scv.pl.scatter cannot generate plot to show transcriptional switches

[MoLuLuMo]

scv.pl.scatter cannot generate plot to show transcriptional switches. I convert seurat object to anndata, and then perform rna velocity (dynamical model) through scVelo. I can generate the heatmap of top likelihood genes.

var_names = ['TNFRSF4']
scv.pl.scatter(WT_WT, var_names, frameon=False)
scv.pl.scatter(WT_WT, x='latent_time', y='IL1RN', frameon=False)

Error

```pytb KeyError Traceback (most recent call last) ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local) 200 if self.has_resolvers: --> 201 return self.resolvers[key] 202 ~/.conda/envs/py_seq/lib/python3.8/collections/__init__.py in __getitem__(self, key) 897 pass --> 898 return self.__missing__(key) # support subclasses that define __missing__ 899 ~/.conda/envs/py_seq/lib/python3.8/collections/__init__.py in __missing__(self, key) 889 def __missing__(self, key): --> 890 raise KeyError(key) 891 KeyError: 'IL1RN' During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local) 211 # e.g., df[df > 0] --> 212 return self.temps[key] 213 except KeyError as err: KeyError: 'IL1RN' The above exception was the direct cause of the following exception: UndefinedVariableError Traceback (most recent call last) in 1 var_names = ['TNFRSF4'] 2 scv.pl.scatter(WT_WT, var_names, frameon=False) ----> 3 scv.pl.scatter(WT_WT, x='latent_time', y='IL1RN', frameon=False) ~/.local/lib/python3.8/site-packages/scvelo/plotting/scatter.py in scatter(adata, basis, x, y, vkey, color, use_raw, layer, color_map, colorbar, palette, size, alpha, linewidth, linecolor, perc, groups, sort_order, components, projection, legend_loc, legend_loc_lines, legend_fontsize, legend_fontweight, legend_fontoutline, xlabel, ylabel, title, fontsize, figsize, xlim, ylim, add_density, add_assignments, add_linfit, add_polyfit, add_rug, add_text, add_text_pos, add_outline, outline_width, outline_color, n_convolve, smooth, rescale_color, color_gradients, dpi, frameon, zorder, ncols, nrows, wspace, hspace, show, save, ax, **kwargs) 431 obs = adata.obs[obs_keys] 432 x = obs.astype(np.float32).eval(x) --> 433 y = obs.astype(np.float32).eval(y) 434 else: 435 raise ValueError( ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs) 3597 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers) 3598 -> 3599 return _eval(expr, inplace=inplace, **kwargs) 3600 3601 def select_dtypes(self, include=None, exclude=None) -> DataFrame: ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace) 340 ) 341 --> 342 parsed_expr = Expr(expr, engine=engine, parser=parser, env=env) 343 344 # construct the engine and evaluate the parsed expression ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in __init__(self, expr, engine, parser, env, level) 796 self.parser = parser 797 self._visitor = PARSERS[parser](self.env, self.engine, self.parser) --> 798 self.terms = self.parse() 799 800 @property ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in parse(self) 815 Parse an expression. 816 """ --> 817 return self._visitor.visit(self.expr) 818 819 @property ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs) 399 method = "visit_" + type(node).__name__ 400 visitor = getattr(self, method) --> 401 return visitor(node, **kwargs) 402 403 def visit_Module(self, node, **kwargs): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Module(self, node, **kwargs) 405 raise SyntaxError("only a single expression is allowed") 406 expr = node.body[0] --> 407 return self.visit(expr, **kwargs) 408 409 def visit_Expr(self, node, **kwargs): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs) 399 method = "visit_" + type(node).__name__ 400 visitor = getattr(self, method) --> 401 return visitor(node, **kwargs) 402 403 def visit_Module(self, node, **kwargs): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Expr(self, node, **kwargs) 408 409 def visit_Expr(self, node, **kwargs): --> 410 return self.visit(node.value, **kwargs) 411 412 def _rewrite_membership_op(self, node, left, right): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs) 399 method = "visit_" + type(node).__name__ 400 visitor = getattr(self, method) --> 401 return visitor(node, **kwargs) 402 403 def visit_Module(self, node, **kwargs): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Name(self, node, **kwargs) 533 534 def visit_Name(self, node, **kwargs): --> 535 return self.term_type(node.id, self.env, **kwargs) 536 537 def visit_NameConstant(self, node, **kwargs): ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/ops.py in __init__(self, name, env, side, encoding) 84 tname = str(name) 85 self.is_local = tname.startswith(LOCAL_TAG) or tname in DEFAULT_GLOBALS ---> 86 self._value = self._resolve_name() 87 self.encoding = encoding 88 ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/ops.py in _resolve_name(self) 101 102 def _resolve_name(self): --> 103 res = self.env.resolve(self.local_name, is_local=self.is_local) 104 self.update(res) 105 ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local) 215 from pandas.core.computation.ops import UndefinedVariableError 216 --> 217 raise UndefinedVariableError(key, is_local) from err 218 219 def swapkey(self, old_key: str, new_key: str, new_value=None): UndefinedVariableError: name 'IL1RN' is not defined ```

Versions:

scvelo==0.2.3 scanpy==1.7.2 anndata==0.7.5 > 

[WeilerP]

@Lobbii, seems like 'ILIRN' is either not in adata.var_names or no model has been fitted.

