Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.

The relationship between the psychological resilience and post-traumatic growth of college students during the COVID-19 pandemic: a model of conditioned processes mediated by negative emotions and moderated by deliberate rumination.

BACKGROUND: The mental health of university students during the COVID-19 pandemic has attracted the attention of researchers. For the present study researchers constructed a mediation model to explore the relationship between psychological resilience and post-traumatic growth, the mediating role of negative emotions and the moderating role of deliberate rumination in students. METHODS: The Psychological Resilience Scale, Posttraumatic Growth Inventory, Depression-Anxiety-Stress Scale (DASS-21) and Event Related Rumination Inventory were used in a survey of 881 college students. The data were analyzed using SPSS 26.0 and the PROCESS plugin (version 3.3). RESULTS: (1) Psychological resilience is positively related with post-traumatic growth. Deliberate rumination is positively related to psychological resilience, posttraumatic growth, and negative emotions. Psychological resilience, post-traumatic growth and negative emotions are negatively related. (2) Negative emotions mediated the relationship between psychological resilience and post-traumatic growth. (3) Deliberate rumination plays a moderating role in psychological resilience affecting negative emotions. Deliberate rumination plays a moderating role in the extent to which psychological resilience influences PTG through negative emotions. CONCLUSIONS: Psychological resilience affects post-traumatic growth directly and also indirectly through negative emotions. With the increase of mental resilience, the level of negative emotion tended to decrease. When individuals are experiencing negative emotions, high levels of active rumination are more likely to promote post-traumatic growth. This study helps to explore the factors affecting the mental health of college students during the epidemic, thus providing guidance for appropriate mental health interventions.

A new genomic framework to categorize pediatric acute myeloid leukemia.

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 887 pAML into 23 mutually distinct molecular categories, including new major entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3 or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a new prognostic framework for pAML based on these updated molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Proposal of a new genomic framework for categorization of pediatric acute myeloid leukemia associated with prognosis.

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 895 pAML into 23 molecular categories that are mutually distinct from one another, including new entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3, or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a prognostic framework for pAML based on molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

siRNA-mediated knockdown of two tyrosinase genes from Schistosoma japonicum cultured in vitro.

The cross-linking process of eggshell proteins in helminths is dependent on the activities of tyrosinases (TYRs), which can be inhibited by phenol oxidase inhibitors. Two genes encoding TYRs, SjTYR1 and SjTYR2, have been identified in Schistosoma japonicum. In this study, siRNA-mediated RNA interference (RNAi) was performed to silence these two SjTYR genes to evaluate their roles in eggshell formation. The effects of individual or double knockdown of the SjTYR genes were compared by determining SjTYR1/SjTYR2 transcript levels, enzyme activities, and by observing the morphology and amounts of intrauterine eggs. Results showed that SjTYR transcript levels were significantly reduced on the 3rd day post-RNAi. Significant reductions in TYR enzyme activities, as well as obvious changes in morphology and the number of intrauterine eggs followed the reductions in SjTYR transcript levels. On the 8th day after simultaneous knockdown of both SjTYR genes, which effected a 40% reduction in SjTYR1 transcript level and a 59% reduction in SjTYR2 transcript level, we observed an 80% reduction in diphenol oxidase (DPO) activity of TYRs, and a 74% reduction in the number of normal eggs in female uteri. Knockdown of both SjTYR genes has a greater effect than single knockdown of the SjTYR genes. These results demonstrate that both SjTYRs play an important role in eggshell sclerotization of S. japonicum, and that their enzyme activities depend on the transcript levels of two SjTYR genes.

Integration of differential expression and network structure for 'omics data analysis.

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

Characterization and expression of a novel cystatin gene from Schistosoma japonicum.

Cystatins are a family of cysteine protease inhibitors that play a crucial role in the immune evasion from their host and in the adaptation to host defence. Here, we isolated a full-length cDNA sequence inferred to encode a novel cystatin gene from a blood fluke, Schistosoma japonicum. The cDNA, designated SjCystatin, comprised an open reading frame (ORF) of 306 bp, and encoded 101 amino acids with a predicted molecular weight of 11.3 kDa. This predicted protein shared a significant degree of sequence identity with the type I cystatin (stefin) of Schistosoma mansoni and Homo sapiens. These proteins exhibited a typical cystatin topology, including the absence of disulfide bonds and three conserved catalytic motifs, Gly at the N-terminus (Gly(6)), Gln-X-Val-X-Gly motif (Q(49)VVAG(53)) and an LP pair at the C-terminus (L(76)P(77)). The SjCystatin gene spanned 376 bp and contained three exons. The positions of two introns were conserved between the cystatin genes of trematodes and their vertebrate hosts. Reverse transcription polymerase chain reaction confirmed the transcription of SjCystatin in the egg, schistosomula and adult stages of S. japonicum. The encoding ORF region was cloned into pET-28a (+) prokaryotic expression vector. After purification, the recombinant protein SjCystatin (recSjCystatin), expressed in Escherichia coli, was used to immunize animals and produce its specific polyclonal antibody. Western blot analysis revealed that the native SjCystatin was expressed in the egg and adult stages. The enzyme activity assay of the recSjCystatin showed that it inhibited the proteolytic activity of papain. SjCystatin protein was mainly localized on the miracidium within eggs. Immunohistochemistry revealed that SjCystatin mainly localized in the epithelial cells lining the gut as well as the tegument on the surface of adult worms. The conserved genomic DNA structure among cystatin homologues of trematode and their vertebrate host emphasized the characteristics of compatibility between parasites and their hosts. This study provides the first insight into the gene and protein of S. japonicum cystatin and a basis for a further understanding the functions of this gene.

Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.

Identification of novel molecular subtypes of disease using multi-source 'omics data is an active area of on-going research. Integrative clustering is a powerful approach to identify latent subtype structure inherent in the data sets accounting for both between and within data correlations. We propose a new integrative network-based clustering method using the non-negative matrix factorization, nNMF, for clustering multiple types of interrelated datasets assayed on same tumor-samples. nNMF utilizes the consensus matrices generated using the non-negative matrix factorization (NMF) algorithm on each type of data as networks among the patient samples. The multiple networks are then combined, and a comprehensive network structure is created optimizing the strengths of the relationships. A spectral clustering algorithm is then used on the final network data to determine the cluster groups. nNMF is a non-parametric method and therefore prior assumptions on the statistical distribution of data is not required. The application of the proposed nNMF method has been provided with simulated and the real-life datasets obtained from The Cancer Genome Atlas studies on glioblastoma, lower grade glioma and head and neck cancer. nNMF was found to be working competitively with previous methods and sometimes better as compared to previous NMF or model-based method especially when the signal to noise ratio is small. The novel nNMF method allows researchers to utilize such relationships to identify the latent subtype structure inherent in the data so that further association studies can be carried out. The R program for the nNMF will be available upon request.

Inferior Glenohumeral Ligament (IGHL) Injuries: A Case Series of Magnetic Resonance (MR) Imaging Findings and Arthroscopic Correlation.

INTRODUCTION: The inferior glenohumeral ligament (IGHL) complex commonly is assessed by both magnetic resonance imaging (MRI) and magnetic resonance (MR) arthrogram. Our study compared the accuracy of MR arthrogram compared to MRI using arthroscopic correlation as the gold standard. METHODS: A retrospective review of cases reporting an IGHL injury was performed. Seventy-seven cases met inclusion criteria, while five had arthroscopic reports that directly confirmed or refuted the presence of IGHL injury. Two arthroscopic reports confirmed concordant IGHL injuries, while three arthroscopic reports mentioned discordant findings compared to MR. All three discordant cases involved MR arthrogram. Findings included soft tissue edema, fraying of the axillary pouch fibers, and cortical irregularity of the humeral neck. Of the two concordant cases, one was diagnosed by MRI, revealing an avulsion of the anterior band, while the second was diagnosed by MR arthrogram showing ill-defined anterior band fibers. Many cases involved rotator cuff or labral tears, which may have been the focus of care for providers, given their importance for shoulder stability. Additionally, a lack of diagnostic confidence in MR reports may have influenced surgeons in the degree to which they assessed the IGHL complex during arthroscopy. CONCLUSION: Radiologists seemed more likely to make note of IGHL injuries when MR arthrograms were performed; meanwhile, all three discordant cases involved MR arthrogram reads. Therefore, additional larger studies are needed with arthroscopic correlation to elucidate MR findings that confidently suggest injury to the IGHL complex, to avoid false positive radiology reports.