Juan Cruz Rodriguez, Ph.D.

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together… Mostrar más

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation.
Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers. Mostrar menos

ShinyWYSIWYG is a visual editor that allows the , using the drag and drop technique, inserting the various fields that Shiny provides as input and output, by selecting the desired size and position for each field. Once the interface is generated, ShinyWYSIWYG also eases the development of the server logic. For each event that the wishes to generate, the input field that triggers the action must be specified, the code to be executed, the input variables that it will use, and if… Mostrar más

ShinyWYSIWYG is a visual editor that allows the , using the drag and drop technique, inserting the various fields that Shiny provides as input and output, by selecting the desired size and position for each field. Once the interface is generated, ShinyWYSIWYG also eases the development of the server logic. For each event that the wishes to generate, the input field that triggers the action must be specified, the code to be executed, the input variables that it will use, and if desired, which is the output field where the results will be rendered. ShinyWYSIWYG also allows using global variables and adding code that must be executed before running the server (application’s global code). Once the GUI design is complete, ShinyWYSIWYG provides the complete R code that generates it. Then the should only have to copy
and paste the code and run its Shiny application. ShinyWYSIWYG also allows loading previously saved projects. ShinyWYSIWYG is entirely developed in R using the Shiny, ggplot2, and shinyjs libraries. It works independently of R and Shiny versions. It is freely available at github.com/jcrodriguez1989/shinyWYSIWYG, where is also an example to recreate, by ShinyWYSIWYG, the Shiny \01 hello" application. Mostrar menos

Transcriptomic analysis is essential for detecting biological alterations. At the present, this data is obtained mainly through two technologies: microarrays and RNASeq. However, these technologies present a difference in the statistical distribution of their resulting expression data.
Although the first step in this type of analysis is the detection of differentially expressed genes, which has been extensively studied for both types of data. It is essential to carry out a deeper analysis… Mostrar más

Transcriptomic analysis is essential for detecting biological alterations. At the present, this data is obtained mainly through two technologies: microarrays and RNASeq. However, these technologies present a difference in the statistical distribution of their resulting expression data.
Although the first step in this type of analysis is the detection of differentially expressed genes, which has been extensively studied for both types of data. It is essential to carry out a deeper analysis, known as functional analysis. At present, there are no scientific studies that give a recommendation on which differentially expressed genes detection method to use when feeding functional analysis.
In the present work, the most commonly used methods are compared, from the point of view of functional analysis results. Moreover, similarities and differences between results coming from microarrays and RNA-Seq data are studied.
The results indicate that the best alternative when performing functional analysis from RNA-Seq data is Voom+Limma. Further, it is shown that both technologies provide results in common, but in addition, each one is able to focus more strongly whether on more specific or general gene sets. Mostrar menos

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in of… Mostrar más

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Mostrar menos

Transcriptome analysis is essential to elucidate phenotype biological changes, where the detection of differentially expressed (DE) genes is the starting point; for a comprehensive understanding, functional analysis (FA) turns crucial. One of the most used methods of FA is the Over­ Representation Analysis (ORA), which is fed with a list of candidate genes. The development of the RNA­Seq technology and large screening sequencing projects as TCGA are providing new challenges for both DE genes… Mostrar más

