Open SteveTur opened 1 month ago
my0916
commented
1 month ago Hi,
I'm also suffering from a similar problem... https://github.com/aertslab/scenicplus/discussions/378#discussioncomment-9314238
I also tried to export to Cytoscape with the export_to_cytoscape function, but the exported cys file cannot be loaded with the message 'Cannot find the cysession.xml file'
Thank you for your help.
Best, Masa
yiyelinfeng
commented
1 month ago I have same problem, please help!
SeppeDeWinter
commented
1 month ago Hi @SteveTur, @my0916 and @yiyelinfeng
I am aware of the issue. I have added it to my to do list and will try to fix it whenever I have some time.
All the best,
Seppe
yiyelinfeng
commented
1 month ago Hi@ SeppeDeWinter,
my issue only just the .cys file cannot be loaded in cytoscape with the message 'Cannot find the cysession.xml file' when file -> import -> Network from file ... after import the SNENIC+ network layout. Because I want to check and change the network graph using Cytoscape. but can't load it.
thanks,
Best, Lin
DmitriiSeverinov
commented
1 month ago Hi @SeppeDeWinter , @yiyelinfeng @my0916 , @SteveTur ,
I had similar issue with blank output, what I found is that if I comment the following line in the create_nx_tables function, it works:
subset_eRegulons = [x + '_[^a-zA-Z0-9]' for x in subset_eRegulons]
So, here is my code how I did it:
from pycisTopic.diff_features import find_highly_variable_features
scplus_obj.uns['direct_e_regulon_metadata_filtered'] = scplus_mdata.uns['direct_e_regulon_metadata_filtered']
hvr = find_highly_variable_features(scplus_obj.to_df('ACC').loc[list(set(scplus_obj.uns['direct_e_regulon_metadata_filtered']['Region']))], n_top_features=3000, plot = False)
hvg = find_highly_variable_features(scplus_obj.to_df('EXP')[list(set(scplus_obj.uns['direct_e_regulon_metadata_filtered']['Gene']))].T, n_top_features=3000, plot = False)
from scenicplus.networks import create_nx_tables, create_nx_graph, plot_networkx, export_to_cytoscape
nx_tables = list()
def _format_df_nx(df, key, var):
"""
A helper function to format differential test results
"""
df.index = df['names']
df = pd.DataFrame(df['logfoldchanges'])
df.columns = [var+'_Log2FC_'+key]
df.index.name = None
return df
def _get_log2fc_nx(scplus_obj: 'SCENICPLUS',
variable,
features,
contrast: Optional[str] = 'gene'
):
"""
A helper function to derive log2fc changes
"""
if contrast == 'gene':
adata = anndata.AnnData(X=scplus_obj.X_EXP, obs=pd.DataFrame(
index=scplus_obj.cell_names), var=pd.DataFrame(index=scplus_obj.gene_names))
if contrast == 'region':
adata = anndata.AnnData(X=scplus_obj.X_ACC.T, obs=pd.DataFrame(
index=scplus_obj.cell_names), var=pd.DataFrame(index=scplus_obj.region_names))
adata.obs = pd.DataFrame(scplus_obj.metadata_cell[variable])
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata = adata[:, features]
sc.tl.rank_genes_groups(
adata, variable, method='wilcoxon', corr_method='bonferroni')
groups = adata.uns['rank_genes_groups']['names'].dtype.names
diff_list = [_format_df_nx(sc.get.rank_genes_groups_df(
adata, group=group), group, variable) for group in groups]
return pd.concat(diff_list, axis=1)
def create_nx_tables(scplus_obj: 'SCENICPLUS',
eRegulon_metadata_key: str ='eRegulon_metadata',
subset_eRegulons: List = None,
subset_regions: List = None,
subset_genes: List = None,
add_differential_gene_expression: bool = False,
add_differential_region_accessibility: bool = False,
differential_variable: List =[]):
"""
A function to format eRegulon data into tables for plotting eGRNs.
