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+ mso-ascii-theme-font:minor-latin; + mso-hansi-font-family:Calibri; + mso-hansi-theme-font:minor-latin;} +</style> +<![endif]--><!--[if gte mso 9]><xml> + <o:shapedefaults v:ext="edit" spidmax="4098"/> +</xml><![endif]--><!--[if gte mso 9]><xml> + <o:shapelayout v:ext="edit"> + <o:idmap v:ext="edit" data="1"/> + </o:shapelayout></xml><![endif]--> +</head> + +<body lang=EN-US style='tab-interval:.5in'> + +<div class=WordSection1> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>This workflow will provide +an overview of how to use BNW to:<o:p></o:p></span></p> + +<p class=MsoListParagraphCxSpFirst style='margin-right:307.5pt;mso-add-space: +auto;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"; +mso-fareast-font-family:Arial'><span style='mso-list:Ignore'>1)<span +style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"'>Select +a structure learning method and upload data into BNW<o:p></o:p></span></p> + +<p class=MsoListParagraphCxSpMiddle style='margin-right:307.5pt;mso-add-space: +auto;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"; +mso-fareast-font-family:Arial'><span style='mso-list:Ignore'>2)<span +style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"'>Use +the BNW structural restraint interface<o:p></o:p></span></p> + +<p class=MsoListParagraphCxSpLast style='margin-right:307.5pt;mso-add-space: +auto;text-indent:-.25in;mso-list:l1 level1 lfo2'><![if !supportLists]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"; +mso-fareast-font-family:Arial'><span style='mso-list:Ignore'>3)<span +style='font:7.0pt "Times New Roman"'> </span></span></span><![endif]><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"'>Make +predictions with the network structure<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><b style='mso-bidi-font-weight: +normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans-serif"'>1. +Select structure learning method and uploading data into BNW<o:p></o:p></span></b></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>We do not know the network +structure for this dataset, so we will first use BNW to learn the structure. Selecting +<u>Learn a network model from data</u> on the BNW home page presents a list of the +three structure learning methods that are currently implemented in BNW. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shapetype + id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" + path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> + <v:stroke joinstyle="miter"/> + <v:formulas> + <v:f eqn="if lineDrawn pixelLineWidth 0"/> + <v:f eqn="sum @0 1 0"/> + <v:f eqn="sum 0 0 @1"/> + <v:f eqn="prod @2 1 2"/> + <v:f eqn="prod @3 21600 pixelWidth"/> + <v:f eqn="prod @3 21600 pixelHeight"/> + <v:f eqn="sum @0 0 1"/> + <v:f eqn="prod @6 1 2"/> + <v:f eqn="prod @7 21600 pixelWidth"/> + <v:f eqn="sum @8 21600 0"/> + <v:f eqn="prod @7 21600 pixelHeight"/> + <v:f eqn="sum @10 21600 0"/> + </v:formulas> + <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> + <o:lock v:ext="edit" aspectratio="t"/> +</v:shapetype><v:shape id="Picture_x0020_2" o:spid="_x0000_i1037" type="#_x0000_t75" + style='width:300pt;height:117pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image003.jpg" o:title="" croptop="19333f" + cropbottom="34461f" cropleft="11633f" cropright="33260f"/> +</v:shape><![endif]--><![if !vml]><img width=450 height=156 +src="BNW_workflow_test_files/image003.jpg" v:shapes="Picture_x0020_2"><![endif]></span><span +style='font-size:12.0pt;line-height:115%;font-family:"Arial","sans-serif"'><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>After selecting the +Exhaustive Search button, we are prompted to upload a file containing the data. +<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> +<img width=700 height=225 +src="BNW_workflow_test_files/image006.jpg" v:shapes="Picture_x0020_5"><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><br>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 <u>Data formatting guidelines</u> option in the left menu. Additionally, two additional options, <u>Select additional constraints</u> or <u>Perform Bayesian network modeling with no restraints</u>, appear in the left menu after loading the data file.<o:p></o:p></span></p><br> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> +<![if !vml]><img width=675 height=225 +src="BNW_workflow_test_files/image008.jpg" v:shapes="Picture_x0020_8"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><b style='mso-bidi-font-weight: +normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans-serif"'>2. +Use the structural constraint interface<o:p></o:p></span></b></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><o:p> </o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> +<![if !vml]><img width=750 height=300 +src="BNW_workflow_test_files/image010.jpg" v:shapes="Picture_x0020_14"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-top:0in;margin-right:307.5pt;margin-bottom: +0in;margin-left:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>For this +network, we want to assign Genotype to Tier1, the gene expression traits to +Tier2, and the Phenotype to Tier3.