Between-group analysis of microarray data.

Learning large network (with hundreds of variables) is gaining interest of many researchers with the emergence of high-throughput biological data sources such as micro-array data. In this paper, we investigated the two popular large scale network structure learning algorithms, sparse candidate hill climbing (SCHC) and Grow-Shrinkage(GS) algorithm. The experiments show that in fact both of them have serious effectiveness problems when the number of variables(genes) is large compared to the number of instances(experimental conditions), which is a common case in micro-array data. We further propose a new large scale structure learning algorithm based on Lasso regression. Theoretical analysis in [10] suggested that the L1-norm in lasso regression could make our algorithm especially suitable in the cases that the number of variables and instances is unbalance. Our algorithm achieves much better results than SCHC and GS on the synthetic data. We also show the effectiveness of our algorithm by learning genetic regulatory network modules from a real micro-array data (with more than 6000 genes), combined with the genome-wide location analysis data. The learned results are consistent well with biological knowledge.

<p>Supplementary Figure 1. Validation of the lncRNA microarray data using qRT-PCR. Supplementary Figure 2. Expression levels of DZnep-induced lncRNAs in a panel of HCC cell lines and non-tumor cell lines. Supplementary Figure 3. Expression levels of DZnep-induced lncRNAs in paired human HCC tissues and adjacent non-tumor tissues. Supplementary Figure 4. High specificity of ChIP assay in measuring true interaction between EZH2 and the promoters of EZH2 targeted genes. Supplementary Figure 5. Inhibition of TCAM1P-004 and RP11-598D14.1 with siRNAs promoted cell proliferation. Supplementary Figure 6. Knockdown of TCAM1P-004 and RP11-598D14.1 with shRNAs inhibited cell apoptosis, promoted colony formation ability and transformation ability, and stimulated tumor growth in xenograft mice model. Supplementary Figure 7. Overexpression of TCAM1P-004 and RP11-598D14.1 repressed cell colony formation and transformation abilities, and tumor growth in xenograft mice model. Supplementary Figure 8. Overexpression of TCAM1P-004 or RP11-598D14.1 promoted apoptosis in subcutaneous tumors derived from Hep3B cells. Supplementary Figure 9. Inhibition of TCAM1P-004 or RP11-598D14.1 could induce tumorigenesis of non-tumor MIHA cells. Supplementary Figure 10. KEGG pathway enrichment analysis of TCAM1P-004 and RP11-598D14.1 regulated genes. Supplementary Figure 11. qRT-PCR validated signaling pathways related to TCAM1P-004 and RP11-598D14.1. Supplementary Figure 12. Expression of DDIT and PFKFB4 in HCC cell lines and HCC tumor samples. Supplementary Figure 13. Expression of PFKFB4 in HCC tumor samples. Supplementary Figure 14. Alignment analysis between lncRNA TCAM1P-004 and RP11-598D14.1. Supplementary Figure 15. EZH1 showed no significant role in HCC.</p>

The high dimensionality of global gene expression profiles, where number of variables (genes) is very large compared to the number of observations (samples), presents challenges that affect generalizability and applicability of microarray analysis. Latent variable modeling offers a promising approach to deal with high-dimensional microarray data. The latent variable model is based on a few latent variables that capture most of the gene expression information. Here, we describe how to accomplish a reduction in dimension by a latent variable methodology, which can greatly reduce the number of features used to characterize microarray data. We propose a general latent variable framework for prediction of predefined classes of samples using gene expression profiles from microarray experiments. The framework consists of (i) selection of smaller number of genes that are most differentially expressed between samples, (ii) dimension reduction using hierarchical clustering, where each cluster partition is identified as latent variable, (iii) discretization of gene expression matrix, (iv) fitting the Rasch item response model for genes in each cluster partition to estimate the expression of latent variable, and (v) construction of prediction model with latent variables as covariates to study the relationship between latent variables and phenotype. Two different microarray data sets are used to illustrate a general framework of the approach. We show that the predictive performance of our method is comparable to the current best approach based on an all-gene space. The method is general and can be applied to the other high-dimensional data problems.