Parameters
---------
scplus_obj: SCENICPLUS
A SCENICPLUS object with eRegulons
eRegulon_metadata_key: str, optional
Key where the eRegulon metadata dataframe is stored
subset_eRegulons: list, optional
List of eRegulons to subset
subset_regions: list, optional
List of regions to subset
subset_genes: list, optional
List of genes to subset
add_differential_gene_expression: bool, optional
Whether to calculate differential gene expression logFC for a given variable
add_differential_region_accessibility: bool, optional
Whether to calculate differential region accessibility logFC for a given variable
differential_variable: list, optional
Variable to calculate differential gene expression or region accessibility.
Return
---------
A dictionary with edge feature tables ('TF2G', 'TF2R', 'R2G') and node feature tables ('TF', 'Gene', 'Region')
"""
er_metadata = scplus_obj.uns[eRegulon_metadata_key].copy()
if subset_eRegulons is not None:
# subset_eRegulons = [x + '_[^a-zA-Z0-9]' for x in subset_eRegulons]
er_metadata = er_metadata[er_metadata['Region_signature_name'].str.contains(
'|'.join(subset_eRegulons))]
if subset_regions is not None:
er_metadata = er_metadata[er_metadata['Region'].isin(subset_regions)]
if subset_genes is not None:
er_metadata = er_metadata[er_metadata['Gene'].isin(subset_genes)]
nx_tables = {}
nx_tables['Edge'] = {}
nx_tables['Node'] = {}
# Generate edge tables
r2g_columns = [x for x in er_metadata.columns if 'R2G' in x]
tf2g_columns = [x for x in er_metadata.columns if 'TF2G' in x]
nx_tables['Edge']['TF2R'] = er_metadata[er_metadata.columns.difference(
r2g_columns + tf2g_columns)].drop('Gene', axis=1).drop_duplicates()
nx_tables['Edge']['TF2R'] = nx_tables['Edge']['TF2R'][['TF', 'Region'] +
nx_tables['Edge']['TF2R'].columns.difference(['TF', 'Region']).tolist()]
nx_tables['Edge']['R2G'] = er_metadata[er_metadata.columns.difference(
tf2g_columns)].drop('TF', axis=1).drop_duplicates()
nx_tables['Edge']['R2G'] = nx_tables['Edge']['R2G'][['Region', 'Gene'] +
nx_tables['Edge']['R2G'].columns.difference(['Region', 'Gene']).tolist()]
nx_tables['Edge']['TF2G'] = er_metadata[er_metadata.columns.difference(
r2g_columns)].drop('Region', axis=1).drop_duplicates()
nx_tables['Edge']['TF2G'] = nx_tables['Edge']['TF2G'][['TF', 'Gene'] +
nx_tables['Edge']['TF2G'].columns.difference(['TF', 'Gene']).tolist()]
# Generate node tables
tfs = list(set(er_metadata['TF']))
nx_tables['Node']['TF'] = pd.DataFrame(
'TF', index=tfs, columns=['Node_type'])
nx_tables['Node']['TF']['TF'] = tfs
genes = list(set(er_metadata['Gene']))
genes = [x for x in genes if x not in tfs]
nx_tables['Node']['Gene'] = pd.DataFrame(
'Gene', index=genes, columns=['Node_type'])
nx_tables['Node']['Gene']['Gene'] = genes
regions = list(set(er_metadata['Region']))
nx_tables['Node']['Region'] = pd.DataFrame(
'Region', index=regions, columns=['Node_type'])
nx_tables['Node']['Region']['Region'] = regions
# Add gene logFC
if add_differential_gene_expression is True:
for var in differential_variable:
nx_tables['Node']['TF'] = pd.concat([nx_tables['Node']['TF'], _get_log2fc_nx(
scplus_obj, var, nx_tables['Node']['TF'].index.tolist(), contrast='gene')], axis=1)
nx_tables['Node']['Gene'] = pd.concat([nx_tables['Node']['Gene'], _get_log2fc_nx(
scplus_obj, var, nx_tables['Node']['Gene'].index.tolist(), contrast='gene')], axis=1)
if add_differential_region_accessibility is True:
for var in differential_variable:
nx_tables['Node']['Region'] = pd.concat([nx_tables['Node']['Region'], _get_log2fc_nx(
scplus_obj, var, nx_tables['Node']['Region'].index.tolist(), contrast='region')], axis=1)
return nx_tables
nx_table = create_nx_tables(
scplus_obj = scplus_obj,
eRegulon_metadata_key ='direct_e_regulon_metadata_filtered',
subset_eRegulons = ['atf3', 'bach2b'],
subset_regions = hvr,
subset_genes = hvg,
