Table 2.

Transformation prerequisites and equivalence factors from iterated (×100) linear regression in feasible-sized training sets (Step 1).

	Size (N)	Iterations	Rsq mean	Rsq SD	Rsq 2.5Q–97.5Q		Beta mean	Beta SD	Beta 2.5Q–97.5Q		Itc mean	Itc SD	Itc 2.5Q–97.5Q
FTLD-Tau GM	Tot	1	0.92	–	–	–	0.89	–	–	–	1.08	–	–	–
	24	100	0.92	0.03	0.87	0.96	0.89	0.04	0.80	0.96	1.08	0.04	0.99	1.16
	12	100	0.91	0.05	0.79	0.98	0.89	0.07	0.74	1.01	1.09	0.11	0.88	1.31
FTLD-Tau WM	Tot	1	0.92	–	–	–	0.90	–	–	–	1.11	–	–	–
	24	100	0.91	0.02	0.88	0.94	0.90	0.03	0.84	0.96	1.12	0.09	0.93	1.25
	12	100	0.90	0.06	0.77	0.97	0.90	0.08	0.75	1.05	1.12	0.19	0.79	1.49
FTLD-TDP GM	Tot	1	0.75	–	–	–	1.00	–	–	–	0.96	–	–	–
	24	100	0.76	0.10	0.44	0.87	1.00	0.14	0.74	1.25	0.95	0.35	0.37	1.67
	12	100	0.72	0.18	0.26	0.94	0.98	0.22	0.57	1.41	0.90	0.55	-0.12	1.98
FTLD-TDP WM	Tot	1	0.78	–	–	v	0.81	–	–	–	0.09	–	–	–
	24	100	0.78	0.07	0.64	0.87	0.81	0.07	0.65	0.93	0.10	0.29	-0.56	0.57
	12	100	0.76	0.12	0.47	0.92	0.81	0.13	0.61	1.10	0.14	0.53	-0.66	1.33

FTLD-Tau, frontotemporal lobar degeneration with inclusions of the tau protein; FTLD-TDP, frontotemporal lobar degeneration with inclusions of the transactive response DNA-binding protein 43 kDa; GM, gray matter; Itc, intercept; N, number of tissue samples; Q, quantile; Rsq, R squared; SD, standard deviation; Tot, total dataset; WM, white matter. Table shows transformation prerequisites (i.e., Rsq) and equivalence factors (i.e., beta, intercept) in randomly subsampled training sets of small sample size, corresponding to a half (N = 12) or one full rack (N = 24) in staining batches, feasible for use in prospective transformations. We performed 100 iterations of the linear regression, and we report mean, standard deviation and a non-parametric quantile-based confidence interval (2.5–97.5% of the distribution) for R squared, beta and intercept values of the linear models. For comparison, we also show these parameters from linear models obtained in the total datasets (i.e., FTLD-Tau GM/WM, FTLD-TDP GM/WM).