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LouisFaure / scFates

a scalable python suite for tree inference and advanced pseudotime analysis from scRNAseq data.
https://scfates.readthedocs.io/
BSD 3-Clause "New" or "Revised" License
49 stars 1 forks source link

Questions about scf.pl.single_trend' #25

Closed SummerColdWind closed 8 months ago

SummerColdWind commented 9 months ago

Hello, author, when I run' scf.tl.test_association' and' scf.tl.fit',' found 1661 significant features (0: 18: 28)-> added' is displayed, and then the shape of my adata also becomes (2650, 1661). Therefore, when I want to use scf.pl.single_trend to draw these 1661 genes, I can't get the expected results. Please reply if you have time. Thank you.

LouisFaure commented 8 months ago

Are you trying to plot all genes at the same time with scf.pl.single_trend? This function accepts one single gene at a time.

If you wish to display all genes you can use scf.pl.trends instead. However, showing all 1661 in one trends plot might not be as informative, I would use scf.tl.cluster to group gene trends before that.

SummerColdWind commented 8 months ago

Are you trying to plot all genes at the same time with scf.pl.single_trend? This function accepts one single gene at a time.

If you wish to display all genes you can use scf.pl.trends instead. However, showing all 1661 in one trends plot might not be as informative, I would use scf.tl.cluster to group gene trends before that.

For example, there are 10,000 genes in my AnnData. After screening the hypervariable genes, there are 1,661 genes left. My idea is to draw any of the remaining 8,339 genes besides these 1,661 genes.

LouisFaure commented 8 months ago

If you wish to display non significant genes, I would then suggest to call fit function with all genes as following:

scf.tl.fit(adata,features=adata.var_names)
SummerColdWind commented 8 months ago

If you wish to display non significant genes, I would then suggest to call fit function with all genes as following:

scf.tl.fit(adata,features=adata.var_names)

Tks :)