add_differential_gene_expression = True,
add_differential_region_accessibility = True,
differential_variable = ['ident']
)
G, pos, edge_tables, node_tables = create_nx_graph(nx_table,
use_edge_tables = ['TF2R','R2G'],
color_edge_by = {'TF2R': {'variable' : 'TF', 'category_color' : {'atf3': 'Red', 'bach2b': 'Blue'}},
'R2G': {'variable' : 'importance_x_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1}},
transparency_edge_by = {'R2G': {'variable' : 'importance_R2G', 'min_alpha': 0.1, 'v_min': 0}},
width_edge_by = {'R2G': {'variable' : 'importance_R2G', 'max_size' : 1.5, 'min_size' : 1}},
color_node_by = {'TF': {'variable': 'TF', 'category_color' : {'atf3': 'Red', 'bach2b': 'Blue'}},
'Gene': {'variable': 'ident_Log2FC_RSNs', 'continuous_color' : 'bwr'},
'Region': {'variable': 'ident_Log2FC_RSNs', 'continuous_color' : 'viridis'}},
transparency_node_by = {'Region': {'variable' : 'ident_Log2FC_RSNs', 'min_alpha': 0.1},
'Gene': {'variable' : 'ident_Log2FC_RSNs', 'min_alpha': 0.1}},
size_node_by = {'TF': {'variable': 'fixed_size', 'fixed_size': 30},
'Gene': {'variable': 'fixed_size', 'fixed_size': 15},
'Region': {'variable': 'fixed_size', 'fixed_size': 10}},
shape_node_by = {'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'}},
label_size_by = {'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 20.0},
'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0},
'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0}},
layout='kamada_kawai_layout',
scale_position_by=250)
plt.figure(figsize=(20,20))
plot_networkx(G, pos)
plt.savefig('figures/TFs_target_genes/eRegulons.pdf')
export_to_cytoscape(G, pos, out_file = os.path.join('figures/network.cys'))
I hope it will work for you as well!
However, I didn't manage to solve the problem with import to cytoscape :(
Best, Dmitrii
yiyelinfeng
commented
1 month ago just change "export_to_cytoscape(G, pos, out_file = os.path.join('figures/network.cyjs'))", it works.
Hello,
It seems that the function to plot the GRN on python doesn't work anymore since the recent updates:
from scenicplus.networks import create_nx_tables, create_nx_graph, plot_networkx, export_to_cytoscape G, pos, edge_tables, node_tables = create_nx_graph( nx_tables, use_edge_tables = ['TF2R','R2G'], color_edge_by = { 'TF2R': {'variable' : 'TF', 'category_color' : category_color}, 'R2G': {'variable' : 'R2G_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1} }, transparency_edge_by = { 'R2G': {'variable' : 'R2G_importance', 'min_alpha': 0.1, 'v_min': 0} }, width_edge_by = { 'R2G': {'variable' : 'R2G_importance', 'max_size' : 1.5, 'min_size' : 1} }, color_node_by = { 'TF': {'variable': 'TF', 'category_color' : category_color}, 'Gene': {'variable': 'GEX_celltype_Log2FC_6', 'continuous_color' : 'bwr'}, 'Region': {'variable': 'GEX_celltype_Log2FC_6', 'continuous_color' : 'viridis'} }, transparency_node_by = { 'Region': {'variable' : 'GEX_celltype_Log2FC_6', 'min_alpha': 0.1}, 'Gene': {'variable' : 'GEX_celltype_Log2FC_6', 'min_alpha': 0.1} }, size_node_by = { 'TF': {'variable': 'fixed_size', 'fixed_size': 30}, 'Gene': {'variable': 'fixed_size', 'fixed_size': 15}, 'Region': {'variable': 'fixed_size', 'fixed_size': 10} }, shape_node_by = { 'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'}, 'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'}, 'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'} }, label_size_by = { 'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0}, 'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0}, 'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0} }, layout='kamada_kawai_layout', scale_position_by=250 )
plt.figure(figsize=(50,50)) plot_networkx(G, pos)
Only blank output are coming out for some reason. We never got any problem before the recent changes. What should we do?
Thank you for your help,
Best,
Steven