[MoLuLuMo]

@Lobbii, seems like 'ILIRN' is either not in adata.var_names or no model has been fitted.

I have run the dynamic model, how to perform the fitting ?

I have run the script shown below:

scv.pp.filter_and_normalize(WT_WT, min_shared_counts=20, n_top_genes=2000) scv.pp.moments(WT_WT, n_pcs=30, n_neighbors=30) scv.tl.recover_dynamics(WT_WT) scv.tl.velocity(WT_WT, mode='dynamical') scv.tl.velocity_graph(WT_WT) scv.tl.latent_time(WT_WT) scv.pl.scatter(WT_WT, color='latent_time', color_map='gnuplot', size=80) top_genes = WT_WT.var['fit_likelihood'].sort_values(ascending=False).index[:300] scv.pl.heatmap(WT_WT, var_names=top_genes, sortby='latent_time', col_color='SCT_snn_res.0.2', n_convolve=100)

[WeilerP]

Sorry, not sure I understand your question correctly. How is this related to the issue? scv.tl.recover_dynamics fits the models. Please have a look at our docs and the tutorials shown there.

[MoLuLuMo]

Sorry, not sure I understand your question correctly. How is this related to the issue? scv.tl.recover_dynamics fits the models. Please have a look at our docs and the tutorials shown there.

I followed the tutorial and run the dynamical model and calculate latent time. If I need to perform the fitting, how to do it? Which function should I use?

Thanks

[WeilerP]

I followed the tutorial and run the dynamical model and calculate latent time. If I need to perform the fitting, how to do it? Which function should I use?

As mentioned here: scv.tl.recover_dynamics is used to fit the model.

[MoLuLuMo]

I followed the tutorial and run the dynamical model and calculate latent time. If I need to perform the fitting, how to do it? Which function should I use?

As mentioned here: scv.tl.recover_dynamics is used to fit the model.

I have performed scv.tl.recover_dynamics, and I found the fitting information in anndata.var. In this case, how to generate the scatter plot?

AnnData object with n_obs × n_vars = 3129 × 2000 obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'percent.mt', 'Cells', 'Update_Cells', 'nCount_spliced', 'nFeature_spliced', 'nCount_unspliced', 'nFeature_unspliced', 'nCount_ambiguous', 'nFeature_ambiguous', 'initial_size_spliced', 'initial_size_unspliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'root_cells', 'end_points', 'velocity_pseudotime', 'latent_time' var: 'features', 'SCT_features', 'ambiguous_features', 'spliced_features', 'unspliced_features', 'means', 'dispersions', 'dispersions_norm', 'highly_variable', 'fit_r2', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fitt', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'velocity_genes' uns: 'pca', 'neighbors', 'recover_dynamics', 'velocity_params', 'velocity_graph', 'velocity_graph_neg' obsm: 'X_umap', 'X_pca', 'velocity_umap' varm: 'PCs', 'loss' layers: 'SCT', 'ambiguous', 'spliced', 'unspliced', 'Ms', 'Mu', 'fit_t', 'fit_tau', 'fittau', 'velocity', 'velocity_u' obsp: 'distances', 'connectivities'

[WeilerP]

Please have a look at this tutorial.

[MoLuLuMo]

Please have a look at this tutorial.

I followed the tutorial, and I have run the dynamical model to get top-likelihood genes based on fit_likelihood. Is there any thing should I check? Thanks

[MoLuLuMo]

Please have a look at this tutorial.

