A multivariate approach to the integration of multi-omics datasets

Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets

Decoding pooled RNAi screens by means of barcode tiling arrays

<p>Abstract</p> <p>Background</p> <p>RNAi screens via pooled short hairpin RNAs (shRNAs) have recently become a powerful tool for the identification of essential genes in mammalian cells. In the past years, several pooled large-scale shRNA screens have identified a variety of genes involved in cancer cell proliferation. All of those studies employed microarray analysis, utilizing either the shRNA's half hairpin sequence or an additional shRNA-associated 60 nt barcode sequence as a molecular tag. Here we describe a novel method to decode pooled RNAi screens, namely barcode tiling array analysis, and demonstrate how this approach can be used to precisely quantify the abundance of individual shRNAs from a pool.</p> <p>Results</p> <p>We synthesized DNA microarrays with six overlapping 25 nt long tiling probes complementary to each unique 60 nt molecular barcode sequence associated with every shRNA expression construct. By analyzing dilution series of expression constructs we show how our approach allows quantification of shRNA abundance from a pool and how it clearly outperforms the commonly used analysis via the shRNA's half hairpin sequences. We further demonstrate how barcode tiling arrays can be used to predict anti-proliferative effects of individual shRNAs from pooled negative selection screens. Out of a pool of 305 shRNAs, we identified 28 candidate shRNAs to fully or partially impair the viability of the breast carcinoma cell line MDA-MB-231. Individual validation of a subset of eleven shRNA expression constructs with potential inhibitory, as well as non-inhibitory, effects on the cell line proliferation provides further evidence for the accuracy of the barcode tiling approach.</p> <p>Conclusions</p> <p>In summary, we present an improved method for the rapid, quantitative and statistically robust analysis of pooled RNAi screens. Our experimental approach, coupled with commercially available lentiviral vector shRNA libraries, has the potential to greatly facilitate the discovery of putative targets for cancer therapy as well as sensitizers of drug toxicity.</p

Minería de datos para el descubrimiento de patrones en enfermedades respiratorias en Bogotá, Colombia

Trabajo de InvestigaciónEl presente proyecto se basa en la aplicación de minería de datos mediante el algoritmo de clustering K- means que permita la generación de un modelo descriptivo con el análisis de los datos y con el objetivo de identificar posibles comportamientos en enfermedades respiratorias en la ciudad de Bogotá. El conjunto de clústeres generados por la herramienta RapidMiner es la recopilación de datos de un periodo de cinco años de 2012 a 2016, en donde se contemplan el número de casos asociados a 184 diagnósticos de enfermedades respiratorias y la edad de los pacientes corresponde de 0 a 5 años.Trabajo de Investigación1. GENERALIDADES 2. OBJETIVOS 3. JUSTIFICACIÓN 4. DELIMITACIÓN 5. MARCO REFERENCIAL 6. METODOLOGÍA 7. FUENTES DE EXTRACCIÓN Y SUS VARIABLES 8. DISEÑO 9. SELECCIÓN DE ALGORITMOS DE CLUSTERING 10. RECONOCER PATRONES A PARTIR DE LA INFORMACIÓN RECOPILADA 11. CONCLUSIONES 12. TRABAJOS FUTUROS 13. REFERENCIAS BIBLIOGRÁFICAS 14. ANEXOSPregradoIngeniero de Sistema

Quantitative proteome profiling of human myoma and myometrium tissue reveals kinase expression signatures with potential for therapeutic intervention

Uterine leiomyomas are benign tumors affecting a large proportion of the female population. Despite the very high prevalence, the molecular basis for understanding the onset and development of the disease are still poorly understood. In this study, we profiled the proteomes and kinomes of leiomyoma as well as myometrium samples from patients to a depth of >7000 proteins including 200 kinases. Statistical analysis identified a number of molecular signatures distinguishing healthy from diseased tissue. Among these, nine kinases (ADCK4, CDK5, CSNK2B, DDR1, EPHB1, MAP2K2, PRKCB, PRKG1, and RPS6KA5) representing a number of cellular signaling pathways showed particularly strong discrimination potential. Preliminary statistical analysis by receiver operator characteristics plots revealed very good performance for individual kinases (area under the curve, AUC of 0.70-0.94) as well as binary combinations thereof (AUC 0.70-1.00) that might be used to assess the activity of signaling pathways in myomas. Of note, the receptor tyrosine kinase DDR1 holds future potential as a drug target owing to its strong links to collagen signaling and the excessive formation of extracellular matrix typical for leiomyomas in humans

Quantitative proteome profiling of human myoma and myometrium tissue reveals kinase expression signatures with potential for therapeutic intervention

Uterine leiomyomas are benign tumors affecting a large proportion of the female population. Despite the very high prevalence, the molecular basis for understanding the onset and development of the disease are still poorly understood. In this study, we profiled the proteomes and kinomes of leiomyoma as well as myometrium samples from patients to a depth of >7000 proteins including 200 kinases. Statistical analysis identified a number of molecular signatures distinguishing healthy from diseased tissue. Among these, nine kinases (ADCK4, CDK5, CSNK2B, DDR1, EPHB1, MAP2K2, PRKCB, PRKG1, and RPS6KA5) representing a number of cellular signaling pathways showed particularly strong discrimination potential. Preliminary statistical analysis by receiver operator characteristics plots revealed very good performance for individual kinases (area under the curve, AUC of 0.70-0.94) as well as binary combinations thereof (AUC 0.70-1.00) that might be used to assess the activity of signaling pathways in myomas. Of note, the receptor tyrosine kinase DDR1 holds future potential as a drug target owing to its strong links to collagen signaling and the excessive formation of extracellular matrix typical for leiomyomas in humans