Prospect of cell therapy for Parkinson’s disease

Jeanne Adiwinata Pawitan 대한해부학회 2011 Anatomy & Cell Biology Vol.44 No.4

Th e hallmark of Parkinson’s disease is on-going degeneration of dopaminergic neurons in the substantia nigra, which may be due to various etiologies. Various approaches to alleviate symptoms are available, such as life-long pharmacological intervention, deep brain stimulation, and transplantation of dopaminergic neuron-containing fetal tissue. However, each of these approaches has a disadvantage. Several studies have shown that various kinds of stem cells, induced pluripotent stem cells,and other cells can diff erentiate into dopaminergic neurons and may be promising for treating Parkinson’s disease in the future. Th erefore, this review addresses those cells in terms of their prospects in cell therapy for Parkinson’s disease. In addition, the need for safety and effi cacy studies, various cell delivery modes and sites, and possible side eff ects will be discussed.

Droop Based DC Microgrid Bus Voltage Regulation

Gusti Arif Hanifah Pawitan,Jung-Su Kim 제어로봇시스템학회 2020 제어로봇시스템학회 국내학술대회 논문집 Vol.2020 No.7

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival

Suo, Chen,Hrydziuszko, Olga,Lee, Donghwan,Pramana, Setia,Saputra, Dhany,Joshi, Himanshu,Calza, Stefano,Pawitan, Yudi Oxford University Press 2015 Bioinformatics Vol.31 No.16

<P><B>Motivation:</B> Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures.</P><P><B>Results:</B> An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (<I>P</I> = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer.</P><P><B>Availability and implementation:</B> The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/.</P><P><B>Contact:</B> yudi.pawitan@ki.se</P><P><B>Supplementary information:</B> Supplementary data are available at <I>Bioinformatics</I> online.</P>

Direct Calculation of the Variance of Maximum Penalized Likelihood Estimates via EM Algorithm

Lee, W.,Pawitan, Y. American Statistical Association 2014 The American statistician Vol.68 No.2

Sparse pathway-based prediction models for high-throughput molecular data

Lee, Sangin,Lee, Youngjo,Pawitan, Yudi Elsevier 2018 Computational statistics & data analysis Vol.126 No.-

<P><B>Abstract</B></P> <P>Pathway-based prediction problems for high-throughput molecular data motivate the development of sparsity-constrained models with structured predictive variables. Intuitively it is desirable to incorporate the structural information into the model building procedure, potentially for improving both interpretability and prediction performances. Various random-effect models are developed for structured sparse prediction where the predictive variables/genes can be naturally grouped into overlapping groups or pathways. The hierarchical likelihood approach can be used for these random-effect models that impose sparse selection of the overlapping groups as well as further selection within the selected groups. In addition, the approach leads to a unified optimization algorithm for these random-effect models. Extensive numerical studies based on simulated and real breast-cancer data demonstrate that the proposed methods perform well against existing methods that ignore the structural information.</P>

Sparse Canonical Covariance Analysis for High-throughput Data

Lee, Woojoo,Lee, Donghwan,Lee, Youngjo,Pawitan, Yudi Walter de Gruyter GmbH 2011 Statistical applications in genetics and molecular Vol.10 No.1

<P>Canonical covariance analysis (CCA) has gained popularity as a method for the analysis of two sets of high-dimensional genomic data. However, it is often difficult to interpret the results because canonical vectors are linear combinations of all variables, and the coefficients are typically nonzero. Several sparse CCA methods have recently been proposed for reducing the number of nonzero coefficients, but these existing methods are not satisfactory because they still give too many nonzero coefficients. In this paper, we propose a new random-effect model approach for sparse CCA; the proposed algorithm can adapt arbitrary penalty functions to CCA without much computational demands. Through simulation studies, we compare various penalty functions in terms of the performance of correct model identification. We also develop an extension of sparse CCA to address more than two sets of variables on the same set of observations. We illustrate the method with an analysis of the NCI cancer dataset.</P>

A Selection Operator for Summary Association Statistics Reveals Allelic Heterogeneity of Complex Traits