Besides, I can generate scatter for dynamics genes to show the learned dynamics (spliced vs unspliced). I have checked the tutorial multiple times, and followed the colab jupyter notebook. But I don't know why I cannot generate the scatter plot to show transcriptional switches.

[MoLuLuMo]

Please have a look at this tutorial.

Can I know which adata.var (for var_names ) is used for scv.pl.scatter(WT_WT, x='latent_time', y= var_names , frameon=False)?

[MoLuLuMo]

@WeilerP Package version: scv.logging.print_versions() scvelo==0.2.3 scanpy==1.7.2 anndata==0.7.5 loompy==3.0.6 numpy==1.20.3 scipy==1.6.3 matplotlib==3.4.2 sklearn==0.24.2 pandas==1.2.4

Here is my script import scvelo as scv import scanpy as sc import numpy as np

scv.logging.print_version() scv.settings.verbosity = 3 scv.settings.presenter_view = True
scv.settings.set_figure_params('scvelo')

scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000) scv.pp.moments(adata, n_pcs=30, n_neighbors=30) Filtered out 6699 genes that are detected 20 counts (shared). WARNING: Did not normalize X as it looks processed already. To enforce normalization, set enforce=True. Normalized count data: spliced, unspliced. Extracted 2000 highly variable genes. WARNING: Did not modify X as it looks preprocessed already. computing neighbors finished (0:00:23) --> added 'distances' and 'connectivities', weighted adjacency matrices (adata.obsp) computing moments based on connectivities finished (0:00:01) --> added 'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)

scv.tl.recover_dynamics(adata) recovering dynamics (using 1/32 cores) finished (0:04:26) --> added 'fit_pars', fitted parameters for splicing dynamics (adata.var) scv.tl.latent_time(adata) computing velocities finished (0:00:01) --> added 'velocity', velocity vectors for each individual cell (adata.layers) computing velocity graph finished (0:00:02) --> added 'velocity_graph', sparse matrix with cosine correlations (adata.uns) computing terminal states identified 0 region of root cells and 1 region of end points . finished (0:00:00) --> added 'root_cells', root cells of Markov diffusion process (adata.obs) 'end_points', end points of Markov diffusion process (adata.obs) WARNING: No root cells detected. Consider specifying root cells to improve latent time prediction. computing latent time using root_cells as prior finished (0:00:01) --> added 'latent_time', shared time (adata.obs)

top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index scv.pl.scatter(adata, basis=top_genes[1], frameon=False)

scv.pl.scatter(adata, x='latent_time', y=top_genes[1], frameon=False)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local)
    200             if self.has_resolvers:
--> 201                 return self.resolvers[key]
    202 

~/.conda/envs/py_seq/lib/python3.8/collections/__init__.py in __getitem__(self, key)
    897                 pass
--> 898         return self.__missing__(key)            # support subclasses that define __missing__
    899 

~/.conda/envs/py_seq/lib/python3.8/collections/__init__.py in __missing__(self, key)
    889     def __missing__(self, key):
--> 890         raise KeyError(key)
    891 

KeyError: 'GPR34'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local)
    211                 # e.g., df[df > 0]
--> 212                 return self.temps[key]
    213             except KeyError as err:

KeyError: 'GPR34'

The above exception was the direct cause of the following exception:

UndefinedVariableError                    Traceback (most recent call last)
<ipython-input-9-0f4129e17c43> in <module>
----> 1 scv.pl.scatter(adata, x='latent_time', y=top_genes[1], frameon=False)

~/.local/lib/python3.8/site-packages/scvelo/plotting/scatter.py in scatter(adata, basis, x, y, vkey, color, use_raw, layer, color_map, colorbar, palette, size, alpha, linewidth, linecolor, perc, groups, sort_order, components, projection, legend_loc, legend_loc_lines, legend_fontsize, legend_fontweight, legend_fontoutline, xlabel, ylabel, title, fontsize, figsize, xlim, ylim, add_density, add_assignments, add_linfit, add_polyfit, add_rug, add_text, add_text_pos, add_outline, outline_width, outline_color, n_convolve, smooth, rescale_color, color_gradients, dpi, frameon, zorder, ncols, nrows, wspace, hspace, show, save, ax, **kwargs)
    431                         obs = adata.obs[obs_keys]
    432                         x = obs.astype(np.float32).eval(x)
--> 433                         y = obs.astype(np.float32).eval(y)
    434                     else:
    435                         raise ValueError(

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
   3597         kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
   3598 
-> 3599         return _eval(expr, inplace=inplace, **kwargs)
   3600 
   3601     def select_dtypes(self, include=None, exclude=None) -> DataFrame:

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
    340         )
    341 
--> 342         parsed_expr = Expr(expr, engine=engine, parser=parser, env=env)
    343 
    344         # construct the engine and evaluate the parsed expression

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in __init__(self, expr, engine, parser, env, level)
    796         self.parser = parser
    797         self._visitor = PARSERS[parser](self.env, self.engine, self.parser)
--> 798         self.terms = self.parse()
    799 
    800     @property

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in parse(self)
