labeled inverse average expression: IGM
iae_igm(expr, features = NULL, label, lambda = 7, thres = 0)
a matrix, features in row and cells in column
vector, feature names or indexes to compute
vector, group label of each cell
numeric, hyperparameter for IGM
numeric, cell only counts when expr > threshold, default 0
a vector of inverse gravity moment score for each feature
$$\mathbf{IGM_i} = log(1+\lambda\frac{max(mean(N_{i,j\in D})_{k})}{\sum_{k}^{K}(mean(N_{i,j\in D})_{k}*r_{k})+e^{-8}})$$ where \(\lambda\) is the hyper parameter, \(N_{i,j\in D}\) is the counts of feature \(i\) in cell \(j\) within class \(D\), and \(r_k\) is the rank of \(mean(N_{i,j\in D})\).