Belén San Román

Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling

BackgroundNowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.ResultsThe proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.ConclusionsThis work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Identification of saccadic components in spinocerebellar ataxia applying an independent component analysis algorithm

Anomalies in the oculomotor system are well known symptoms in different neurodegenerative diseases. It has been found that patients suffering from severe spino cerebellar ataxia type 2 show deterioration in the main parameters used to describe saccadic movements, specifically the slowing of horizontal saccadic eye movements. Besides, a combination of two components, named pulse and step, constitutes an accepted model of the saccadic generation system. In the present work, independent component analysis is applied in order to separate both pulse and step components, revealing significant differences in several parameters related to the morphology of these components between patients and control responses. Ten electro-oculographic records of spino cerebellar ataxia type 2 patients and ten control subjects were processed with the proposed algorithm with the aim of obtaining a correct diagnosis. The results obtained from these real experiments reveal the validity of the proposed approach as a classification tool for the diagnosis of this disease.

Computer-Aided Diagnosis of Ataxia SCA-2 Using a Blind Source Separation Algorithm

This work discusses a new approach for ataxia SCA-2 diagnosis based on the application of independent component analysis to the data obtained by electro-oculography in several experiments carried out over healthy and sick subjects. Abnormalities in the oculomotor system are well-known clinical symptoms in patients of several neurodegenerative diseases, including modifications in latency, peak velocity, and deviation in saccadic movements, causing changes in the waveform of the patient response. The changes in the morphology waveform suggest a higher degree of statistic independence in sick patients when compared to healthy individuals regarding the patient response to the visual saccadic stimulus modeled by means of digital generated saccade waveforms. The electro-oculogram records of thirteen patients diagnosed with ataxia SCA2 (a neurodegenerative hereditary disease) and thirteen healthy subjects used as control were processed to extract saccades.

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.

Advances in ataxia SCA-2 diagnosis using independent component analysis

This work discusses a new approach for ataxia SCA-2 diagnosis based in the application of independent component analysis to the data obtained by electro-oculography in several experiments carried out over healthy and sick subjects. Abnormalities in the oculomotor system are well known clinical symptoms in patients of several neurodegenerative diseases, including modifications in latency, peak velocity, and deviation in saccadic movements, causing changes in the waveform of the patient response. The changes in the morphology waveform suggest a higher degree of statistic independence in sick patients when compared to healthy individuals regarding the patient response to the visual saccadic stimulus modeled by means of digital generated saccade waveforms. The electro-oculogram records of six patients diagnosed with ataxia SCA2 (a neurodegenerative hereditary disease) and six healthy subjects used as control were processed to extract saccades.

Independent Component Analysis Aided Diagnosis of Cuban Spino Cerebellar Ataxia 2

Precedent studies have found abnormalities in the oculomotor system in patients with severe SCA2 form of autosomal dominant cerebellar ataxias (ADCA), including the latency, peak velocity, and deviation in saccadic movements, and causing changes in the morphology of the patient response waveform. This different response suggests a higher degree of statistic independence in sick patients when compared to healthy individuals regarding the patient response to the visual saccadic stimulus. We processed electro-oculogram records of six patient diagnosed with severe ataxia SCA2 and six healthy subjects used as control, employing independent component analysis (ICA), significant differences have been found in the statistical independence of the person response with the stimulus for 60° saccadic tests.