    815         Parse an expression.
    816         """
--> 817         return self._visitor.visit(self.expr)
    818 
    819     @property

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
    399         method = "visit_" + type(node).__name__
    400         visitor = getattr(self, method)
--> 401         return visitor(node, **kwargs)
    402 
    403     def visit_Module(self, node, **kwargs):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Module(self, node, **kwargs)
    405             raise SyntaxError("only a single expression is allowed")
    406         expr = node.body[0]
--> 407         return self.visit(expr, **kwargs)
    408 
    409     def visit_Expr(self, node, **kwargs):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
    399         method = "visit_" + type(node).__name__
    400         visitor = getattr(self, method)
--> 401         return visitor(node, **kwargs)
    402 
    403     def visit_Module(self, node, **kwargs):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Expr(self, node, **kwargs)
    408 
    409     def visit_Expr(self, node, **kwargs):
--> 410         return self.visit(node.value, **kwargs)
    411 
    412     def _rewrite_membership_op(self, node, left, right):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
    399         method = "visit_" + type(node).__name__
    400         visitor = getattr(self, method)
--> 401         return visitor(node, **kwargs)
    402 
    403     def visit_Module(self, node, **kwargs):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/expr.py in visit_Name(self, node, **kwargs)
    533 
    534     def visit_Name(self, node, **kwargs):
--> 535         return self.term_type(node.id, self.env, **kwargs)
    536 
    537     def visit_NameConstant(self, node, **kwargs):

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/ops.py in __init__(self, name, env, side, encoding)
     84         tname = str(name)
     85         self.is_local = tname.startswith(LOCAL_TAG) or tname in DEFAULT_GLOBALS
---> 86         self._value = self._resolve_name()
     87         self.encoding = encoding
     88 

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/ops.py in _resolve_name(self)
    101 
    102     def _resolve_name(self):
--> 103         res = self.env.resolve(self.local_name, is_local=self.is_local)
    104         self.update(res)
    105 

~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local)
    215                 from pandas.core.computation.ops import UndefinedVariableError
    216 
--> 217                 raise UndefinedVariableError(key, is_local) from err
    218 
    219     def swapkey(self, old_key: str, new_key: str, new_value=None):

UndefinedVariableError: name 'GPR34' is not defined

[WeilerP]

Does the issue persist when you calculate velocities and the velocity graph explicitly, i.e.

scv.tl.recover_dynamics(adata)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
scv.tl.latent_time(adata)

?

[WeilerP]

Just ran your pipeline on the Pancreas dataset used in the tutorials and everything works fine. Do you encounter the same problem there?

[MoLuLuMo]

Does the issue persist when you calculate velocities and the velocity graph explicitly, i.e.

scv.tl.recover_dynamics(adata)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
scv.tl.latent_time(adata)

?

No, it works well. I can generate stream plot of velocity

[MoLuLuMo]

Just ran your pipeline on the Pancreas dataset used in the tutorials and everything works fine. Do you encounter the same problem there?

I didn't encounter the same problem. I converted my dataset from Seurat Object to Anndata.

[WeilerP]

No, it works well. I can generate stream plot of velocity

So after running the explicit velocity calculation you can plot the gene expression vs. latent time?

I didn't encounter the same problem. I converted my dataset from Seurat Object to Anndata.

Just to confirm:

1. You ran the tutorial notebook in the same environment?
2. Your data is in cells x genes form, right?

Would you please check immediately prior to the line causing the problem that

1. 'latent_time' in list(adata.obs.keys()) + list(adata.layers.keys())
2. 'GPR34' in adata.var_names both return True?

Also,

scv.pl.scatter(adata, x='latent_time', y=top_genes[:4], frameon=False)

does not work as well, I presume?

[MoLuLuMo]

No, it works well. I can generate stream plot of velocity

So after running the explicit velocity calculation you can plot the gene expression vs. latent time?

