Combining assumptions and graphical network into gene expression data analysis
نویسندگان
چکیده
Abstract Background Analyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network relations that exist among genes. Combining these different elements cannot only improve statistical power, provide a better framework through which can be properly analyzed. Material and methods We propose novel model combines the analysis. Assumptions are important since every test statistic is valid when required hold. So, we hybrid p -values show that, under null hypothesis of primary interest, uniformly distributed. These proposed take consideration. incorporate analysis because neighboring genes share biological functions. This correlation factor taken account via similar prior probabilities for Results With series simulations our approach compared with other approaches. Area Under ROC Curves (AUCs) constructed to compare methodologies; AUC based methodology larger than others. For regression analysis, from method contains AUCs Spearman Pearson test. In addition, true negative rates (TNRs) known as specificities higher two group comparison instance, sample size n =10, specificity corresponding 0.716146 t-test rank sum 0.689223 0.69797, respectively. Our shown more powerful. Conclusions procedures introduced general class procedure-selection, multiple-testing, graphical obtain very good performance in simulations, real
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ژورنال
عنوان ژورنال: Journal of Statistical Distributions and Applications
سال: 2021
ISSN: ['2195-5832']
DOI: https://doi.org/10.1186/s40488-021-00126-z