network

network contains functions to arrange and analyze glycans in the context of networks. In such a network, each node represents a glycan and edges represent, for instance, their connection via a biosynthetic step. It should be noted, since glycowork treats glycans as molecular graphs, that these networks represent hierarchical graphs, with the network being one graph and each node within the network also a graph. network contains the following modules:

biosynthesis

constructing and analyzing biosynthetic glycan networks

construct_network

 construct_network (glycans:List[str],
                    allowed_ptms:FrozenSet[str]=frozenset({'6S', '4Ac',
                    '3S', 'OS', '6P', '9Ac', '1P', '3P', 'OAc', 'OP'}),
                    edge_type:str='monolink',
                    permitted_roots:Optional[FrozenSet[str]]=None,
                    abundances:List[float]=[])

Construct glycan biosynthetic network

Type Default Details
glycans List List of glycans
allowed_ptms FrozenSet frozenset({‘6S’, ‘4Ac’, ‘3S’, ‘OS’, ‘6P’, ‘9Ac’, ‘1P’, ‘3P’, ‘OAc’, ‘OP’}) Set of allowed PTMs
edge_type str monolink Edge label type: monolink/monosaccharide/enzyme
permitted_roots Optional None Allowed root nodes
abundances List [] Glycan abundances in the same order as glycans; default:empty
Returns DiGraph Biosynthetic network 
glycans = ["Gal(b1-4)Glc-ol", "GlcNAc(b1-3)Gal(b1-4)Glc-ol",
           "GlcNAc6S(b1-3)Gal(b1-4)Glc-ol",
           "Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol", "Fuc(a1-2)Gal(b1-4)Glc-ol",
          "Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol"]
network = construct_network(glycans)
network.nodes()
NodeView(('Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc6S(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Fuc(a1-2)Gal(b1-4)Glc-ol', 'Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[Gal(b1-4)GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol'))

plot_network

 plot_network (network:networkx.classes.graph.Graph,
               plot_format:str='pydot2', edge_label_draw:bool=True,
               lfc_dict:Optional[Dict[str,float]]=None)

Visualize biosynthetic network

Type Default Details
network Graph Biosynthetic network
plot_format str pydot2 Layout type: pydot2/kamada_kawai/spring
edge_label_draw bool True Whether to draw edge labels
lfc_dict Optional None Enzyme:log2FC mapping for edge width
Returns None Displays plot 
plot_network(network, plot_format = 'kamada_kawai')

infer_network

 infer_network (network:networkx.classes.graph.Graph, network_species:str,
                species_list:List[str],
                network_dic:Dict[str,networkx.classes.graph.Graph])

Replace virtual nodes observed in other species

Type Details
network Graph Network to infer
network_species str Source species
species_list List Species to compare against
network_dic Dict Species:network mapping
Returns Graph Network with inferred nodes

retrieve_inferred_nodes

 retrieve_inferred_nodes (network:networkx.classes.graph.Graph,
                          species:Optional[str]=None)

Get inferred virtual nodes from network

Type Default Details
network Graph Network with inferred nodes
species Optional None Source species if multiple
Returns Union Inferred nodes list or dict

update_network

 update_network (network_in:networkx.classes.graph.Graph,
                 edge_list:List[Tuple[str,str]],
                 edge_labels:Optional[List[str]]=None,
                 node_labels:Optional[Dict[str,int]]=None)

Update network with new edges and labels

Type Default Details
network_in Graph Input network
edge_list List List of edges to add
edge_labels Optional None Labels for new edges
node_labels Optional None Node virtual status (0: observed, 1: virtual)
Returns Graph Updated network

trace_diamonds

 trace_diamonds (network:networkx.classes.graph.Graph,
                 species_list:List[str],
                 network_dic:Dict[str,networkx.classes.graph.Graph],
                 threshold:float=0.0, nb_intermediates:int=2,
                 mode:str='presence')

Analyze diamond motif (A->B,A->C,B->D,C->D) path preferences using evolutionary data

Type Default Details
network Graph Biosynthetic network
species_list List Species to compare against
network_dic Dict Species:network mapping
threshold float 0.0 Cutoff threshold
nb_intermediates int 2 Number of intermediate nodes; has to be a multiple of 2
mode str presence Analysis mode: presence/abundance
Returns DataFrame Path analysis results, with proportion (0-1) of how often glycan has been experimentally observed in this path (or average abundance)

evoprune_network

 evoprune_network (network:networkx.classes.graph.Graph,
                   network_dic:Optional[Dict[str,networkx.classes.graph.Gr
                   aph]]=None, species_list:Optional[List[str]]=None,
                   node_attr:str='abundance', threshold:float=0.01,
                   nb_intermediates:int=2, mode:str='presence')