Transcriptome analysis is essential to elucidate phenotype biological changes, where the detection of differentially expressed (DE) genes is the starting point; for a comprehensive understanding, functional analysis (FA) turns crucial. One of the most used methods of FA is the Over­ Representation Analysis (ORA), which is fed with a list of candidate genes. The development of the RNA­Seq technology and large screening sequencing projects as TCGA are providing new challenges for both DE genes detection (DEGD) and FA. It is known that the DEGD is affected by the used method thus affecting the FA. Despite this, DEGD methods were mainly compared in of statistical accuracy or genes detected, but their impact on FA from a biological point of view has not been addressed so far. In this work we evaluate the impact of the most used DEGD methods for RNA­Seq data on ORA results. For this, the well known TCGA breast cancer cohort was used. Since there is no gold standard and simulated data lack biological information, the MicroArray (MA) data was used as reference, since this kind of data was widely used and analyzed in of DEGD and ORA. Breast Cancer RNA­Seq and MA expression data were ed from the TCGA repository. Subjects were classified as Basal­Like, Her2, Luminal A and Luminal B subtypes by means of the PAM50 algorithm. Only those subjects who agreed classification on both RNA­Seq and MA data were used, and those genes that were reliably detected in both matrices were kept. Then, all pairwise combinations of subtypes (six) were compared for DEGD and subsequent ORA. In each case, to feed the ORA, DE genes were obtained using the three most DEGD commonly used methods of RNA­Seq, i.e., edgeR, DESeq2 and Voom+limma. The three edgeR gene dispersion estimation methods, i.e., common, trended and tagwise, were also compared. The Gene Ontology gene sets were used for enrichment test by ORA. Mostrar menos

Gene expression analysis does not end in a list of differentially expressed (DE) genes, but requires a comprehensive functional analysis (FA) of the underlying molecular mechanisms. Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology (GO) are the most used FA approaches. Several statistical methods have been developed and compared in of computational efficiency and/or appropriateness. However, none of them were evaluated from a biological point of view or in … Mostrar más

Gene expression analysis does not end in a list of differentially expressed (DE) genes, but requires a comprehensive functional analysis (FA) of the underlying molecular mechanisms. Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology (GO) are the most used FA approaches. Several statistical methods have been developed and compared in of computational efficiency and/or appropriateness. However, none of them were evaluated from a biological point of view or in of consistency on information retrieval. In this context, questions regarding “are methods comparable?”, “is one of them preferable to the others?”, “how sensitive are they to different parameterizations?” All of them are crucial questions to face prior choosing a FA tool and they have not been, up to now, fully addressed.

In this work we evaluate and compare the effect of different methods and parameters from an information retrieval point of view in both GSEA and SEA under GO. Several experiments comparing breast cancer subtypes with known different outcome (i.e. Basal-Like vs. Luminal A) were analyzed. We show that GSEA could lead to very different results according to the used statistic, model and parameters. We also show that GSEA and SEA results are fairly overlapped, indeed they complement each other. Also an integrative framework is proposed to provide complementary and a stable enrichment information according to the analyzed datasets. Mostrar menos

El control de calidad es una etapa fundamental para remover artefactos técnicos en datos de expresión de genes obtenidos a través de microarreglos de ADN. Los abordajes tradicionales sólo emplean estrategias univariadas, con el fin de comprobar el supuesto de normalidad global de los genes, al igual que para obtener valores de expresión comparables entre las diferentes muestras. No obstante, los anteriores presentan dos falencias: i) no incluyen la información del diseño experimental en el… Mostrar más

El control de calidad es una etapa fundamental para remover artefactos técnicos en datos de expresión de genes obtenidos a través de microarreglos de ADN. Los abordajes tradicionales sólo emplean estrategias univariadas, con el fin de comprobar el supuesto de normalidad global de los genes, al igual que para obtener valores de expresión comparables entre las diferentes muestras. No obstante, los anteriores presentan dos falencias: i) no incluyen la información del diseño experimental en el control de calidad; ii) la exploración de los genes se realiza de forma univariada. En este contexto, se presenta como alternativa para el control de calidad, descomponer los valores observados de expresión utilizando la información del diseño experimental a través de un análisis de la varianza (ANOVA). Luego, la contribución de los diferentes factores puede ser analizada de forma multivariada mediante dos técnicas de exploración como los son el Análisis de Componentes Principales (PCA por sus siglas en inglés) y la regresión de Mínimos Cuadrados Parciales (PLS por sus siglas en inglés). La estrategia propuesta ha sido aplicada con éxito sobre un experimento de microarreglos de dos factores. Con ella se ha logrado detectar y remover artefactos no considerados en el diseño experimental, que no pudieron ser detectados ni removidos por los abordajes tradicionales. Mostrar menos