Closed dawe closed 3 years ago
Hi @dawe, do you know how this is solved in scanpy? Would be nice if we can use a similar solution here.
AFAIK if a list of names other than adata.var_names
is given to sc.pl.heatmap
, you need to specify the column in adata.var
that stores the values of the given list through the argument gene_symbols
. So, to have symbols one has to call sc.pl.heatmap(adata, ['gs1', 'gs2',…], gene_symbols='Symbol')
assuming adata.var
has a column Symbol
.
I find scanpy solution a bit counterintuitive, IMHO a easier solution would be to call heamtap with actual var_names and let the plot function to get the appropriate names from the specified column: sc.pl.heatmap(adata, ['ens1', 'ens2',…], gene_symbols='Symbol')
. If I have multiple columns I want to use for names in different plots I only have to change the key, whereas the current implementations forces me to pass a different list for each plot.
@michalk8, could you draft an implementation for this?
Hi @michalk8, what's the status here?
@michalk8, any progress on this yet?
Currently, this is not on my priority list.
@michalk8, let's either close this (can add to release schedule for a later release if important) or add to 1.5, I leave this to you.
@michalk8, let's either close this (can add to release schedule for a later release if important) or add to 1.5, I leave this to you.
I have context manager implemented in squidpy
which switches between gene symbols stored elsewhere in adata.var
, so I think I can just rehash that (as long as it's fine I make the gene symbols unique if they are not unique, otherwise, the implementatin is a bit problematic [we store the fitted models in a dict of dics, <lineage>: <gene>: ...
]).
That sounds like a good solution, let's go for that.
closed via #726 (parameter name is gene_symbols
)
cr.tl.estimators
?cr.tl.kernels
?cr.ul.models
?cr.pl
?I have AnnData var names as EnsID, there's a adata.var['Symbol'] column for readability. It would be useful if alternate gene labels could then be passed to cr.pl functions, in particular
cr.pl.heatmap()
but alsocr.pl.gene_trends()