I am trying to use UMAP through umap-learn. And I am running 2,000,000 cells with 16 dimensions as my training dataset and another 1,000,000 cells with 16 dimensions as my testing dataset. However, when I add another 100,000 cells to my testing dataset(so it becomes 1,100,000 cells), the layout of the UMAP changed completely. I am wondering if there is anyway I can fix the layout of UMAP when adding only a small fraction of cells to the dataset?
I have tried the following code
data = np.array(np.vstack([training_array, testing_array]), dtype=np.float64)
embedding = umap.UMAP(random_state=42).fit_transform(data)