labeled inverse average expression: probability based
iae_prob(expr, features = NULL, label, multi = TRUE, thres = 0)a matrix, features in row and cells in column
vector, feature names or indexes to compute
vector, group label of each cell
logical, if to compute based on binary (FALSE) or multi-class (TRUE)
numeric, cell only counts when expr > threshold, default 0
a matrix of inverse average expression score
$$\mathbf{IAE_{i,j}} = log(1+\frac{mean(N_{i,j\in D})}{max(mean(N_{i,j\in \hat D}))+ e^{-8}}*mean(N_{i,j\in D}))$$ where \(N_{i,j\in D}\) is the counts of feature \(i\) in cell \(j\) within class \(D\), and \(\hat D\) is the class except \(D\).
data <- matrix(rpois(100, 2), 10, dimnames = list(1:10))
smartid:::iae_prob(data, label = sample(c("A", "B"), 10, replace = TRUE))
#> B B B B B A B
#> 1 0.6731064 0.6731064 0.6731064 0.6731064 0.6731064 2.03885626 0.6731064
#> 2 1.0479686 1.0479686 1.0479686 1.0479686 1.0479686 0.85441532 1.0479686
#> 3 1.1260113 1.1260113 1.1260113 1.1260113 1.1260113 1.52605630 1.1260113
#> 4 0.8040479 0.8040479 0.8040479 0.8040479 0.8040479 1.39562568 0.8040479
#> 5 1.0216512 1.0216512 1.0216512 1.0216512 1.0216512 0.55961578 1.0216512
#> 6 1.4377265 1.4377265 1.4377265 1.4377265 1.4377265 1.16498736 1.4377265
#> 7 2.4567358 2.4567358 2.4567358 2.4567358 2.4567358 0.44628710 2.4567358
#> 8 1.8632184 1.8632184 1.8632184 1.8632184 1.8632184 0.05218575 1.8632184
#> 9 1.0348965 1.0348965 1.0348965 1.0348965 1.0348965 0.39348891 1.0348965
#> 10 1.7013754 1.7013754 1.7013754 1.7013754 1.7013754 1.23969088 1.7013754
#> A A A
#> 1 2.03885626 2.03885626 2.03885626
#> 2 0.85441532 0.85441532 0.85441532
#> 3 1.52605630 1.52605630 1.52605630
#> 4 1.39562568 1.39562568 1.39562568
#> 5 0.55961578 0.55961578 0.55961578
#> 6 1.16498736 1.16498736 1.16498736
#> 7 0.44628710 0.44628710 0.44628710
#> 8 0.05218575 0.05218575 0.05218575
#> 9 0.39348891 0.39348891 0.39348891
#> 10 1.23969088 1.23969088 1.23969088