labeled inverse average expression: relative frequency

iae_rf(expr, features = NULL, label, multi = TRUE, thres = 0)

Arguments

expr: a matrix, features in row and cells in column
features: vector, feature names or indexes to compute
label: vector, group label of each cell
multi: logical, if to compute based on binary (FALSE) or multi-class (TRUE)
thres: numeric, cell only counts when expr > threshold, default 0

Value

a matrix of inverse average expression score

Details

$$\mathbf{IAE} = log(1+\frac{mean(N_{i,j\in D})}{max(mean(N_{i,j\in \hat D}))+ e^{-8}})$$ 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 $D$.

Examples

data <- matrix(rpois(100, 2), 10, dimnames = list(1:10))
smartid:::iae_rf(data, label = sample(c("A", "B"), 10, replace = TRUE))
#>            A         A         B         B         B         A         A
#> 1  0.4700036 0.4700036 0.9808292 0.9808292 0.9808292 0.4700036 0.4700036
#> 2  0.8649974 0.8649974 0.5465437 0.5465437 0.5465437 0.8649974 0.8649974
#> 3  0.9694006 0.9694006 0.4769241 0.4769241 0.4769241 0.9694006 0.9694006
#> 4  0.8383292 0.8383292 0.5663955 0.5663955 0.5663955 0.8383292 0.8383292
#> 5  0.6701577 0.6701577 0.7166777 0.7166777 0.7166777 0.6701577 0.6701577
#> 6  1.6486586 1.6486586 0.2135741 0.2135741 0.2135741 1.6486586 1.6486586
#> 7  0.8007778 0.8007778 0.5959834 0.5959834 0.5959834 0.8007778 0.8007778
#> 8  0.8649974 0.8649974 0.5465437 0.5465437 0.5465437 0.8649974 0.8649974
#> 9  0.3566749 0.3566749 1.2039728 1.2039728 1.2039728 0.3566749 0.3566749
#> 10 0.5978370 0.5978370 0.7985077 0.7985077 0.7985077 0.5978370 0.5978370
#>            B         B         B
#> 1  0.9808292 0.9808292 0.9808292
#> 2  0.5465437 0.5465437 0.5465437
#> 3  0.4769241 0.4769241 0.4769241
#> 4  0.5663955 0.5663955 0.5663955
#> 5  0.7166777 0.7166777 0.7166777
#> 6  0.2135741 0.2135741 0.2135741
#> 7  0.5959834 0.5959834 0.5959834
#> 8  0.5465437 0.5465437 0.5465437
#> 9  1.2039728 1.2039728 1.2039728
#> 10 0.7985077 0.7985077 0.7985077