Seurat -Combine two samples and perform CCA


This tool can be used to do the canonical correlation analysis (CCA) and to combine two Seurat objects for later joined analysis.



As inputs, give two Seurat object generated with Seurat Setup tool.
NOTE: make sure you have assigned the sample or group name in the Setup tool (use short names like "CTRL", "TREAT"). This name will be used later on to separate the samples.

First, we choose the genes for the alignment process: the tool takes the union of top 1000 genes with the highest dispersion (=var/mean) from both datasets (/samples/Seurat objects).

Next, the canonical correlation analysis (CCA) is performed to identify common sources of variation between the datasets. A combined Seurat object is generated. The results of CCA can be seen in the CC1 versus CC2 and in the CC1 violin plot.

For the downstream analysis and for the next tool (integrated analysis) we need to decide which CCs to use and align. This is similar to the problem of choosing PCs. MetageneBicorPlot shows the correlation strength of each CC -the idea is to look for a saturation point (=where the curve flattens).

For more details, please check the Seurat tutorials for multiple sample analysis.