Javier Cabrera

Rutgers, The State University of New Jersey

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Dhammika Amaratunga

Princeton University

Ziv Shkedy

Hasselt University

Yajie Duan

Rutgers Sexual and Reproductive Health and Rights

Chun Pang Lin

Rutgers, The State University of New Jersey

Helena Geys

Janssen (Belgium)

Dianne Cook

Monash University

Ge Cheng

Ocean University of China

Mariusz Lubomirski

Amgen (United States)

Andreas Buja

University of Pennsylvania

Traymon Beavers

Johnson & Johnson (United States)

Cooperative Institutions

Rutgers, The State University of New Jersey

Janssen (United States)

Johnson & Johnson (United States)

Janssen (Belgium)

Johnson University

Decision Sciences (United States)

Rutgers Sexual and Reproductive Health and Rights

Pfizer (United States)

Cardiovascular Institute of the South

Columbia University

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Outlier Resistance, Standardization, and Modeling Issues for DNA Microarray Data

Birkhäuser Basel eBooks (2001)

Dhammika Amaratunga Javier Cabrera

Gene expression levels for several viral genes placed on DNA microarrays were measured under mock and true infection with the objective of identifying which viral genes are upregulated by infection, i.e., are expressed to a higher degree when exposed to true infection as compared to noninfection. After nonlinearly standardizing the microarrays to a single intensity scale, the average expression level for each gene was estimated resistantly using a double median estimator. Approaches based on mixed effects modeling allows strength to be borrowed across genes to assess the significance of any evidence of upregulation.

10.1007/978-3-0348-8326-9_2

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Citations (7)

Automated Spot Counting in Microbiology

IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023)

Chun Pang Lin Yajie Duan Davit Sargsyan Javier Cabrera Christine M. Livingston

Biological samples are routinely analyzed for microbe concentration. The samples are diluted, loaded onto established host cell cultures, and incubated. If infectious agents are present in the samples, they form circular spots that do not contain the host cells. Each spot is assumed to be originated from a single microbial unit such as a bacterial colony forming unit or viral plaque forming unit. The undiluted sample concentration is estimated by counting the spots and back-calculating. Counting the number of spots by trained technicians is currently the gold standard but it is laborious, subjective, and hard to scale. This paper presents a new automated algorithm for spot counting, Localized and Sequential Thresholding (LoST). Validation studies showed that LoST performance was comparable with manual counting and outperformed several existing tools on images with overlapping spots. The LoST algorithm employs sequential thresholding through a two-stage segmentation and borrows information across all images from the same dilution series to fine-tune the count and identify right censoring. The algorithm increases the efficiency of the spot counting and the quality of the downstream analysis, especially when coupled with an appropriate statistical serial dilution model to enhance the undiluted sample concentration estimation procedure.

Spots

Sample (material)

10.1109/tcbb.2023.3317339

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Citations (2)

Using Fisher's Method to Identify Enriched Gene Sets

Statistics in Biopharmaceutical Research (2014)

Volha Tryputsen Javier Cabrera An De Bondt Dhammika Amaratunga

In gene expression studies where gene-level p-values have been calculated, Fisher's method for pooling p-values (here referred to as MLP for “mean log p”) can be used to identify predefined gene sets that are enriched in the sense that the genes that comprise them have comparatively low p-values. Since gene-level p-values tend not to follow a uniform distribution even in situations that could be regarded as null, a permutation procedure is the most effective way to assess significance. However, this may prove computationally burdensome if a large number of analyses need to be done. In this article, we derive a highly accurate approximation to the permutation p-value that can be used to assess the significance of Fisher's test statistic in a computationally efficient manner. In addition, we show the superiority of this approach compared to methods based on the (regular or weighted) Kolmogorov–Smirnov statistic, which is the basis of the popular GSEA method for gene set enrichment analysis, and Fisher's exact test, which is the basis of several other gene set analysis modalities such as Ingenuity, GoMiner, MAPPFinder, and EASE. We also explore some simple but novel variations of the MLP and find that one of them, MLQ, essentially Fisher's method based on FDR-adjusted p-values or q-values, has comparable performance to MLP for small gene set sizes, but for large gene set sizes, offers noticeable improvement over MLP.

Statistic

Resampling

False Discovery Rate

Pooling

p-value

10.1080/19466315.2014.888013

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Citations (5)

Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure

Journal of Computational and Graphical Statistics (2024)

Traymon Beavers Ge Cheng Yajie Duan Javier Cabrera Mariusz Lubomirski

Big data, with N×P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory or time constraints generated by calculations of order P*N(N−1)2. To circumvent this problem, typically the clustering technique is applied to a random sample drawn from the dataset; however, a weakness is that the structure of the dataset, particularly at the edges, is not necessarily maintained. We propose a new solution through the concept of "data nuggets", which reduces a large dataset into a small collection of nuggets of data, each containing a center, weight, and scale parameter. The data nuggets are then input into algorithms that compute methods such as principal components analysis and clustering in a more computationally efficient manner. We show the consistency of the data nuggets based covariance estimator and apply the methodology of data nuggets to perform exploratory analysis of a flow cytometry dataset containing over one million observations using PCA and K-means clustering for weighted observations. Supplementary materials for this article are available online.