Ning, Zheng,Lee, Youngjo,Joshi, Peter K.,Wilson, James F.,Pawitan, Yudi,Shen, Xia University of Chicago Press [etc.] 2017 American journal of human genetics Vol.101 No.6

<P>In recent years, as a secondary analysis in genome-wide association studies (GWASs), conditional and joint multiple-SNP analysis (GCTA-COJO) has been successful in allowing the discovery of additional association signals within detected loci. This suggests that many loci mapped in GWASs harbor more than a single causal variant. In order to interpret the underlying mechanism regulating a complex trait of interest in each discovered locus, researchers must assess the magnitude of allelic heterogeneity within the locus. We developed a penalized selection operator for jointly analyzing multiple variants (SOJO) within each mapped locus on the basis of LASSO (least absolute shrinkage and selection operator) regression derived from summary association statistics. We found that, compared to stepwise conditional multiple-SNP analysis, SOJO provided better sensitivity and specificity in predicting the number of alleles associated with complex traits in each locus. SOJO suggested causal variants potentially missed by GCTA-COJO. Compared to using top variants from genome-wide significant loci in GWAS, using SOJO increased the proportion of variance prediction for height by 65% without additional discovery samples or additional loci in the genome. Our empirical results indicate that human height is not only a highly polygenic trait, but also has high allelic heterogeneity within its established hundreds of loci.</P>

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

Alexeyenko, Andrey,Lee, Woojoo,Pernemalm, Maria,Guegan, Justin,Dessen, Philippe,Lazar, Vladimir,Lehtiö,, Janne,Pawitan, Yudi BioMed Central 2012 BMC bioinformatics Vol.13 No.-

<P><B>Background</B></P><P>Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.</P><P><B>Results</B></P><P>We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.</P><P><B>Conclusions</B></P><P>The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.</P>

Identifying and Assessing Interesting Subgroups in a Heterogeneous Population

Lee, Woojoo,Alexeyenko, Andrey,Pernemalm, Maria,Guegan, Justine,Dessen, Philippe,Lazar, Vladimir,Lehtiö,, Janne,Pawitan, Yudi Hindawi Publishing Corporation 2015 BioMed research international Vol.2015 No.-

<P>Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as <I>k</I>-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided.</P>

One CNV Discordance in <i>NRXN1</i> Observed Upon Genome-wide Screening in 38 Pairs of Adult Healthy Monozygotic Twins

Magnusson, Patrik K. E.,Lee, Donghwan,Chen, Xu,Szatkiewicz, Jin,Pramana, Setia,Teo, Shumei,Sullivan, Patrick F.,Feuk, Lars,Pawitan, Yudi Cambridge University Press 2016 TWIN RESEARCH AND HUMAN GENETICS - Vol.19 No.2

<P>Monozygotic (MZ) twins stem from the same single fertilized egg and therefore share all their <I>inherited</I> genetic variation. This is one of the unequivocal facts on which genetic epidemiology and twin studies are based. To what extent this also implies that MZ twins share genotypes in adult tissues is not precisely established, but a common pragmatic assumption is that MZ twins are 100% genetically identical also in adult tissues. During the past decade, this view has been challenged by several reports, with observations of differences in post-zygotic copy number variations (CNVs) between members of the same MZ pair. In this study, we performed a systematic search for differences of CNVs within 38 adult MZ pairs who had been misclassified as dizygotic (DZ) twins by questionnaire-based assessment. Initial scoring by PennCNV suggested a total of 967 CNV discordances. The within-pair correlation in number of CNVs detected was strongly dependent on confidence score filtering and reached a plateau of <I>r</I> = 0.8 when restricting to CNVs detected with confidence score larger than 50. The top-ranked discordances were subsequently selected for validation by quantitative polymerase chain reaction (qPCR), from which one single ~120kb deletion in <I>NRXN1</I> on chromosome 2 (bp 51017111-51136802) was validated. Despite involving an exon, no sign of cognitive/mental consequences was apparent in the affected twin pair, potentially reflecting limited or lack of expression of the transcripts containing this exon in nerve/brain.</P>