Prune network using evolutionary path preferences

Type Default Details
network Graph Biosynthetic network
network_dic Optional None Species:network mapping
species_list Optional None Species to compare against
node_attr str abundance Node attribute to use for pruning
threshold float 0.01 Cutoff threshold
nb_intermediates int 2 Number of intermediate nodes; has to be a multiple of 2
mode str presence Analysis mode: presence/abundance
Returns Graph Evolutionarily pruned network (with virtual node probability as a new node attribute) 
plot_network(evoprune_network(network), plot_format = 'kamada_kawai')

highlight_network

 highlight_network (network:networkx.classes.graph.Graph, highlight:str,
                    motif:Optional[str]=None, abundance_df:Optional[pandas
                    .core.frame.DataFrame]=None, glycan_col:str='glycan',
                    intensity_col:str='rel_intensity', conservation_df:Opt
                    ional[pandas.core.frame.DataFrame]=None, network_dic:O
                    ptional[Dict[str,networkx.classes.graph.Graph]]=None,
                    species:Optional[str]=None)

Add visual highlighting to network nodes, to be used in plot_network

Type Default Details
network Graph Biosynthetic network
highlight str What to highlight: motif/species/abundance/conservation
motif Optional None Motif to highlight; highlight=motif
abundance_df Optional None Glycan abundance data; highlight=abundance
glycan_col str glycan Glycan column name; highlight=abundance
intensity_col str rel_intensity Intensity column name; highlight=abundance
conservation_df Optional None Species-glycan data; highlight=conservation
network_dic Optional None Species:network mapping; highlight=conservation/species
species Optional None Species to highlight; highlight=species
Returns Graph Network with highlight attributes (‘origin’ (motif/species) or ‘abundance’ (abundance/conservation) node attribute)

export_network

 export_network (network:networkx.classes.graph.Graph, filepath:str,
                 other_node_attributes:Optional[List[str]]=None)

Export network to Cytoscape/Gephi compatible files

Type Default Details
network Graph Biosynthetic network
filepath str Output path prefix, will be appended by file description and type
other_node_attributes Optional None Additional attributes for extraction
Returns None Saves network files (edge list/labels + node IDs and labels)

get_maximum_flow

 get_maximum_flow (network:networkx.classes.graph.Graph,
                   source:str='Gal(b1-4)Glc-ol',
                   sinks:Optional[List[str]]=None)

Estimate maximum flow and flow paths between source and sinks

Type Default Details
network Graph Biosynthetic network
source str Gal(b1-4)Glc-ol Source node
sinks Optional None Target nodes; default:all terminal nodes
Returns Dict Flow results; sink: {maximum flow value, flow path dictionary}

get_max_flow_path

 get_max_flow_path (network:networkx.classes.graph.Graph,
                    flow_dict:Dict[str,Dict[str,float]], sink:str,
                    source:str='Gal(b1-4)Glc-ol')

Get path giving maximum flow value

Type Default Details
network Graph Biosynthetic network
flow_dict Dict Flow dictionary as returned by get_maximum_flow
sink str Target node
source str Gal(b1-4)Glc-ol Source node
Returns List Path edge list

get_reaction_flow

 get_reaction_flow (network:networkx.classes.graph.Graph,
                    res:Dict[str,Dict[str,float]],
                    aggregate:Optional[str]=None)

Get aggregated flows by reaction type

Type Default Details
network Graph Biosynthetic network
res Dict Flow results as returned by get_maximum_flow
aggregate Optional None Aggregation: sum/mean/None
Returns Union Reaction flows (reaction: flow)

get_differential_biosynthesis

 get_differential_biosynthesis (df:Union[pandas.core.frame.DataFrame,str],
                                group1:List[Union[str,int]], group2:Option
                                al[List[Union[str,int]]]=None,
                                analysis:str='reaction',
                                paired:bool=False,
                                longitudinal:bool=False,
                                id_column:str='ID')