The present study investigated the potential interconnection between the place of cultivation of Greek oregano samples and the composition and properties of their essential oils (EOs). In addition, it attempted to identify characteristic chemical features that could differentiate between geographical origins with the use of chemometric tools. To this end, a total of 142 samples of commercially available Greek oregano (Origanum vulgare ssp. hirtum) plants harvested during the calendar years 2017–2018 were obtained for this study. The samples came from five different geographical areas of Greece and represented twelve localities. After appropriate processing, the oregano samples were subjected to hydrodistillation (HD), and the resulting EOs were analyzed for their total phenolic content (TPC), antioxidant activity, and chemical composition. The acquired data were subjected to the chemometric methods of multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) to investigate the potential of classifying the oregano samples in terms of geographical origin. In addition, stepwise LDA (SLDA) was used as a final step to narrow down the number of variables and identify those wielding the highest discriminatory power (marker compounds). Carvacrol was identified as the most abundant component in the majority of samples, with a content ranging from 28.74% to 68.79%, followed by thymol, with a content ranging from 7.39% to 35.22%. The TPC values, as well as the Trolox equivalent antioxidant capacity (TEAC) values, showed no significant variations among the samples, ranging from 74.49 ± 3.57 mg GAE/g EO to 89.03 ± 4.76 mg GAE/g EO, and from 306.83 ± 5.01 μmol TE/g EO to 461.32 ± 7.27 μmol TE/g EO, respectively. The application of the cross-validation method resulted in high correct classification rates in both geographical groups studied (93.3% and 82.7%, respectively), attesting to a strong correlation between location and oregano EO composition.

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Correspondence analysis is generally a data analysis technique that expresses a two-dimensional or higher contingency table in a low-dimensional space to see the combination of rows and columns. In general, a statistical analysis method applied to contingency table analysis uses a chi-square test, but the chi-square test has a limitation in that it cannot show the coupling pattern for rows and columns. As an alternative, correspondence analysis is used. In general, the row profiles of correspondence analysis sum to 1. For data evaluated on the Likert scale for various attributes of each entity, the sum of rows is different for each row, so general correspondence analysis cannot be performed. Correspondence analysis can be applied to such data by doubling the number of column categories by adding the positive and negative values of each attribute so that the sum is equal. This is called the doubling technique. As a result of exploratory multivariate data analysis that does not rely on normal distribution and statistical models for Likert data, a correspondence analysis study using a doubling technique was proposed (Han, 2019). By quantifying the examiners and subjects, and expressing the result using a graphic technique, it was easy to visually recognize and interpret geometrically clear meaning. However, for the method proposed by Han (2019), the stability evaluation for quantification analysis results has not yet been developed. Therefore, in order to develop a methodology for the stability evaluation of the materialistic method for Likert data analysis, which is often seen in real life such as public opinion surveys or consumer preference surveys, but lacks development of analysis methods, and to show its usefulness through case analysis do.

In recent years so many statistical distributions have been derived in the literature. The weighted distributions are widely used in many real-life fields such as medicine, ecology, reliability, etc., for the development of proper statistical model. The concept of double weighted distribution is applied in Exponential-exponential distribution and thus we propose a new double weighted distribution. The statistical properties like mean, variance, coefficient of variation, moments, mode, reliability function, hazard function and the reverse hazard function of double weighted exponential-exponential distribution (DWEED) are explored. The parameters of this distribution are estimated by the maximum likelihood estimation method. The plots of survival function and hazard function of the proposed distribution are also presented. The parameter criterion such as AIC, BIC, AICC, and Kolmogorov- Smirnov test has been used to choose a better fitted probability model. Two cancer data sets are used to demonstrate the superiority and adaptability of the suggested model by comparing it with other models using the above model criterions.

