Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks
Fig 6
A simple batch effect dataset was simulated and visualized. This data has 5 dimensions, with 2 informative dimensions for visualization. (a) Two simulated data samples with the same subpopulations. The means shifted (up in sample 2) due to measurement batch effect. (b) When the samples are combined, as in the case of analyzing/pooling all samples together, two different subpopulations overlap (left panel). The overlapped subpopulations cannot be distinguished by clustering (right panel). (c) PAC could be used to discover more subpopulations, however, the hints of the present of another subpopulation do not help to resolve the batch effect. Thus, in this case, it is necessary to analyze the samples separately and then find relationships between the subpopulations across the samples.