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Table 1 Clustering comparison of the matrix factorization-based methods in terms of Normalized Mutual information (NMI) and Adjusted Random Index (ARI)

From: A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data

Method Brain Embryo Pancreas
  NMI ARI NMI ARI NMI ARI
PCA 0.582 0.339 0.366 0.187 0.630 0.368
Nimfa 0.494 0.258 0.414 0.173 0.456 0.114
NMFEM 0.456 0.264 0.741 0.614 0.435 0.175
tSNE 0.712 0.544 0.658 0.538 0.793 0.652
ZIFA 0.797 0.721 0.888 0.748 0.641 0.429
pCMF 0.787 0.788 0.822 0.659 0.547 0.334
ZINB-WaVE 0.892 0.916 0.888 0.721 0.518 0.342
scNBMF 0.901 0.933 0.908 0.763 0.716 0.472
  1. The number with bold indicates the best performance method and the number with grey represents the second best performance method