Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Hi, thanks for your previous help. I have used my own dataset to run several times, and its results are different from the picture. Do you have any better suggestions?
`import warnings
warnings.filterwarnings('ignore')
import dynamo as dyn
import scanpy as sc
dyn.dynamo_logger.main_silence()
Hi, thanks for your previous help. I have used my own dataset to run several times, and its results are different from the picture. Do you have any better suggestions? `import warnings warnings.filterwarnings('ignore')
import dynamo as dyn import scanpy as sc dyn.dynamo_logger.main_silence()
adata = dyn.read_h5ad(r"E:\python\h5ad\a.h5ad") print(adata) dyn.pp.recipe_monocle(adata) dyn.tl.moments(adata, layers=['X_unspliced', 'X_spliced']) dyn.tl.dynamics(adata) dyn.tl.reduceDimension(adata) dyn.tl.cell_velocities(adata, basis='pca', transition_genes = adata.var.index[adata.var['use_for_dynamics']], enforce=True) dyn.vf.VectorField(adata, basis='pca', pot_curl_div=True, cores=8, M=1000) dyn.tl.cell_velocities(adata, basis='umap', transition_genes = adata.var.index[adata.var['use_for_dynamics']], enforce=True) dyn.vf.VectorField(adata, basis='umap', pot_curl_div=True, cores=8, M=1000) gene = "ACSL1" dyn.pd.perturbation(adata, gene, [-100], emb_basis="umap", basis='umap') dyn.pl.streamline_plot(adata, color=["leiden_1.2", gene], basis="umap_perturbation",save_show_or_return="show")