A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status

Table 1 Diversity (^α D) and Evenness (^α E) profiles classify TCR (dataset 1) and BCR (dataset 2) immune repertoires by immunological status with high accuracy

	BACC (%)	Sensitivity (%)	Specificity (%)	Significance (p-value)	Median number of alpha-values used
Dataset 1
CD4-Diversity \( \left(\overrightarrow{{}^{\alpha }D}\right) \): Month 2 versus Baseline + Month 12	86.5	72.9	100	0	8
CD4-Evenness \( \left(\overrightarrow{{}^{\alpha }E}\right) \): Month 2 versus Baseline + Month 12	91.7	83.3	100	0	11
CD8-Diversity \( \left(\overrightarrow{{}^{\alpha }D}\right) \): Month 2 versus Baseline + Month 12	79.2	58.3	100	0	9
CD8-Evenness \( \left(\overrightarrow{{}^{\alpha }E}\right) \): Month 2 versus Baseline + Month 12	96.9	93.8	100	0	6
Dataset 2
Diversity: Healthy versus CLL \( \left(\overrightarrow{{}^{\alpha }D}\right) \)	88	77	100	0	5
Evenness: Healthy versus CLL \( \left(\overrightarrow{{}^{\alpha }E}\right) \)	84	77	91	0	2

The median number of alpha-values employed to reach optimal prediction accuracy (BACC) ranged between 2 and 11. BACCs were computed using nested leave-one-out cross-validation and were regarded as significant if p < 0.01. BACC ((Sensitivity + Specificity)/2), balanced prediction accuracy. Diversity and Evenness profiles were calculated in a range of alpha = 0 to alpha = 10 with a step size of 0.2