MultiGPU notebooks calculated differentially expressed PCAs

[Intron7]

Hey Nvidia Genomics Team,

In the Multi GPU Notebook the AnnData object post PCA is created with the PCA in .X. Therefore when you later later perform the differential gene expression you calculate the which PCA is the most import for each cluster.

The ranked gene groups function still has a couple of bugs. There is a PR with a fix. Which also introduces some performance improvements.

Yours Severin

[111kakaluote]

I create anndata with lognormalized and scaledata, like this:

sparse_gpu_array_scale = dask_sparse_arr.compute()  #sparse_gpu_array_scale store scaledata
adata = anndata.AnnData(sparse_gpu_array_scale.get())
adata.layers["normalised"] = sparse_gpu_array.get()  #sparse_gpu_array store lognormalized data
adata.var_names = genes.to_pandas()
adata.obsm['X_pca'] = local_pca.get()

so I can ranked gene groups with adata.X. however, which the newest verison of rapids-single-cell-example? github or docker images?