This workflow will provide
-an overview of how to use BNW to:
-
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1)Select
-a structure learning method and upload data into BNW
-
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2)Use
-the BNW structural restraint interface
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3)Make
-predictions with the network structure
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1.
-Select structure learning method and uploading data into BNW
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For this workflow, we are
-creating a network using a biological data set containing both continuous and
-discrete variables. The variables are a genotype (the discrete variable), three
-gene expression traits (Gene1, Gene2, Gene3), and a phenotype.
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We do not know the network
-structure for this dataset, so we will first use BNW to learn the structure. Selecting
-Learn a network model from data on the BNW home page presents a list of the
-three structure learning methods that are currently implemented in BNW.
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Because the network contains
-only 5 variables, we can use any of these structure learning methods. If more
-than 6 variables are present, using the global optimal search method would likely be necessary.
-We will use the exhaustive search with model averaging method in this workflow.
-More information about the structure learning methods can be found on the BNW
-help page.
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-
After selecting the
-Exhaustive Search button, we are prompted to upload a file containing the data.
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The figure
-below shows BNW after we have uploaded the data file. The first line of the
-data contains the names of the variables included in the network, while the
-remaining lines are the variable values for individual samples. A description
-of how to format data files for use in BNW can be found on the BNW help page and can be accessed by the the Data formatting guidelines option in the left menu. Additionally, two additional options, Select additional constraints or Perform Bayesian network modeling with no restraints, appear in the left menu after loading the data file.
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2.
-Use the structural constraint interface
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Here, we will use the
-structural constraint interface to help identify biologically meaningful
-network structures. Specifically, we want to investigate how the genotype
-impacts gene expression which then impacts the phenotype.
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The first
-section of the structural constraint interface allows users to assign the variables
-(nodes) in the network to tiers. By default, three tiers are shown, but this
-can be changed by selecting a different number in the drop-down menu. The
-leftmost box of this section contains draggable boxes with the names of the
-variables in the network.
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For this
-network, we want to assign Genotype to Tier1, the gene expression traits to
-Tier2, and the Phenotype to Tier3.
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The second
-section in the structural constraint interface allows users to specify the
-types of interactions that are allowed both between and within tiers. In this
-case, we will keep the default settings, which will allow there to be edges
-between nodes within a tier, prevent nodes in Tier1 from being the child of
-Tier2 and Tier3 nodes, and prevent nodes in Tier2 from being the child of Tier2
-nodes.
-
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-
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The final
-section of the structural constraint interface would allow for the
-specification of particular edges that should be banned from the network or
-required to be in the network. Users can identify these edges by dragging the
-nodes to appropriate boxes. In this case, we do not want to ban or require any
-specific edges.
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After
-entering the structural constraints, BNW will perform structure learning from
-the data after clicking Perform Bayesin network modeling on the left menu and
-present the structure of the network as shown below. Genotype, the discrete
-node, is shown as a bar chart with the bars showing the fraction of samples
-with each genotype in the data, while the other nodes are shown as lines with
-the Gaussian distributions that best fit the data.
-
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-
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To further
-investigate the structure of the network, users can select Display Structure
-Matrix in the left menu. This will bring up a popup windoe containing a table
-showing the confidence of each directed edge in the network after model
-averaging. For this network, all of the nodes included in the network were
-present in almost all high scoring networks, as the values of the edges are all
-near 1. BNW displays all edges with a confidence greater than 0.5 in the
-network structure. A second table in the window shows the structure matrix with
-a 1 for edges included in the structure and 0 for edges that are not included. These
-tables can be downloaded by clicking the download link.
-
-
-
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3) Make
-predictions with the network structure
-
-
BNW can be used to both
-predict the values of variables in a network given known evidence and to
-investigate how the network might change in response to interventions. Users
-can switch between evidence and intervention modes by selecting the proper
-button on the top of the page containing the network structure.
-
-
For example,
-suppose we want to use the model to make predictions for a new sample that was
-not in the original dataset. We know that this sample was from an individual
-with Genotype=2 and want to predict the values of the genes and phenotype for
-the individual. To enter this evidence in the network, we simply click on the
-Genotype node in the network and enter 2 in the popup window.
-
-
The figure below shows the
-changes in the network after entering this evidence.The node for which evidence was entered now contains a red outline and all of the nodes in the network now contain both blue and red lines. The blue lines show the original values of the distributions, while the red lines shown the predicted values given the
-evidence. In this case, knowing that the individual had Genotype=2 would cause
-us to predict that the expression of Gene1 and the value of the phenotype would
-be above average, while the expression of Gene2 and Gene3 would be decreases.
-The specific changes in the predicted values can be investigated by hovering
-over the nodes and observing the values at the peaks of the distribution.
-
-
-
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Evidence
-can be entered in more than one node in the network. For example, the figure
-below shows predicted values for the network after entering evidence for both
-Genotype and Gene1.
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-
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To use the
-network to predict the results of intervention, select the proper button on the
-top of the window to enter intervention mode. While evidence simply changes the
-predicted values of the other nodes in the network, intervention has a larger
-effect, as it removes the dependence of the intervened network on its parents
-and changes the network structure.
-
-
-
-
The figure
-below shows the changes in the network in BNW after intervention on Gene1 that
-results Gene1 having a low value. The predictions of the network after this
-intervention could be compared with experiments that prevent Gene1 from being
-expressed. Intervention that decreases Gene1 is predicted to result in an increase
-in Gene2 and a decrease in the phenotype. Note that Genotype and Gene3, which
-are not descendents of Gene1, are not affected by the intervention.
This workflow contains examples of using the BNW structural constraint interface to help identify biologically meaningful genetic network models for two cases:
-
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1)A genetic network linking genotype and phenotype
-
-
2)A genetic network with multiple genotypes and cis- and trans-regulated genes
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-
1.
-A genetic network linking genotype and phenotype
-
-
In this example, we will use the structural constraint interface to create a genetic network linking a genotype with intermediated phenotypes (i.e., gene expression or other cellular level traits) and a higher-order phenotype. The figure below shows a screenshot of BNW after loading the data file and selecting Go to structure learning settings and the BNW structural constraint interface. There are 5 nodes in the network, Genotype, Int1, Int2, Int3, and a Phenotype.
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We want to assign the nodes to three tiers in this example, so we will keep the number of tiers at the default setting, and drag the nodes to the proper tiers.
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In this example, we do not want the genotype to be the direct parent of the phenotype, so we made some changes to the Define interactions allowed between tiers section of the structural constraint interface. Specifically, we have unchecked two boxes that are different from the default settings: (1) we have unchecked the box that allows the node(s) in Tier1 from being the parents of the node(s) in Tier3 and (2) we have unchecked the box that allows the node(s) in Tier3 from being the children of the node(s) in Tier1. Actually, unchecking either of these boxes would have been sufficient in preventing direct interactions between Genotype and Phenotype, but, there is no harm in unchecking both boxes. There are no additional specific edges that we want to ban or require in the network, so we do not have to add any edges to the Specify additional constraint section and we can proceed with structure learning.
-
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2.
-A genetic network with multiple genotypes and cis- and trans-regulated genes.
-
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In this example, we will add restrictions to a network containing 8 nodes: 2 genotype nodes (Geno1 and Geno2), 3 cis-regulated gene expression traits (cisGene1, cisGene2, and cisGene3), 2 trans-regulated gene expression traits (transGene1 and transGene2), and a phenotype (Pheno). We have four tiers of nodes (genotypes, cis-regulated genes, trans-regulated genes, and phenotype), so we have selected 4 from the dropdown menu at the top of the page and assigned the nodes to the correct tiers. We could make a more complex system of tiers that would allow us to specify which genes are regulated by which genotypes (for example, Geno1 regulates cisGene1 and transGene1, while Geno2 regulates cisGene2, cisGene3, and transGene2), but, for this example, we will use a simpler system of 4 tiers.
-
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We have made one change to the default setting in the Define interactions allowed between tiers section. For Tier1, which contains the genotypes, we have selected "No" for the "Are within tier interactions allowed?", as it does not make biological sense for one genotype variation to cause the variation in another genotype in this examples.
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Assume that a known regulatory relationship between cisGene1 and transGene1 has been established from previous experiments. We can require that this relationship is included in the network by adding the edge list of required edges in the Specify additional constraints section.
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diff --git a/sourcecodes/BNW_workflow_net1.htm b/sourcecodes/BNW_workflow_net1.htm
index aafab31f..3ad19201 100644
--- a/sourcecodes/BNW_workflow_net1.htm
+++ b/sourcecodes/BNW_workflow_net1.htm
@@ -294,7 +294,7 @@ ul
line-height:115%;font-family:"Arial","sans-serif"'>
This tutorial provides
-an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available here.
The data file is formatted according to the guidelines on the BNW help page. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which have two possible states (1 and 2), are the only discrete variables in the network and are the leftmost variables in the input file. The quantitative traits are continuous variables.
+an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available here.
The data file is formatted according to the guidelines on the BNW help page. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which have two possible states (1 and 2), are the only discrete variables in the network. The quantitative traits are continuous variables.
1. Structure learning using default options
@@ -303,17 +303,17 @@ normal'>We do not know the network structure that underlies the relationships between the variables in this dataset, so we will use BNW to learn the structure that best explains the data. To begin, select Learn a network model from data from the BNW home page. Next, click Choose File at the top of the file upload page, navigate to and select the data file, and click Upload. A screen similar to image shown below should be displayed.
Clicking on the View uploaded variables and data on the left menu will bring up a pop-up window that displays the uploaded data file or information about the data set, such whether variables were classified as discrete or continuous. This information can be used to ensure that the input file was uploaded and properly interpreted in BNW.
Other options on the left menu will continue with structure learning. Initially, we will perform structure learning using default options in BNW and click on the Perform Bayesian network modeling using default settings button. By default, BNW limits the maximum number of parents for any node in the network to 4 and presents the structure of the single highest scoring network. Structure learning can take a significant amount of time for larger networks. For this dataset, structure learning should take only a few seconds, and the structure below will soon be displayed. The network can be also be accessed here.
+line-height:115%;font-family:"Arial","sans-serif"'>The buttons on the top of the page allow you to perform Bayesian network modeling using default settings; modify structure learning settings and add structural constraints; or remove variables from the data set. Under these buttons, a description of the loaded data set is provided, including if BNW classified the variables as discrete or continuous. In this case, the uploaded data file contained 8 variables and had 200 cases. Two variables (Geno1 and Geno2) were determined to be discrete variables because they had only 2 different values in the input data file. The other six variables were continuous variables with the means and standard deviations shown in the table.
Initially, we will perform structure learning using default options in BNW and click on the Perform Bayesian network modeling using default settings button. By default, BNW limits the maximum number of parents for any node in the network to 4 and presents the structure of the single highest scoring network. Structure learning can take a significant amount of time for larger networks. For this dataset, structure learning should take only a few seconds, and the structure below will soon be displayed. The network can be also be accessed here.
-
+
In order to test if edges present the single best scoring network are conserved across high scoring networks. We can modify the structure learning settings to get identify the structures of many high scoring networks and perform model averaging over these structures. To do this, return to the BNW home page, select Learn a network model from data, and upload the data file. Instead of using the default settings, select Go to structure learning settings and the BNW structural constraint interface. A more detailed overview of use of the structural constraint interface is provided in another tutorial, but, here, we will investigate the impact of modifying some of the structure learning settings shown below:
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>In order to test if edges present the single best scoring network are conserved across high scoring networks, we can modify the structure learning settings to identify the structures of many high scoring networks and perform model averaging over these structures. To do this, click the Modify network strucutre button on the left of the page, opening a menu with three options that can be used to modify the network structure. Add or remove edges from network provides an interface for making specific changes to the network structure. The use of this interface is discussed here. In this tutorial, we are examining the impact of changing the settings used during structure learning on the network structure and click on Modify structure learning settings. We then select Go to structure learning settings and the BNW structural constraint interface on the resulting page. A more detailed overview of use of the structural constraint interface is provided in another tutorial.
Here, we only change the Number of networks to include in model averaging to 1000 and select Continue to assign variables to tiers. In this case, we will not assign variables to tiers, and can immediately click Click here to perform Bayesian network modeling after creating tiers. Now, instead of displaying the single highest scoring network, BNW will determine the 1000 highest scoring networks, perform model averaing over these networks, and display the structure after model averaging that includes all features with a Model averaging edge selection threshold greater than 0.5. Model averaging over the 1000 highest scoring structures has resulted in a change in the network structure as shown below and is available here.
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+
Change the Number of networks to include in model averaging to 100 and select Perform Bayesian network modeling. Now, instead of displaying the single highest scoring network, BNW will determine the 100 highest scoring networks, perform model averaing over these networks, and display the structure after model averaging that includes all features with a Model averaging edge selection threshold greater than 0.5. Model averaging over the 100 highest scoring structures has resulted in a change in the network structure as shown below and is available here.
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> Specifically, the single best model network contains a directed edge linking Trait6 with Trait3, while this edge is absent from the structure after model averaging over the 1000 best scoring networks. More information about the network structure can be viewed by clicking Network image options on the left menu and, then, Show network with edge weights. The network structure with the edges labeled by their scores after model averaging will be displayed.
-
+
Specifically, the single best model network contains a directed edge linking Trait6 with Trait3, while this edge is absent from the structure after model averaging over the 100 best scoring networks. Clicking Display structure matrix displays the model averaging scores as well as the structure matrix. The structure matrix file can be downloaded and used in return sessions to BNW, allowing users to skip structure learning and more quickly use the model to make predictions.
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In the model averaging scores, we can observe that most of the network edges in the model average network were found in all or nearly all of the 100 highest scoring networks. For example, edges from Geno2 to Traits 3 and 4 have posterior probabilities of 1. Also, the edge from Trait6 to Trait3 that was observed in the single highest scoring network has a posterior probability of 0.46, and it, therefore, falls just short of being included in the model averaged network structure.
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> The model averaging scores show that the edges in this network model were found in all or nearly all of the 1000 highest scoring networks, and, thus, have scores of 0.99 or 1.00. For example, edges from Geno2 to Traits 3 and 4 have scores of 1. The model averaging scores for all possible directed edges in the network can be found by clicking View structure matrix under the More about network menu. The data provided in the structure matrix shows that the edge from Trait6 to Trait3 that was observed in the single highest scoring network has a posterior probability of 0.40, and it, therefore, falls short of being included in the model averaged network structure. The structure matrix file can be downloaded and used in return sessions to BNW, allowing users to skip structure learning and more quickly use the model to make predictions.
3. Using the network structure to make predictions
To make predictions with the network, we will use the structure learned after model averaging of the top 100 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will use evidence mode when making predictions, which is the default behavior in BNW. The difference between evidence and intervention modes is discussed in the BNW FAQ page. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>The network model can be used by make predictions through an interactive interface after clicking on the Use network to make predictions button on the left menu. To make predictions with the network, we will use the structure learned after model averaging of the top 1000 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will use evidence mode when making predictions, which is the default behavior in BNW. The difference between evidence and intervention modes is discussed in the BNW FAQ page. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.
-
+
If Geno1 has genotype 2, the value of Traits 1, 2, and 4 are expected to increase compared with the distribution for all data. Specifically, the mean value of Trait2 is expected to be near 1 for Geno1=2 data, while it is close to 0 when this evidence is not known. Traits 1 and 4 are also expected to increase, but the magnitude of this increase is not expected to be as large. Predicted distributions for the other nodes in the network, which are not descendants of Geno1, are expected to be close to the same as their original distributions, and, the red line covers the blue line for some nodes.
-To quantitatively assess the impact of this evidence on the network predictions, the View parameters button on the left menu can be selected. Clicking this button brings up a pop-up window with the network parameters (i.e., the probability distributions of the states of discrete nodes and the means and standard deviations of the Gaussian distributions for continuous nodes) for both the original data set and the data when considering the entered evidence.
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> If Geno1 has genotype 2, the value of Traits 1, 2, and 4 are expected to increase compared with the distribution for all data. Specifically, the mean value of Trait2 is expected to be near 1 for Geno1=2 data, while it is close to 0 when this evidence is not known. Traits 1 and 4 are also expected to increase, but the magnitude of this increase is not expected to be as large.
+To quantitatively assess the impact of this evidence on the network predictions, the View parameters button within the More about network parameters and structure menu can be selected. Clicking this button brings up a pop-up window with the network parameters (i.e., the probability distributions of the states of discrete nodes and the means and standard deviations of the Gaussian distributions for continuous nodes) for both the original data set and the data when considering the entered evidence.
Evidence for multiple nodes can be considered at the same time by clicking on a new node in the network and entering a value. Alternatively, users can select Clear evidence to reset the network to show the orignial distributions in a new tab.
Next, we will make predictions using the intervention mode. To use the prediction mode, click the button next to Intervention at the top of the page. The Selected mode tab on the left of the screan should now display Intervention. The effects of experimental intervention on Trait2 can be predicted by clicking on the blue line in the Trait2 node and entering a value for the variable. The figure below shows the network after setting Trait2 to a value of 1.5.
-
+
In this example, we will use the structural constraint interface to create a genetic network linking a genotype with gene expression traits and a higher-order phenotype. There are 5 nodes in the network: Genotype, Gene1, Gene2, Gene3, and Phenotype. The input data file is available here. To begin, select Learn a network model from data on the BNW homepage and load the data file, displaying what is shown below:
-
+
We now have the option of either learning performing structure learning using the default BNW settings and no additional structural constraints or we can use modify settings and add structural constraints. Here, we will select Go to structure learning settings and the BNW structural constraint interface to select the latter option. The BNW structural constraint interface is described in our Help page. At shown in the figure below, the top of the page has several settings for global features of the structure search. We have kept the default settings, except we have changed the Number of networks to include in model averaging to 1000. As this is a small network with only 5 nodes, including many high scoring networks had little effect on the estimated time required to perform structure learning, and the estimated run time increased from 12 to 13 seconds. We also could increase the Maximum number of parents for any node to any value without significantly changing the run time for a network of this size. For larger networks with more than approximately 10 nodes, changing these settings can have a major impact on estimated run times.
+line-height:115%;font-family:"Arial","sans-serif"'> We now have the option of either learning performing structure learning using the default BNW settings or we can modify settings and add structural constraints. Here, we will select Go to structure learning settings and the BNW structural constraint interface to select the latter option. The BNW structural constraint interface is described in our Help page. At shown in the figure below, the first page has several settings for global features of the structure search. We have kept the default settings, except we have changed the Number of networks to include in model averaging to 1000. As this is a small network with only 5 nodes, including many high scoring networks had little effect on the estimated time required to perform structure learning, and the estimated run time increased from 12 to 13 seconds. We also could increase the Maximum number of parents for any node to any value without significantly changing the run time for a network of this size. For larger networks with more than approximately 10 nodes, changing these settings can have a major impact on estimated run times.
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+
In the next section of the structural constraint interface, we assign nodes to tiers which will can be used to focus network searches on biologically meaningful networks. Our dataset contains a genotype, three gene expression traits, and a higher order phenotype. Instead of considering all possible network models for this dataset, we may want to focus on models relevant to a questions such as: How does variation in genotype and gene expression explain the variation observed in the phenotype? To address this question, we assign the network nodes to three tiers: Tier1 contains the genotype, Tier2 contains the gene expression traits, and Tier3 contains the phenotype.
+line-height:115%;font-family:"Arial","sans-serif"'> Clicking the Continue to assign variables to tiers button allows the assignment of nodes to tiers which can focus network searches on biologically meaningful networks. Our dataset contains a genotype, three gene expression traits, and a higher order phenotype. Instead of considering all possible network models for this dataset, we may want to focus on models relevant to a questions such as: How does variation in genotype and gene expression explain the variation observed in the phenotype? To address this question, we assign the network nodes to three tiers: Tier1 contains the genotype, Tier2 contains the gene expression traits, and Tier3 contains the phenotype.
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+
The third section of the BNW structural constraint interface allows users to specify the interactions that are allowed within and between tiers. By default, within tier interactions (i.e., nodes in TierX can be parents or children of other nodes in TierX) are allowed, but users may want to prevent within tier interactions for some cases. For example, it may be advantageous to prevent interactions between a tier that contained a set of some demographic variables (e.g., age, sex, and race), as these variables are not likely to be causal factor that influence other factors in the tier. In this case, within tier interactions only apply to Tier2, and we do not have any prior knowledge that indicates that between gene interactions should not be allowed, so we will keep the default setting and allow within tier interactions.
The default settings for between tier interactions allow nodes within a tier to be the parents of all nodes in lower ranking tiers. Here, the Genotype node in Tier1 can be parents of the gene nodes in Tier2 and the Phenotype node in Tier3, the gene nodes in Tier2 can be the parents of the Tier3 Phenotype node, and the Tier3 Phenotype node cannot be the parents of nodes in any other tier. Therefore, by default, the Genotype node can be the direct parent of the Phenotype node. We may want to allow this interaction, as the genotype may influence genotype through genes or other factors that are not explicitly included as variables in the network. If users do not want to allow this direct Genotype-Phenotype interaction, they can unclick the Tier3 box in the Which tiers contain nodes that can be the children of this tier? for Tier1. In this case, we have maintained the default settings which are shown below.
+line-height:115%;font-family:"Arial","sans-serif"'> The third section of the BNW structural constraint interface allows users to specify the interactions that are allowed within and between tiers. By default, within tier interactions (i.e., nodes in TierX can be parents or children of other nodes in TierX) are allowed, but users may want to prevent within tier interactions for some cases. For example, it may be advantageous to prevent interactions between a tier that contained a set of some demographic variables (e.g., age, sex, and race), as these variables are not likely to be causal factor that influence other factors in the tier. In this case, within tier interactions only apply to Tier2, and we do not have any prior knowledge that indicates that between gene interactions should not be allowed, so we will keep the default setting and allow within tier interactions.
The default settings for between tier interactions allow nodes within a tier to be the parents of all nodes in lower ranking tiers. Here, the Genotype node in Tier1 can be parents of the gene nodes in Tier2 and the Phenotype node in Tier3, the gene nodes in Tier2 can be the parents of the Tier3 Phenotype node, and the Tier3 Phenotype node cannot be the parents of nodes in any other tier. Therefore, by default, the Genotype node can be the direct parent of the Phenotype node. We may want to allow this interaction, as the genotype may influence genotype through genes or other factors that are not explicitly included as variables in the network. If users do not want to allow this direct Genotype-Phenotype interaction, they can unclick the Tier3 box in the Which tiers contain nodes that can be the children of this tier? for Tier1. In this case, we have maintained the default settings which are shown below. The final section of this menu allows for listing specific edges that should be banned or required in the network structures. We will not ban or require any specific edges in this network.
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+
In this example, we will not specify any additional constraints in the fourth section of the structural constraint interface, and we can click Perform Bayesian network modeling on the upper left corner of the page. The figure below shows the network with the model average network of the 1000 highest scoring networks and this network is available here. Genotype directly influences two of the genes (Gene1 and Gene3), and two of the genes (Gene2 and Gene3) directly influence the Phenotype. In this case, although we did not prevent the Genotype from directly influencing the Phenotype, the highest scoring networks did not include this directed edge. The right side of the figure shows the predictions of the network with Genotype=1 used as evidence. If Genotype is known to be in state 1, the values of all other variables in the network are expected to decrease compared to the distributions learned using all phenotypes. A more complete description of using BNW to make predictions with network models can be found in a separate tutorial.
+line-height:115%;font-family:"Arial","sans-serif"'> In this example, we will not specify any additional constraints in the fourth section of the structural constraint interface, and we can click Click here to perform Bayesian network modeling on the top of the page. The figure below shows the network with the model average network of the 1000 highest scoring networks and this network is available here. Genotype directly influences two of the genes (Gene1 and Gene3), and two of the genes (Gene2 and Gene3) directly influence the Phenotype. In this case, although we did not prevent the Genotype from directly influencing the Phenotype, the highest scoring networks did not include this directed edge. The right side of the figure shows the predictions of the network with Genotype=1 used as evidence. If Genotype is known to be in state 1, the values of all other variables in the network are expected to decrease compared to the distributions learned using all phenotypes. A more complete description of using BNW to make predictions with network models can be found in a separate tutorial.
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In this exa
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We have made one change to the default setting in the Define interactions allowed between tiers section. For Tier1, which contains the genotypes, we have selected "No" for the "Are within tier interactions allowed?", as it does not make biological sense for one genotype variation to cause the variation in another genotype in this example.
+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> We have made one change to the default setting in the Define interactions allowed between tiers section. For Tier1, which contains the genotypes, we have selected "No" for the "Are within tier interactions allowed?", as it does not make biological sense for one genotype variation to cause the variation in another genotype in this example.
Also, assume that a known regulatory relationship between cisGene1 and transGene1 has been established from previous experiments. We can require that this relationship is included in the network by adding the edge list of required edges in the Specify additional constraints section.
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+
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Assume that a known regulatory relationship between cisGene1 and transGene1 has been established from previous experiments. We can require that this relationship is included in the network by adding the edge list of required edges in the Specify additional constraints section.
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