Illustration of sparsity of matrix P obtained with NMF.

Plot of sorted coefficients of matrix P, obtained using LR-MVRC, CORR and PCORR matrices with different values of K in NMF.</p

Overlapping communities derived from CORR.

This figure displays the communities derived using CORR based matrix via thresholding of matrix P obtained with NMF (K = 8).</p

Low rank and sparsity constrained method for identifying overlapping functional brain networks - Fig 1

First row: Community structure obtained using LR-MVRC and CORR matrices with NMF and modularity optimization methods. Second row: Community structure obtained using ICA with K = 8 and 15 in the first and second columns, respectively. In the first row, first two columns represent the matrix P obtained from LR-MVRC and CORR with K = 8 and 15 in NMF, respectively. The third column represents modularity optimization results on both LR-MVRC and CORR based adjacency matrices.</p

Overlapping communities derived from LR-MVRC.

This figure displays the communities derived from the LR-MVRC based adjacency matrix via thresholding of matrix P obtained with NMF (K = 8). Eight communities derived from LR-MVRC matrix shown in this figure refer to default mode and subcortical (C1), visual (C2), bilateral limbic (C3), cognitive control and default mode (C4), default mode and visual (C5), auditory and motor (C6), subcortical (C7), and default mode and bilateral limbic (C8) networks.</p

Non overlapping communities derived from LR-MVRC.

This figure displays the communities derived using LR-MVRC based matrix via modularity optimization. Ten communities derived by LR-MVRC shown in this figure refer to motor (C1), visual (C2), default mode and cognitive control (C3), default mode (C4), subcortical (C5), bilateral limbic (C6), default mode and bilateral limbic (C7), cognitive control (C8), subcortical (C9) and auditory networks.</p

Brain networks identified using LR-MVRC with <i>K</i> = 15 in NMF.

Fourth column represent AAL atlas ROI indices belonging to the community K, whereas second and third columns represent their corresponding associated brain networks and number of ROIs in one community, respectively.</p

Brain networks identified using LR-MVRC with <i>K</i> = 8 in NMF.

Fourth column represent AAL atlas ROI indices belonging to the community K, whereas second and third columns represent the associated brain networks and number of ROIs in that community, respectively.</p

Data_Sheet_1_Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways.pdf

Purpose: Gene expression data generated from microarray technology is often analyzed for disease diagnostics and treatment. However, this data suffers with missing values that may lead to inaccurate findings. Since data capture is expensive, time consuming, and is required to be collected from subjects, it is worthwhile to recover missing values instead of re-collecting the data. In this paper, a novel but simple method, namely, DSNN (Doubly Sparse DCT domain with Nuclear Norm minimization) has been proposed for imputing missing values in microarray data. Extensive experiments including pathway enrichment have been carried out on four blood cancer dataset to validate the method as well as to establish the significance of imputation.Methods: A new method, namely, DSNN, was proposed for missing value imputation on gene expression data. Method was validated on four dataset, CLL, AML, MM (Spanish data), and MM (Indian data). All the dataset were downloaded from GEO repository. Missing values were introduced in the original data from 10 to 90% in steps of 10% because method validation requires ground truth. Quantitative results on normalized mean square error (NMSE) between the ground truth and imputed data were computed. To further validate and establish the significance of the proposed imputation method, two experiments were carried out on the data imputed with the proposed method, data imputed with the state-of-art methods, and data with missing values. In the first experiment, classification of normal vs. cancer subjects was carried out. In the second experiment, biological significance of imputation was ascertained by identifying top candidate tumor drivers using the existing state-of-the-art SPARROW algorithm, followed by gene list enrichment analysis on top candidate drivers.Results: Quantitative NMSE results of the DSNN method were compared with three state-of-the-art imputation methods. DSNN method was observed to perform better compared to these other methods both at high as well as low observable data. Experiment-1 demonstrated superior results on classification with imputation compared to that performed on missing data matrix as well as compared to classification on imputed data with existing methods. In experiment-2, cancer affected pathways were discovered with higher significance in the data imputed with the proposed method compared to those discovered with the missing data matrix.Conclusion: Missing value problem in microarray data is a serious problem and can adversely influence downstream analysis. A novel method, namely, DSNN is proposed for missing value imputation. The method is validated quantitatively on the application of classification and biologically by performing pathway enrichment analysis.</p

Statistical results on a total of 364 (260+102) plasma cells.

Statistical results on a total of 364 (260+102) plasma cells.</p

Bhattacharyya distance calculated between different image regions using the ground truth data.

Bhattacharyya distance calculated between different image regions using the ground truth data.</p