Analysis of high-throughput proteomic/genomic data, in particular, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) data and microarray data, has led to a multitude of techniques aimed at identifying potential biomarkers. Most of the statistical techniques for comparing two groups are based on qualitative measures such as P-value. A quantitative way such as interval estimation for the contrasts of two groups is more appealing. We have devised a simultaneous confidence bands method capable of detecting potential biomarkers, while controlling for overall confidence coverage level, in high-dimensional datasets that discriminate two treatment groups using a permutation scheme. For example, for the SELDI-TOF MS data, we deal with the entire spectrum simultaneously and construct (1 - alpha) confidence bands for the mean differences between groups. Furthermore, peaks were identified based on the maximal differences between the groups as determined by the confidence bands. The analysis method herein described gives both qualitative (P-value) and quantitative data (magnitude of difference). The Clinical Proteomics Programs Databank's ovarian cancer dataset and data from in-house samples containing known spiked-in proteins were analyzed. We were able to identify potential biomarkers similar to those described in previous analysis of the ovarian cancer data, however, while these markers are highly significant between cancer and normal groups, our analysis indicated the absolute difference between the two groups was minimal. In addition, we found additional markers than those previously described with greater differences in average intensities. The proposed confidence bands method successfully detected the spiked-in peaks, as well as, secondary peaks generated by adducts and double-charged species. We also illustrate our method utilizing paired gene expression data from a prostate cancer microarray experiment by constructing confidence bands for the fold changes between cancer and normal samples. R-package, 'seie.zip' (license: GNU GPL), is publiclly available at http://research2.dfci.harvard.edu/dfci/MS_spike-in_data/

cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm in the study of molecular biology. One of the significant challenges in this genomic revolution is to develop sophisticated approaches to facilitate the visualization, analysis, and interpretation of the vast amounts of multi-dimensional gene expression data. We have applied self-organizing map (SOM) in order to meet these challenges. In essence, we utilize U-matrix and component planes in microarray data visualization and introduce general procedure for assessing significance for a cluster detected from U-matrix. Our case studies consist of two data sets. First, we have analyzed a data set containing 13,824 genes in 14 breast cancer cell lines. In the second case we show an example of the SOM in drug treatment of prostate cancer cells. Our results indicate that (1) SOM is capable of helping finding certain biologically meaningful clusters, (2) clustering algorithms could be used for finding a set of potential predictor genes for classification purposes, and (3) comparison and visualization of the effects of different drugs is straightforward with the SOM. In summary, the SOM provides an excellent format for visualization and analysis of gene microarray data, and is likely to facilitate extraction of biologically and medically useful information.

Figure 1 Experimental design of the candidate gene prioritization process.Information filtering is organized in three levels.At level 1, all the 142 genes are considered in the analysis.At the end of level 1, three sets of genes are obtained: 1) genes that are part of relevant GO categories, 2) genes for which there is significant amount of literature, and 3) genes that are part of enriched GO categories.All uncharacterized genes from the three lists are extracted and passed to the second level of prioritization.In addition, genes with at least two supporting evidences will also be forwarded to the second level of exploration.The filtered gene set from level 2 is used as input in level 3, where physical and genetic interaction among these genes are further explored.The resulting sets of genes will be the uncharacterized genes and the genes with at least two supporting evidences, which are then prioritized further if they are interrelated with at least one physical or genetic interaction.

Dimension reduction is an important issue to understand microarray data. In this study, we proposed a efficient approach for dimensionality reduction of microarray data. Our method allows to apply the manifold learning algorithm to analyses dimensionality reduction of microarray data. The intra-/inter-category distances were used as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Colon cancer and leukaemia gene expression datasets are selected for our investigation. When the neighborhood parameter was effectivly set, all the intrinsic dimension numbers of data sets were low. Therefore, manifold learning is used to study microarray data in the low-dimensional projection space. Our results indicate that Manifold learning method possesses better effects than the linear methods in analysis of microarray data, which is suitable for clinical diagnosis and other medical applications.

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Oligonucleotide and DNA arrays, or microarrays, have proven to be useful tools to investigate biological function, and are the focal point of an increasing number of studies. However, most papers shed little light on the underlying basis and best use of microarray technology, often leaving a number of important questions unanswered. Under what conditions are microarrays helpful? How should microarray data be analyzed? What data analysis methods should be avoided? Which biological questions can microarrays address? Which biological questions are not best answered by microarrays? Here, we examine the technology itself, the data produced, proper experimental design, data analysis techniques, and experimental validation. These are issues important to all users of DNA arrays, from the mathematician who may have only limited knowledge of the biology behind the technology, to the biologist who is concerned with experimental design and the details of data analysis. Finally, we stress the importance of great experimental care, sample and data triage, well-characterized and rigorous analysis, and the need for appropriate follow-up and verification, especially when using animal or human tissue.

Heterogeneity is a hallmark of cancer and perhaps one of the most important features associated with resistance to therapies and likelihood of recurrence and/or metastasis. Genomic instability contributes to genetic diversity, which leads to high levels of intratumoural heterogeneity. While genetic diversity is one contributor to phenotypic heterogeneity in tumors, the spatial variation in the tumor microenvironment (TME) also drives emergent phenotypic heterogeneity and intrinsic variation in resistance to drugs and the immune system. Ultimately, heterogeneity leads to higher likelihood of metastasis. Traditionally, pathology allowed for the assessment of heterogeneity. However, this was limited to gross assessment of the TME, through measurement of mitotic levels and severity of cancer invasion. We present SimBioSys PhenoScope, a multi-scale analysis and visualization platform that integrates cancer data across scales to extract cross modality trends that drive cancer invasion. As a demonstrated use case for the platform, we present a vignette of using the platform to analyze pathology slides at three scales. Three convolutional neural networks (CNN) are developed and validated. The outputs of these networks were combined with 2D simulations of the metabolic behavior and growth of cells within the TME. Two CNNs were developed and one implemented: one that identifies cells undergoing mitosis, one that segments individual cells and classifies their type, and one that segments five tissues from pathology slides. Additionally, transcriptional data was used to generate patient specific metabolic models. Each CNN was developed using training images and validated on the test images. The mitosis detection CNN was found to have an accuracy of 76.2% (precision=83%, recall=76%) in the test set. The classification CNN was found to have a Dice Similarity Coefficient (DSC) of 0.821 for segmenting cells and an F1 score for classifying cells ranging from 0.559 (F1i) to 0.756 (F1d). The segmentation CNN was found to have an accuracy of 78.1% with DSC ranging from 0.66 and 0.86 depending on tissue. The segmentations were input into a proprietary simulation framework along with patient-specific metabolic models to predict the spatial gradients of nutrients, and spatial organization of growth and metabolism. Simulations show multiple behaviors such as regions of high lactate production or consumption by cancer, and regions that differ by lactate production and alanine uptake in cancers. These behaviors were correlated with local cell mitoses and invasion of Tumor Infiltrating Lymphocytes. Tools to examine cancers across scales and within the TME are currently lacking. We demonstrate a proof-of-principle approach of combining data across scales in a fashion that allows for novel predictions of TME behavior. Citation Format: Joseph R. Peterson, Matthew Shin, Patricia Carrigan, Michael J. Hallock, Snehal Patel, The SimBioSys Team. A multi-scale analysis and visualization platform for cancer data - deriving tumor microenvironment behavior from pathology and transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 469.

Correspondence analysis (CA) is a method of data visualization that is applicable to cross‐tabular data such as counts, compositions, or any ratio‐scale data where relative values are of interest. All the data should be on the same scale and the row and column margins of the table must make sense as weighting factors because the analysis gives varying importance to the respective rows and columns according to these margins. This method is one of a large class of methods based on the singular value decomposition and can be considered as the equivalent of principal component analysis for categorical and ratio‐scale data or as a pair of classical scalings of the rows and columns based on their interpoint χ2 distances, using the margins as weights. For categorical data, this method generalizes to multiple CA, a popular method for analyzing questionnaire data. A linearly constrained form of CA, canonical CA, is extensively used in ecological research where species abundance data at various sampling points are visualized subject to being linearly related to environmental variables measured at the same locations. When certain parameters are introduced into its definition, CA has been shown to have limiting cases of unweighted and weighted log‐ratio analysis (the latter also known as the spectral map), as well as classical multidimensional scaling. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis