Data and databases:

Adding new data to the main knowledge repository:

The easiest way to add new information to the main knowledge repository is by finding the nodes to which new knowledge will attach (provided by the convert_to_internal_ids function from the bioflow.neo4j_db.db_io_routines module for a lot of x-ref identifiers for physical entity nodes), and then process to add new relationships and nodes using the functions to add a connection between nodes and DatabaseGraph .create to add a new node. DatabaseGraph.attach_annotation_tag can be used in order to attach annotation tags to new nodes that can be searcheable from the outside. All functions can be batched (cf API documentation).

A new link will have a call signature of type link(node_id, node_id, link_type, {param: val}) ``, where node_ids are internal database ids for the nodes provided by the ``convert_to_internal_ids function, link_type is a link type that would be handy for you to remember (preferably in the snake_case). Two parameters are expected: source and parse_type. parse_type can only take a value in ['physical_entity_molecular_interaction', 'identity', 'refines', 'annotates', 'annotation_relationship', 'xref'], with 'xref' being reserved for the annotation linking.

A new node will have a call signature of type create(node_type, {param:val}) and return the internal id of the created node. node_type is a node type that would be handy for you to remember (preferably in the snake_case). Four paramters are expected: 'parse_type', 'source', 'legacyID' and 'displayName'. 'parse_type' can take only values in ['physical_entity', 'annotation', 'xref'], with 'xref' being reserved for the annotation linking. legacyID is the identifier of the node in the source database and displayName is the name of the biological knowledge node that that will be shown to the end user.

Input files:

Hits set:

By default, BioFlow expects a set of ids of genes or other physical entities that have been associated to a process or a phenotype of interest. In its current implementation, the identifiers will be mapped to SWISSPROT-UNIPROT entities. The input file is expected to be a .tsv/.csv file with a single identifier per line, without a header.

When evaluating the hits set, in order to determine if the hypothesis set (information flow pattern) generated by it has any significance, BioFlow will randomly sample hypothesis sets generated by matched random sets of valid physical entities (SWISSPROT-UNIPROT), and compare the flow generate by the hits set to the one generated by the matched random sets.

Weighted hits set:

It is possible to specify weights assigned to each gene/protein that has been associated to the process or phenotype of interest, indicating how much confidence we have in the fact that a given gene/protein are indeed involved in a phenotype. For consistency with the theoretical framework underlying BioFlow, the values should be log-likelihood. -log(p_values) could be a reasonable weight. The input file is expected to be a .tsv/.csv file with a single identifier and a positive weight per line, without a header.

When evaluating the significance, the matched random sampling will also match the weights of randomly sampled nodes to the weights of real hits. By default, the matching is exact - the weights from the sample will be permuted and randomly assigned to the sampled entities.

Due to the way sampling and statistical significance evaluation works, it is suggested that only significant hits are supplied for the analysis.

Hits & targets set:

In case when we are looking to generate hypotheses that would connect two sets of genes (such as for instance genes experimentally identified as relevant to a phenotype and genes that have in the past been documented as causative to a phenotype, we can supply a secondary hits set to BioFlow.

In the current implementation, hits and secondary hits are assumed to be disjoint and the flow will be calculated between them and only between them.

Similarly to just the primary hits set, both sets can be weighted. The file formatting is identical to the one of the primary set.


By default, to calculate the flow significance, BioFlow samples all the valid identifiers in the database - which in the current implementation is SWISSPROT-UNIPROT confirmed protein IDs. However, for some experimental methods some proteins are effectively impossible to detect due to the experimental modalities. When this is the case, the user can supply a file of identifiers that define an explicit background set to be sampled from. The input file formatting is identical to the primary set.

Weighted background:

In case where the background genes are likely to be detected at random with different probabilities, it is also possible to generate a weighted background, which will be sampling the physical entities (in this case SWISSPOROT-UNIPROT) based on that weight. input file formatting is identical to the weighted primary set as well.

File translation:

The conversion from the external ids to the internal db ids before providing the input to the Interactome/BioKnowledge interface to make sure the sampling sizes are correct in case some external ids cannot be mapped to the internal ids. This is performed by the bioflow.top_level .map_and_save_gene_ids, which is quite polymorphic. It’s general signatures are

> hits_ids, sec_hit_ids, background_internal_ids = map_and_save_gene_ids(
     '')  # single input

> hits_ids, sec_hit_ids, background_internal_ids = map_and_save_gene_ids(
     'test_weighted_background.tsv')  # single input with background

> hits_ids, sec_hit_ids, background_internal_ids = map_and_save_gene_ids(
     '') # double input

> hits_ids, sec_hit_ids, background_internal_ids = map_and_save_gene_ids(
     'test_weighted_background.tsv') # double input with background

hits_ids, sec_hit_ids and background_internal_ids can from then be used in auto_analysis in Bioknowledge or Interactome modules

Node/edge weight adjustment:

For InteractomeInterface, it is possible to adjust the weights of nodes and edges (for instance to rapidly compensate for hotspots due to identified biases in the flow patterns). It is not an encouraged way of addressing biases. Ideally, the user should add the necessary information into the main knowledge graph repository and adjust the weighting policy based on that knowledge.

In order to do it, a laplacian reweighting dictionnary can be applied to an InteractomeInterface instance through the InteractomeInterface.apply_reweight_dict(lapl_reweight_dict) method. To do this, a reweight dictionary, mapping node ids to correction coefficients and node id pairs to new edge weights is supplied as lapl_reweigth_dict. New edge weights simply replace the old ones, whereas the node correction coefficient are used as a multiplier for all edges going in and coming out of the node to which it is applied. Note that internal db ids need to be supplied.

