The projection score - an evaluation criterion for variable subset selection in PCA visualization

<p>Abstract</p> <p>Background</p> <p>In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.</p> <p>Results</p> <p>We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.</p> <p>Conclusions</p> <p>We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.</p

Coxiella burnetii Phagocytosis Is Regulated by GTPases of the Rho Family and the RhoA Effectors mDia1 and ROCK

The GTPases belonging to the Rho family control the actin cytoskeleton rearrangements needed for particle internalization during phagocytosis. ROCK and mDia1 are downstream effectors of RhoA, a GTPase involved in that process. Coxiella burnetii, the etiologic agent of Q fever, is internalized by the host´s cells in an actin-dependent manner. Nevertheless, the molecular mechanism involved in this process has been poorly characterized. This work analyzes the role of different GTPases of the Rho family and some downstream effectors in the internalization of C. burnetii by phagocytic and non-phagocytic cells. The internalization of C. burnetii into HeLa and RAW cells was significantly inhibited when the cells were treated with Clostridium difficile Toxin B which irreversibly inactivates members of the Rho family. In addition, the internalization was reduced in HeLa cells that overexpressed the dominant negative mutants of RhoA, Rac1 or Cdc42 or that were knocked down for the Rho GTPases. The pharmacological inhibition or the knocking down of ROCK diminished bacterium internalization. Moreover, C. burnetii was less efficiently internalized in HeLa cells overexpressing mDia1-N1, a dominant negative mutant of mDia1, while the overexpression of the constitutively active mutant mDia1-ΔN3 increased bacteria uptake. Interestingly, when HeLa and RAW cells were infected, RhoA, Rac1 and mDia1 were recruited to membrane cell fractions. Our results suggest that the GTPases of the Rho family play an important role in C. burnetii phagocytosis in both HeLa and RAW cells. Additionally, we present evidence that ROCK and mDia1, which are downstream effectors of RhoA, are involved in that processFil: Salinas Ojeda, Romina Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Ortiz Flores, Rodolfo Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Distel, Jesús Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Aguilera, Milton Osmar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Colombo, Maria Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Beron, Walter. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; Argentin

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

Neuronal Chemokines: Versatile Messengers In Central Nervous System Cell Interaction

Whereas chemokines are well known for their ability to induce cell migration, only recently it became evident that chemokines also control a variety of other cell functions and are versatile messengers in the interaction between a diversity of cell types. In the central nervous system (CNS), chemokines are generally found under both physiological and pathological conditions. Whereas many reports describe chemokine expression in astrocytes and microglia and their role in the migration of leukocytes into the CNS, only few studies describe chemokine expression in neurons. Nevertheless, the expression of neuronal chemokines and the corresponding chemokine receptors in CNS cells under physiological and pathological conditions indicates that neuronal chemokines contribute to CNS cell interaction. In this study, we review recent studies describing neuronal chemokine expression and discuss potential roles of neuronal chemokines in neuron–astrocyte, neuron–microglia, and neuron–neuron interaction

A comprehensive overview of radioguided surgery using gamma detection probe technology

The concept of radioguided surgery, which was first developed some 60 years ago, involves the use of a radiation detection probe system for the intraoperative detection of radionuclides. The use of gamma detection probe technology in radioguided surgery has tremendously expanded and has evolved into what is now considered an established discipline within the practice of surgery, revolutionizing the surgical management of many malignancies, including breast cancer, melanoma, and colorectal cancer, as well as the surgical management of parathyroid disease. The impact of radioguided surgery on the surgical management of cancer patients includes providing vital and real-time information to the surgeon regarding the location and extent of disease, as well as regarding the assessment of surgical resection margins. Additionally, it has allowed the surgeon to minimize the surgical invasiveness of many diagnostic and therapeutic procedures, while still maintaining maximum benefit to the cancer patient. In the current review, we have attempted to comprehensively evaluate the history, technical aspects, and clinical applications of radioguided surgery using gamma detection probe technology

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

Objective:Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods:Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results:BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion:ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination