scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data.

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Socioeconomic inequality and urban-rural disparity of antenatal care visits in Bangladesh: A trend and decomposition analysis.

BACKGROUND: Socioeconomic inequality in antenatal care visits is a great concern in developing countries including Bangladesh; however, there is a scarcity of investigation to assess the factors of inequality and these changes over time. In this study, we investigated the trend of socioeconomic inequalities (2004-2017) in 1+ANC and 4+ANC visits, and extracted determinants contributions to the observed inequalities and urban-rural disparities in Bangladesh over the period from 2011 to 2017. METHODS: The data from the Bangladesh Demographic and Health Surveys (BDHS) conducted in 2004, 2007, 2011 and 2017 were analyzed in this study. The analysis began with exploratory and bivariate analysis, followed by the application of logistic regression models. To measure the inequalities, the Erreygers concentration index was used, and regression-based decomposition analyses were utilized to unravel the determinant's contribution to the observed inequalities. The Blinder-Oaxaca type decomposition is also used to decompose the urban-rural disparity into the factors. RESULTS: Our analysis results showed that the prevalence of 1+ANC and 4+ANC visits has increased across all the determinants, although the rate of 4+ANC visits remains notably low. The magnitudes of socioeconomic inequality in 4+ANC visits represented an irregular pattern at both the national and urban levels, whereas it increased gradually in rural Bangladesh. However, inequalities in 1+ANC visits declined substantially after 2011 across the national, rural and urban areas of Bangladesh. Decomposition analyses have suggested that wealth status, women's education, place of residence (only for 4+ANC visits), caesarean delivery, husband education, and watching television (TV) are the main determinants to attribute and changes in the level of inequality and urban-rural disparity between the years 2011 and 2017. CONCLUSIONS: According to the findings of our study, it is imperative for authorities to ensure antenatal care visits are more accessible for rural and underprivileged women. Additionally, should focus on delivering high-quality education, ensuring the completion of education, reducing income disparity as well as launching a program to enhance awareness about health facilities, and the impact of caesarean delivery.

Kernel weighted least square approach for imputing missing values of metabolomics data.

Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA .

Robust volcano plot: identification of differential metabolites in the presence of outliers.

BACKGROUND: The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. RESULTS: We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. CONCLUSION: Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Serum and Plasma Metabolomic Biomarkers for Lung Cancer.

In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.

Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.

Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease.

Women's household decision-making autonomy and contraceptive behavior among Bangladeshi women.

BACKGROUND: Women's autonomy is a potentially important but less studied indicator of using contraception among women as well as ability to control their fertility. This study explores women's decision-making autonomy as a potential indicator of the use of contraception in Bangladesh. METHODS: This cross-sectional study utilizes data from the Bangladesh Demographic Health Survey (BDHS) 2007. Information of 8456 currently married and non-pregnant women aged 15-40 years are analyzed to meet up the objective of this study. RESULTS: The mean age of the respondents is 27.19 years and majority of the respondents are from rural areas (62.7%) and also Muslim (90.2%). A large number of women (26.1%) and their husbands (29.0%) have no education and 27.2% respondents were working at the time of interview. The mean number of living children is 2.14. 48.9% of the respondents are currently using a modern method of contraception. More than one-third women are not involved in their household decision-making. Results of this study indicate that household decision-making autonomy is significantly associated with current use of modern contraception, future intention to use contraception and discuss contraception with husband. This measure of women's autonomy provides additional independent explanatory power of contraceptive behavior net of some other socio-demographic variables. CONCLUSION: This study argues in favor of increasing women's autonomy to increase contraception using rate in this population.

Association between adolescent marriage and intimate partner violence: a study of young adult women in Bangladesh.

UNLABELLED: This study explores the association between adolescent marriage and intimate partner violence (IPV) among young adult women using 2007 Bangladesh Demographic Health Survey data. The analyses are restricted to young women 20 to 24 years old. Logistic regression analyses are constructed to estimate the odds ratios and 95% confidence intervals for the association between adolescent marriage and IPV in the past year. RESULTS: show that there is a strong significant relationship between adolescent marriage and experience of physical IPV in the past year among this population. Association between sexual IPV and adolescent marriage is insignificant. Adolescent marriage puts women at increased risk of physical IPV into their young adult period. Government agencies need to enforce existing law on the minimum age at marriage to reduce IPV among adolescent and young adult girls.

External validation of the UCSF-CAPRA (University of California, San Francisco, Cancer of the Prostate Risk Assessment) in Japanese patients receiving radical prostatectomy.

OBJECTIVE: In 2005, the University of California, San Francisco developed the Cancer of the Prostate Risk Assessment (UCSF-CAPRA) score as a new risk stratification tool. The UCSF-CAPRA, which ranges from 0 to 10 points, consists of five clinical variables, prostate-specific antigen, Gleason score, T stage, percent of positive biopsies and age. The aim of this study was to validate the UCSF-CAPRA score for Japanese prostate cancer patients receiving radical prostatectomy using the contemporary Gleason grading. METHODS: From 1999 to 2010, 211 men who underwent radical prostatectomy were used for validation. Biochemical progression-free survival was calculated using the Kaplan-Meier method and the UCSF-CAPRA and D'Amico risk categories were compared using the log-rank method. The concordance index (c-index) for the UCSF-CAPRA and D'Amico risk classification was calculated. RESULTS: Using the UCSF-CAPRA score, 85 (40.3%), 106 (50.2%) and 20 (9.5%) subjects were stratified as 0-2 points (low risk), 3-5 points (intermediate risk) and 6-10 points (high risk). Using the D'Amico risk criteria, 66 (31.3%), 89 (42.2%) and 56 (26.5%) were stratified as low-, intermediate- and high-risk groups, respectively. The Kaplan-Meier analysis showed that the UCSF-CAPRA divided the patients significantly into each risk category. There was no significant difference between low and intermediate in the D'Amico risk classification. The c-index of the UCSF-CAPRA and D'Amico classification was 0.755 and 0.713, respectively. CONCLUSION: The UCSF-CAPRA is an acceptable risk category tool comparable to that of the D'Amico risk classification for Japanese prostate cancer patients receiving radical prostatectomy in the contemporary Gleason grading era.