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> +<![if !vml]><img width=750 height=300 +src="BNW_workflow_test_files/image012.jpg" v:shapes="Picture_x0020_17"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><![if !vml]><img width=475 height=200 +src="BNW_workflow_test_files/image014.jpg" v:shapes="Picture_x0020_20"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'> +<![if !vml]><img width=430 height=227 +src="BNW_workflow_test_files/image016.jpg" v:shapes="Picture_x0020_23"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>After +entering the structural constraints, BNW will perform structure learning from +the data after clicking <u>Perform Bayesin network modeling</u> 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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_26" o:spid="_x0000_i1030" type="#_x0000_t75" style='width:367.5pt; + height:400.5pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image017.png" o:title="" croptop="11774f" + cropbottom="5139f" cropleft="15267f" cropright="16897f"/> +</v:shape><![endif]--><![if !vml]><img width=490 height=534 +src="BNW_workflow_test_files/image018.jpg" v:shapes="Picture_x0020_26"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>To further +investigate the structure of the network, users can select <u>Display Structure +Matrix</u> 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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_41" o:spid="_x0000_i1029" type="#_x0000_t75" style='width:358.5pt; + height:158.25pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image033.png" o:title="" croptop="19224f" + cropbottom="21354f" cropleft="10363f" cropright="12698f"/> +</v:shape><![endif]--><![if !vml]><img width=478 height=211 +src="BNW_workflow_test_files/image020.jpg" v:shapes="Picture_x0020_41"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><b style='mso-bidi-font-weight: +normal'><span style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans-serif"; +mso-no-proof:yes'>3) </span></b><b style='mso-bidi-font-weight:normal'><span +style='font-size:14.0pt;line-height:115%;font-family:"Arial","sans-serif"'>Make +predictions with the network structure<o:p></o:p></span></b></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>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. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_29" o:spid="_x0000_i1028" type="#_x0000_t75" style='width:352.5pt; + height:464.25pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image019.png" o:title="" croptop="5662f" + cropbottom="3557f" cropleft="15900f" cropright="17569f"/> +</v:shape><![endif]--><![if !vml]><img width=470 height=619 +src="BNW_workflow_test_files/image022.jpg" v:shapes="Picture_x0020_29"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_32" o:spid="_x0000_i1027" type="#_x0000_t75" style='width:291.75pt; + height:414pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image021.png" o:title="" croptop="9437f" + cropbottom="5850f" cropleft="16919f" cropright="22117f"/> +</v:shape><![endif]--><![if !vml]><img width=389 height=552 +src="BNW_workflow_test_files/image024.jpg" v:shapes="Picture_x0020_32"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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. <o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_35" o:spid="_x0000_i1026" type="#_x0000_t75" style='width:295.5pt; + height:417.75pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image023.png" o:title="" croptop="9437f" + cropbottom="5375f" cropleft="16718f" cropright="21976f"/> +</v:shape><![endif]--><![if !vml]><img width=394 height=557 +src="BNW_workflow_test_files/image026.jpg" v:shapes="Picture_x0020_35"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>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.<o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><!--[if gte vml 1]><v:shape + id="Picture_x0020_38" o:spid="_x0000_i1025" type="#_x0000_t75" style='width:297.75pt; + height:406.5pt;visibility:visible;mso-wrap-style:square'> + <v:imagedata src="BNW_workflow_test_files/image025.png" o:title="" croptop="9382f" + cropbottom="6755f" cropleft="16815f" cropright="21642f"/> +</v:shape><![endif]--><![if !vml]><img width=397 height=542 +src="BNW_workflow_test_files/image039.jpg" v:shapes="Picture_x0020_38"><![endif]><o:p></o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'><o:p> </o:p></span></p> + +<p class=MsoNormal style='margin-right:307.5pt'><span style='font-size:12.0pt; +line-height:115%;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p> + +</div> + +</body> + +</html> |