Compare biosynthetic patterns between conditions/timepoints

Type Default Details
df Union Glycan abundance data (first column: glycan sequences)
group1 List First group column indices/names (or time points in longitudinal analysis)
group2 Optional None Second group column indices/names (or time points in longitudinal analysis)
analysis str reaction Type: reaction/flow
paired bool False Whether samples are paired
longitudinal bool False Whether to do perform longitudinal analysis
id_column str ID Sample ID column for longitudinal analysis in the ID-style of participant_time_replicate
Returns DataFrame Differential analysis results (differential flow features and statistics OR reaction changes over time 
get_differential_biosynthesis(human_skin_O_PMC5871710_BCC, [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],
                              [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], paired = True)
You're working with an alpha of 0.044390023979542614 that has been adjusted for your sample size of 40.
Mean abundance Log2FC p-val corr p-val significant Effect size
Feature
Neu5Ac(a2-3) 4.542537 -0.391611 0.001702 0.003147 True -0.816234
Gal(b1-?) 3.395793 -0.435594 0.002206 0.003147 True -0.790748
Gal(b1-3) 5.967389 -0.460385 0.003713 0.003147 True -0.739291
Neu5Ac(a2-?) 4.193290 -0.449044 0.004196 0.003147 True -0.727147
Neu5Ac(a2-6) 5.143191 -0.496132 0.007521 0.004513 True -0.668654
Neu5Ac(a2-8) 2.894141 -0.464140 0.013887 0.006944 True -0.605967
OS 2.249050 -0.521844 0.019236 0.008244 True -0.571950
6S 0.824198 -0.270074 0.050454 0.015136 True -0.466992
GlcNAc(b1-6) 0.824198 -0.270074 0.050454 0.015136 True -0.466992
Gal(b1-4) 0.824198 -0.270074 0.050454 0.015136 True -0.466992

extend_network

 extend_network (network:networkx.classes.graph.Graph, steps:int=1,
                 to_extend:Union[str,Dict[str,int],List[str]]='all',
                 strict_context:bool=False)

Extend biosynthetic network physiologically

Type Default Details
network Graph Biosynthetic network
steps int 1 Number of extension steps; default:1
to_extend Union all Nodes to extend (all, specific leaf node, target composition)
strict_context bool False Whether to use network only to derive allowed reaction products; default:False
Returns Tuple (Extended network, New glycans) 
new_network, new_glycans = extend_network(network, strict_context = True)
len(new_glycans)
20

evolution

investigating evolutionary relationships of glycans

distance_from_embeddings

 distance_from_embeddings (df:pandas.core.frame.DataFrame,
                           embeddings:pandas.core.frame.DataFrame,
                           cut_off:int=10, rank:str='Species',
                           averaging:str='median')

Calculate cosine distance matrix from learned embeddings

Type Default Details
df DataFrame DataFrame with glycans (rows) and taxonomic info (columns)
embeddings DataFrame DataFrame with glycans (rows) and embeddings (columns) (e.g., from glycans_to_emb)
cut_off int 10 Minimum glycans per rank to be included; default:10
rank str Species Taxonomic rank for grouping; default:Species
averaging str median How to average embeddings: median/mean
Returns DataFrame Rank x rank distance matrix

distance_from_metric

 distance_from_metric (df:pandas.core.frame.DataFrame,
                       networks:List[networkx.classes.graph.Graph],
                       metric:str='Jaccard', cut_off:int=10,
                       rank:str='Species')

Calculate distance matrix between networks using provided metric

Type Default Details
df DataFrame DataFrame with glycans (rows) and taxonomic info (columns)
networks List List of networkx networks
metric str Jaccard Distance metric to use
cut_off int 10 Minimum glycans per rank to be included; default:10
rank str Species Taxonomic rank for grouping; default:Species
Returns DataFrame Rank x rank distance matrix

dendrogram_from_distance

 dendrogram_from_distance (dm:pandas.core.frame.DataFrame,
                           ylabel:str='Mammalia', filepath:str='')

Plot dendrogram from distance matrix

Type Default Details
dm DataFrame Rank x rank distance matrix (e.g., from distance_from_embeddings)
ylabel str Mammalia Y-axis label
filepath str Path to save plot including filename
Returns None Displays or saves dendrogram plot

check_conservation

 check_conservation (glycan:str, df:pandas.core.frame.DataFrame,
                     network_dic:Optional[Dict[str,networkx.classes.graph.
                     Graph]]=None, rank:str='Order', threshold:int=5,
                     motif:bool=False)

Estimate evolutionary conservation of glycans via biosynthetic networks

Type Default Details
glycan str Glycan or motif in IUPAC-condensed format
df DataFrame DataFrame with glycans (rows) and taxonomic levels (columns)
network_dic Optional None Species:biosynthetic network mapping
rank str Order Taxonomic level to assess
threshold int 5 Minimum glycans per species to be included
motif bool False Whether glycan is a motif vs sequence
Returns Dict Taxonomic group-to-conservation mapping

get_communities

 get_communities (network_list:List[networkx.classes.graph.Graph],
                  label_list:Optional[List[str]]=None)

Find communities for each graph in list of graphs

Type Default Details
network_list List List of undirected biosynthetic networks
label_list Optional None Labels for community names, running_number + _ + label_list[k] for network_list[k]; default:range(len(graph_list))
Returns Dict Community-to-glycan list mapping