2.4 Statistical data analysis
The LFQ intensities of each sample were extracted and statistical analysis was performed using Perseus (Version 2.6.0) (14). Proteins with quantified intensities in less than 70% of the samples were discarded. The data was log2 transformed, median normalized and missing values were imputed separately for each sample group using a kNN based imputation method. A two-sample t-test was then performed on this data and the resultant p-values were corrected using Benjamini-Hochberg FDR correction. Proteins were considered to be significant if they had an FDR corrected p value (q-value) of less than 0.05 and an log 2 fold change value greater than 1.5. Metaboanalyst (Version 5.0) was used for visualization of the heatmap (15); violin plots and volcano plots were created using Python tools. The violin plots were made with Log2 transformed data and the significance level was calculated based on t-test with Bonferroni correction.