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.