samples t test

In this paper we proposed the alternative test to the two independent and normally distributed samples t test based on the cross variance concept. We present the simulation results of the power and the error rate of the special case of the cross variance which is when the variances of the two samples are homogeneous and the t tests. The simulation results show that the special case of the cross variance test has the power and the error type I rate equal to the $t$ test. This result suggests that the proposed test could be used as an alternative to detect whether there is difference between means of the two independent normally distributed samples. We give some example comparative case studies of the special case of the cross variance and the t tests.

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arXiv2013

A study of convolution models for background correction of BeadArrays

The RMA, since its introduction in \cite{Iri03a, Iri03b, Iri06}, has gained popularity among bioinformaticians. It has evolved from the exponential-normal convolution to the gamma-normal convolution, from single to two channels and from the Affymetrix to the Illumina platform. The Illumina design has provided two probe types: the regular and the control probes. This design is very suitable for studying the probability distribution of both and one can apply the convolution model to compute the true intensity estimator. The availability of benchmarking data set at Illumina platform, the {\it Illumina spike-in}, helps researchers to evaluate their proposed method for Illumina BeadArrays. In this paper, we study the existing convolution models for background correction of Illumina BeadArrays in the literature and give a new estimator for the true intensity, where the intensity value is exponentially or gamma distributed and the noise has lognormal distribution. We compare the performance of the models on the Illumina spike-in data set, based on various criteria, for example, root and mean square error, $L_{1}$ error, Kullback-Leibler coefficient, and some adapted criteria from Affycomp \cite{Cop04}. We then provide a simulation study to measure the consistency of the error of background correction and the parametrization. We also study the performance of all models on the FFPE data set. Our study shows that our GLNn model is the optimal one for the benchmarking data set with benchmarking criteria, while the gamma-normal model has the best performance for the benchmarking data set with simulation criteria. At the public data set of FFPE, the gamma-normal and the exponential-gamma models with MLE cannot be used and our proposed models ELN and GLN have the best performance, showing a moderate error in background correction and in the parametrization.

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