Exploratory data analysis

Clustering high-dimensional data

Hierarchical clustering

10.1080/10618600.2024.2341896

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Citations (1)

Resampling-Based Similarity Measures for High-Dimensional Data

Journal of Computational Biology (2014)

Dhammika Amaratunga Javier Cabrera Yung-Seop Lee

An important issue in classification is the assessment of sample similarity. This is nontrivial in high-dimensional or megavariate datasets--datasets that are comprised of simultaneous measurements on thousands of features, many of which carry little or no information regarding consistent sample differences. Conventional similarity measures do not work particularly well for such data. As an alternative, we propose a distance measure that is based on a refiltering process: at each step of the process a random subset of features is selected and a cluster analysis is performed using only this subset; the relative frequency with which a pair of samples clusters together across several such random subsets forms the similarity measure. The features chosen at any step may be completely random or enriched by awarding the more informative features a higher chance of selection; this enrichment turns out to be particularly effective. We use actual datasets from the burgeoning genomics literature to demonstrate the superior performance of this similarity measure, especially the enriched form of the similarity measure, compared to more conventional measures such as Euclidean distance or correlation, or, if the data are categorical, Hamming distance.

Resampling

Similarity (geometry)

10.1089/cmb.2014.0195

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Citations (6)

A New Projection Pursuit Index for Big Data

arXiv (Cornell University) (2023)

Yajie Duan Javier Cabrera Birol Emir

Visualization of extremely large datasets in static or dynamic form is a huge challenge because most traditional methods cannot deal with big data problems. A new visualization method for big data is proposed based on Projection Pursuit, Guided Tour and Data Nuggets methods, that will help display interesting hidden structures such as clusters, outliers, and other nonlinear structures in big data. The Guided Tour is a dynamic graphical tool for high-dimensional data combining Projection Pursuit and Grand Tour methods. It displays a dynamic sequence of low-dimensional projections obtained by using Projection Pursuit (PP) index functions to navigate the data space. Different PP indices have been developed to detect interesting structures of multivariate data but there are computational problems for big data using the original guided tour with these indices. A new PP index is developed to be computable for big data, with the help of a data compression method called Data Nuggets that reduces large datasets while maintaining the original data structure. Simulation studies are conducted and a real large dataset is used to illustrate the proposed methodology. Static and dynamic graphical tools for big data can be developed based on the proposed PP index to detect nonlinear structures.

Projection pursuit

10.48550/arxiv.2312.06465

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Citations (1)

On Methods for Gene Function Scoring as a Means of Facilitating the Interpretation of Microarray Results

Journal of Computational Biology (2006)

N. R. Srinivasa Raghavan Dhammika Amaratunga Javier Cabrera Alex Nie Jie Qin

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the underlying mechanisms. A few methods have been proposed in the literature to formally convert individual gene results into gene function results. In this paper, we will compare the various methods, propose and examine some new ones, and offer a structured approach to incorporating gene function or pathway information into the analysis of expression data. We study the performance of the various methods and also compare them on real data, using a case study from the toxicogenomics area. Our results show that the approaches based on gene function scores yield a different, and functionally more interpretable, array of genes than methods that rely solely on individual gene scores. They also suggest that functional class scoring methods appear to perform better and more consistently than overrepresentation analysis and distributional score methods.

Toxicogenomics

10.1089/cmb.2006.13.798

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Citations (21)

Some stationary Markov processes in discrete time for unit vectors

Luigi Accardi Javier Cabrera G. S. Watson

Polarity (international relations)

Unit vector

Source

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Citations (10)

Differential function of Ag-specific CD4 T cells from sepsis-induced lymphopenia is influenced by gut microbiota (MPF2P.744)

The Journal of Immunology (2015)

Thomas S. Griffith Javier Cabrera Jeffrey C. Babcock Vladimir P. Badovinac

Abstract Sepsis changes the composition and functional potential of the naïve CD4 T cell repertoire, but the mechanism(s) that control such changes are unknown. We posited that the constituents of the gut flora play an important role in defining the responsiveness of CD4 T cell populations specific for Ag present in the commensal bacterial species that establish polymicrobial sepsis after cecal-ligation and puncture (CLP) versus those CD4 T cells undergoing sepsis-induced Ag-independent homeostatic proliferation. To test this hypothesis, we tracked the loss/recovery and function of CD4 T cells specific for an Ag present in segmented filamentous bacteria (SFB) in mice containing or devoid of this bacteria in their gut. SFB-specific CD4 T cells underwent Ag-driven proliferation in CLP-treated SFB+ mice, but not in SFB- mice. This correlated with their functional ability after secondary infection with SFB Ag-expressing Listeria. Interestingly, we found that a second CD4 T cell population specific for the influenza NP311 epitope underwent differential recovery and function depending on the mouse vendor, suggesting antigenic stimulation as a result of cross-reactivity with some yet-to-be defined epitope expressed by the gut commensal bacteria. Our data suggest the extent of recovery and function of a particular Ag-specific T cell population during sepsis can determined by the intestinal “health” of the individual, regardless of possible genetic similarities.

10.4049/jimmunol.194.supp.63.3

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Analysis of Gene Sets

Wiley series in probability and statistics (2014)

Dhammika Amaratunga Javier Cabrera Ziv Shkedy

Gene sets can be compiled by using one of a number of repositories that store a wide range of current knowledge regarding the network of complex molecular interactions and regulations that constitute living organisms. Several methods have been proposed in the literature for identifying enriched gene sets. This chapter reviews some of the more widely used and preferred methods, roughly pigeonholed as follows: 1. Functional Scoring (FS) Methods. These methods use test statistics that summarize the p-values in the gene set. Examples are the MLP and Fisher's Test, GSEA and the Kolmogorov–Smirnov Test. 2. Overrepresentation Analysis (ORA). These methods use test statistics based on the proportion of genes in the gene set that are declared significant.

Data set

10.1002/9781118364505.ch9

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