Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

The correlation between shape and concentration of silver nanoparticles (AgNPs), their cytotoxicity and formation of reactive oxygen species (ROS) in the presence of electromagnetic fields (EMFs) has been investigated. In addition, the bio-effects caused by the combination of EMFs and graphene nanoparticles (GrNPs) have been also assessed. The AgNPs of three shapes (triangular, spherical and colloidal) and GrNPs were added in high concentrations to the culture of human fibroblasts and exposed to EMF of three different frequencies: 900, 2400 and 7500 MHz. The results demonstrated the dependence of the EMF-induced cytotoxicity on the shape and concentration of AgNPs. The maximal cell killing effect was observed at 900 MHz frequency for NPs of all shapes and concentrations. The highest temperature elevation was observed for GrNPs solution irradiated by EMF of 900 MHz frequency. The exposure to EMF led to significant increase of ROS formation in triangular and colloidal AgNPs solutions. However, no impact of EMF on ROS production was detected for spherical AgNPs. GrNPs demonstrated ROS-protective activity that was dependent on their concentration. Our findings indicate the feasibility to control cytotoxicity of AgNPs by means of EMFs. The effect EMF on the biological activity of AgNPs and GrNPs is reported here for the first time.

Background: Ventricular tachycardia (VT) is a major cause of sudden cardiac death (SCD). Clinical investigations can sometimes fail to identify the underlying cause of VT and the event is classified as idiopathic (iVT). VT contributes significantly to the morbidity and mortality in patients with coronary artery disease (CAD) and dilated cardiomyopathy (DCM). Since mutations in arrhythmia-associated genes frequently determine arrhythmia susceptibility screening for disease-predisposing variants could improve VT diagnostics and prevent SCD in patients. Methods: Ninety-two patients diagnosed with coronary heart disease (CHD), DCM, or iVT were included in our study. We evaluated genetic profiles and variants in known cardiac risk genes by targeted next generation sequencing (NGS) using a newly designed custom panel of 96 genes. We hypothesized that shared morphological and phenotypical features among these subgroups may have an overlapping molecular base. To our knowledge, this was the first study of the deep sequencing of 96 targeted cardiac genes in Kazakhstan. The clinical significance of the sequence variants was interpreted according to the guidelines developed by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) in 2015. The ClinVar and Varsome databases were used to determine the variant classifications. Results: Targeted sequencing and stepwise filtering of the annotated variants identified a total of 307 unique variants in 74 genes, totally 456 variants in the overall study group. We found 168 mutations listed in the Human Genome Mutation Database (HGMD) and another 256 rare/unique variants with elevated pathogenic potential. There was a predominance of high- to intermediate pathogenicity variants in LAMA2, MYBPC3, MYH6, KCNQ1, GAA, and DSG2 in CHD VT patients. Similar frequencies were observed in DCM VT, and iVT patients, pointing to a common molecular disease association. TTN, GAA, LAMA2, and MYBPC3 contained the most variants in the three subgroups which confirm the impact of these genes in the complex pathogenesis of cardiomyopathies and VT. The classification of 307 variants according to ACMG guidelines showed that nine (2.9%) variants could be classified as pathogenic, nine (2.9%) were likely pathogenic, 98 (31.9%) were of uncertain significance, 73 (23.8%) were likely benign, and 118 (38.4%) were benign. CHD VT patients carry rare genetic variants with increased pathogenic potential at a comparable frequency to DCM VT and iVT patients in genes related to sarcomere function, nuclear function, ion flux, and metabolism. Conclusions: In this study we showed that in patients with VT secondary to coronary artery disease, DCM, or idiopathic etiology multiple rare mutations and clinically significant sequence variants in classic cardiac risk genes associated with cardiac channelopathies and cardiomyopathies were found in a similar pattern and at a comparable frequency.

Here, we report the draft genome sequences of two clinical isolates of Mycobacterium tuberculosis (MTB-476 and MTB-489) isolated from sputum of Kazakh patients.

We announce the draft genome sequence of the type strain Lactobacillus rhamnosus CLS17 (2,889,314 nt, with a GC content of 46.8%), which is one of the most prevalent lactic acid bacteria present during the manufacturing process of dairy products; the genome consists of 71 large contigs (>100 bp in size). It contains 2,643 protein-coding sequences, single predicted copies of the 5S, 16S, and 23S rRNA genes, and 51 predicted tRNAs.