ABSTRACT
OBJECTIVE: The aim was to analyze the incidence trend and annual average incidence change of type 1 diabetes (T1DM) in the population <18 years of age in Malatya province. MATERIALS AND METHODS: Medical files of patients followed up with T1DM in pediatric endocri- nology clinics were reviewed. The data for the child census was taken from the Turkish Statistical Institute (TUIK), and T1DM incidence was analyzed according to the calendar year, gender, and age groups. Recently diagnosed T1DM patients per 100 000 children per year were calculated. In addition, the trend in annual incidence change over the period 2007-2019 was analyzed. RESULTS: The mean incidence of T1DM during the 13 years was 13.1/105 child years (13.8/105 child years for girls and 12.4/105 child years for boys). During the 13-year follow-up period, a sig- nificant increasing trend in the incidence of T1DM was detected. The average annual percent change (AAPC) was 8.3%. According to age groups, the average AAPC was 8.1% between 0 and 4 years old, 9.4% between 5 and 9 years old, 12.1% between 10 and 14 years old, and 30.1% between 15 and 17 years old. CONCLUSION: The incidence of T1DM in children under 18 years of age in Malatya, one of the larg- est cities in the Eastern Anatolia region of Turkey, was determined as 13.1/105 child years in the last 13 years and the average annual increase rate was 8.3%.
ABSTRACT
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. METHODS: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. MATERIALS: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. RESULTS: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. CONCLUSION: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.
