B73 All genes data analysis

Omics datasets come in a diverse range, scale, and follow their own statistical distributions as they are collected from disparate sources, therefore data standardization becomes crucial for omics datasets. Outputs generated from non-standardized features are often skewed, deviated, and filled with outliers and anomalies. Thus, the advanced exploratory analysis such as Dendrograms, Hierarchical Heatmaps, Hierarchical Scatter plots, Heatmaps and PCA analysis demands high-level data preprocessing and normalization to balance out disproportionate weights across multiple variables. Data normalization transforms the multiscaled data all to the same scale, thereby improving the stability and performance of the learning algorithm. The Maize Feature Store application allows for the normalization of omics numerical features by centering the features with their mean and the standard deviation between 0 and 1 using the most common normalization method called Z-score normalization. In standardized z-score normalization, each feature is normalized as Z = ( X - X' ) / S, where X, X' and S are the feature, the mean and the standard deviation respectively.