TIMM17A VTI1B Class 0.032061314 0.109675 0.034511248 0.093442 0.037674635 0.068003 0.039512032 0.027102 0.052593842 0.073925 0.033237716 0.101937 0.028113628 0.084794 0.039411987 0.071364 0.044011211...

i will focus on DNA-data.xlsx

what i have to start with ?

how can i start to solve it ?

what does VTI1B and TIMM17A mean ?

please help

Extracted text: TIMM17A VTI1B Class 0.032061314 0.109675 0.034511248 0.093442 0.037674635 0.068003 0.039512032 0.027102 0.052593842 0.073925 0.033237716 0.101937 0.028113628 0.084794 0.039411987 0.071364 0.044011211 0.129163 0.03010707 0.019765 0.030718509 0.035181 0.044449828 0.047341 0.028393646 0.134015 0.028911468 0.074726 0.04495849 0.08299 0.064903422 0.090908 0.04750302 0.035103 0.042249057 0.04038 0.040800886 0.101753 0.070032159 0.099384 0.043770561 0.139683 0.040742715 0.106274 0.028546086 0.112439 0.028091795 0.116869 0.051866509 0.088163 0.034527876 0.120834 0.044013278 0.148682 0.056570689 0.142827 0.0678815 0.072986 0.029617308 0.136843 0.035840734 0.188412 0.042366946 0.073923 0.05931188 0.103987 0.041911701 0.056701 0.030070433 0.091566 0.061192148 0.135118 0.035576301 0.047782 DNA_DATA + O Oo o o o o o o o o o o o o o o o o o o o o o o o o o o o o o c o o o o o


Extracted text: Objectives Gain a good understanding of the different level of measurements, data types and descriptive methods for univariate analysis to organize and summarize data. Understand data using graphical displays. Conduct bivariate analysis. In this project , we consider two data sets used in the prediction of the Breast cancer type. the mRNA data set "RNA-DATA.xlsx" and the DNA methylation data set "DNA- DATA.xlsx". In both data sets, the Class attribute corresponds to the type of cancer as indicated in the following table: Class Туре Luminal A 1 Luminal B 2 HER2 3 TNBC 4 Unclear The other attributes in the two data sets correspond to various omics data. For each data set, answer the following questions: 1. Provide a brief description of the dataset (population, observations, variables' types). 2. Conduct a univariate analysis for each variable in the data set using appropriate methods for summary statistics and graphical displays with interpretation. (The descriptive analysis should be as comprehensive as possible). 3. Conduct bivariate analysis with the purpose to understand the factors that may contribute in the identification of the type of breast cancer in the considered data set. 4. Using hypothesis testing, does breast cancer type impact the genomic information "TIMM17A" in DNA-DATA data set