I can generate the plot (scatter & heatmap) the gene expression vs. latent time for scv.datasets.pancreas()

I didn't encounter the same problem. I converted my dataset from Seurat Object to Anndata.

Just to confirm:

1. You ran the tutorial notebook in the same environment?
2. Your data is in cells x genes form, right?

I ran the tutorial notebook in the same environment. My data is in 'cells * genes' form

AnnData object with n_obs × n_vars = 3129 × 2000 (3129 cells * 2000 highly_variable genes

Would you please check immediately prior to the line causing the problem that

1. 'latent_time' in list(adata.obs.keys()) + list(adata.layers.keys())
2. 'GPR34' in adata.var_names both return True?

yes, Both return True

Also,

scv.pl.scatter(adata, x='latent_time', y=top_genes[:4], frameon=False)

does not work as well, I presume?

It doesn't work as well. If I added layer='spliced' argument, I can generate a scatterplot which is different from plots shown in tutorial.

[WeilerP]

Okay, if both return True, the correct if clause here should be entered since is_timeseries evaluates to True and not this case. So it seems

As a sanity check: Could you please check that scv.pl.scatter(adata, x='latent_time', y='GPR34', frameon=False) fails as well? Also that top_genes[1] in adata.var_names also returns True?

[MoLuLuMo]

Okay, if both return True, the correct if clause here should be entered since is_timeseries evaluates to True and not this case. So it seems

As a sanity check: Could you please check that scv.pl.scatter(adata, x='latent_time', y='GPR34', frameon=False) fails as well? Also that top_genes[1] in adata.var_names also returns True?

top_genes[1] GPR34

top_genes[1] in adata.var_names True

scv.pl.scatter(adata, x='latent_time', y='GPR34', frameon=False)

KeyError Traceback (most recent call last) ~/.conda/envs/py_seq/lib/python3.8/site-packages/pandas/core/computation/scope.py in resolve(self, key, is_local) 200 if self.has_resolvers: --> 201 return self.resolvers[key] 202

~/.conda/envs/py_seq/lib/python3.8/collections/init.py in getitem(self, key) 897 pass --> 898 return self.missing(key) # support subclasses that define missing 899

~/.conda/envs/py_seq/lib/python3.8/collections/init.py in missing(self, key) 889 def missing(self, key): --> 890 raise KeyError(key) 891

KeyError: 'GPR34'

[WeilerP]

Hm, okay, sorry I have no idea why this is happening then. Would have to debug this in the code base. Unless you can share your h5ad file, you'll have to clone the scvelo repo and debug why this evaluates to False.

[MoLuLuMo]

@WeilerP
I used the example dataset from http://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/scvelo.html. I performed seurat pipeline for UMAP and clustering and converted seurat object to Anndata. Then I had the same issue for scatter plot.

[WeilerP]

@Lobbii, couldn't run the R code out of the box and do not have the time to debug it. Can you provide the h5ad file you are working with? Before you do so: There seem to have been some problems with pandas==1.3.0 (see e.g. here). Could you check if rolling back your version of pandas fixes the issue?

[WeilerP]

@Lobbii, any update on this?

[MoLuLuMo]

https://drive.google.com/drive/folders/10DYf7Sc9tqyfElIMXaHKxchRjGvfx-hM?usp=sharing

I followed the tutorial provided by Seurat to generate H5ad file

library(Seurat) library(SeuratDisk) library(SeuratWrappers) ldat <- ReadVelocity(file = "./SCG71.loom") bm <- as.Seurat(x = ldat) bm[["RNA"]] <- bm[["spliced"]] bm <- SCTransform(bm) bm <- RunPCA(bm) bm <- RunUMAP(bm, dims = 1:20) bm <- FindNeighbors(bm, dims = 1:20) bm <- FindClusters(bm) DefaultAssay(bm) <- "RNA" SaveH5Seurat(bm, filename = "mouseBM.h5Seurat") Convert("mouseBM.h5Seurat", dest = "h5ad")

[WeilerP]

@Lobbii, the problem are the variable names in adata.raw:

>>> adata.raw.var_names
Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
       ...
       '24411', '24412', '24413', '24414', '24415', '24416', '24417', '24418',
       '24419', '24420'],
      dtype='object', length=24421)

Everything works with use_raw=False, i.e.,

scv.pl.scatter(adata, x='latent_time', y=top_genes[1], frameon=False, use_raw=False)