A way to supply a reweight dictionary is possible through the bioflow.molecular_network.interactome_analysis.auto_analyze through the forced_lapl_reweight parameter.

There is no similar function for BioKnowledge/GO in order to prevent the disruption of a a graph connex only thanks to a single connection in several locations.

Modification of Policies:

A lot of internal behaviors are controlled by “policies”, grouped inside bioflow .algorithms_bank package. Each module in the package allows control of specific aspect of knowledge graph construction and analysis. A bit more involved than modifying configs.yaml file for basic configurations, most policies can be matched by functions with similar signatures and provided as arguments to auto_analyze or InteractomeInterface or GOInterface instances.

Main knowledge graph parsing:

Given the difference in the topology and potential differences in the underlying assumptions, we pull the interactome knowledge network (where all nodes map to molecular entities and edges - to physical/chemical interaction between them) and teh annotome knowledge network (where some nodes might be concepts used to understand the biological systems - such as ontology terms or pathways) separately.

The parse for interactome is performed by retrieving all the nodes and edges whose parse_type is physical_entity for nodes and physical_entity_molecular_interaction, identity or refines. The giant component of the interactome is then extracted and two graph matrices - adjacency and laplacian - are build for it. Weights between the nodes are set in an additive manner according to the policy supplied as the argument to the InteractomeInterafce .full_rebuild function or, in a case a more granular approach is needed to the InteractomeInterafce.create_val_matrix function. By default the active_default_<adj/lapl>_weighting_policy functions are used from the bioflow.algorithms_bank.weigting_policies module. Resulting matrices are stored in the InteractomeInterface.adjacency_matrix and InteractomeInterface.laplacian_matrix instance variables, whears the maps between the matrix indexes and maps are stored in the .neo4j_id_2_matrix_index and .matrix_index_2_neo4j_id variables.

The parse for the annotome is performed in the same way, but matching parse_type for nodes to physical_entity and annotation. In case of a proper graph build, this will result only in the edges of types annotates and annotation_relationship to be pulled. Weighting functions are used in the similar manner, as well as the mappings storage.

Custom weighting function:

In order to account for different possible considerations when deciding which nodes and connections are more likely to be included in hypothesis generation, we provide a possibility for the end user to use their own weight functions for the interactome and the annotome.

The provided functions are stored in bioflow.algorithms_bank.weighting_policies module. An expected signature of the function is starting_node, ending_node, edge > float, where starting_node and ending_node are of <neo4j-driver>.Node type, whereas edge is of the <neo4j-driver>.Edge type. Any properties available stored in the main knowledge repository (neo4j database) will be available as dict-like properties of node/edge objects (<starting/ending>_node['<property>']/edge['property']).

The functions are to be provided to the bioflow.molecular_network .InteractomeInterface.InteractomeInterface.create_val_matrix() method as <adj/lapl>_weight_policy_function for the adjacency and laplacian matrices respectively.

Custom flow calculation function:

In case a specific algorithms to generate pairs of nodes between which to calculate the information flow is needed, it can be assigned to the InteractomeInterface ._flow_calculation_method. It’s call signature should conform to the list, list, int -> list signature, where the return list is the list of pairs of (node_idx, weight) tuples. By default, the general_flow method from bioflow.algorithms_bank.flow_calculation_methods will be used. It will try to match the expected flow calcualtion method based on the parameters provided (connex within a set if the secondary set is empty/None, star-like if the secondary set only has one element, biparty if the secondary set has more than one element).

Similarly, methods to evaluate the number of operations and to reduce their number to a maximum ceiling with the optional int argument sparse_rounds needs to be assigned to the InteractomeInterface._ops_evaluation_method and InteractomeInterface ._ops_reduction_method. By default, the are evaluate_ops and reduce_ops from bioflow.algorithms_bank.flow_calculation_methods.

Custom random set sampling strategy:

In case a custom algorithm for the generation of the background sample needs to be implemented, it should be supplied to the InteractomeInterace.randomly_sample method as the sampling_policy argument.

It is expected to accept the an example of sample and secondary sample to match, background from which to sample, number of samples desired and finally a single string parameter modifying the way it works (supplied by the sampling_policy_options parameter of the InteractomeInterace.randomly_sample method).

By default, this functions implemented by the matched_sampling fundion in the bioflow.algorithms_bank.sampling_policies module.

Custom significance evaluation:

by default, auto_analyze functions for the interactome and the annotome will use the default compare_to_blank functions and seek to determine the significance of flow based on comparison of the flow achieved by nodes of a given degree in the real sample compared to the random “mock” samples. The comparison will be performed using Gumbel_r function fitted to the highest flows achieved by the “mock” runs.

As of now, to change the mode of statistical significance evaluation, a user will need to re-implement the compare_to_blank functions and mokey-patch them in the modules containing the auto_analyze function.

GDF files:


In case of the uniprot nodes (used to start the analysis of the annotation, given the amount of cross-referencing they have), confusion potential, p-value and p_p-value variables have no meaning. For the ease of the presentation, they have been hard-coded to comparatively visible but non-obtrusive 1, 0.05 and 1.3 respectively. You might want to be aware when performing your p-value cutoffs, or alternatively just reweight them in the program you are using to analyze the network.