This demo will show the analysis functionality of Phenom-Networks based on a dataset generated in the Hebrew University, the Faculty of Agriculture in Dani Zamirs laboratory with additional data generated at Cold Spring Harbor in Zachary Lippmans laboratory. All the data presented here has been prepared for publication in Krieger et al, (2009). To initiate the tutorial you will need to select the Tomatoes database and log in the system either as a guest or as a registered user..

Content Slides 3-23: General demonstration of some of the analysis capabilities of Phenom Networks. Slides 24-33: Recipes to generate an equivalent analysis as in Figure 1 (B and C) of Krieger et al (Submitted).

After you log in, go to Phenotype -> analysis. This is the main analysis page of the system which allows the user to perform statistical analysis of the data. The analysis options appear on the left side, organized into categories like univariate, multivariate and so on. In the middle, there is a table that lists all traits and on the right there are fields (which depend on the selected analysis) that will contain the traits that will be analyzed. The toolbar includes filtering possibilties to subset the data as well as other options. Phenotype -> analysis

The first step before starting to analyze is to select the study or studies that you are interested in. For this click on the studies filter button from the toolbar. This will open a popup window that contain all available studies. Lets select the mutant ODO 09 Akko study and click on OK. Studies filter button

After you click OK, the popup window disappears, the icon on the studies filter button is changed to filtered mode and the selected study is indicated on the status bar (the green line on the bottom). The traits list now presents only the traits that were measured in the selected study. Now we are ready to invoke the analyses. Studies filter changed to filter mode The selected study is indicated in the status bar

Lets select the Fit Y by X option (this follows JMP terminology. See: ). In order to define the analysis, we need to select the traits and put them in the appropriate fields (on the right). We can think of the traits as columns of a table and by assigning them to roles (X or Y variables in this case) we define the analysis. Lets select for example the traits Total yield and Brix. After they are selected (you can use the Ctrl button to select them both) click on the Y. variables to move them to the field. Then select the GERMPLASM IDENTIFICATION (breeder name) and put it on the X variables field. This is the trait that defines the genotype (This terminology was defined by the researcher when he uploaded the data). finally click OK. 1) Select Fit Y by X2) Select Brix + Total yield and click on the Y variables button 3) Select GERMPLASM IDENTIFICATION – breeder name and click on the X variables button. 4) Click OK

The result of the analysis is shown on a separate tab - Result 1 in this case. It presents two figures: one for total yield and another for brix. Similarly, I could select more traits as Y variables and generate more figures – one for each trait. Explanation about the figure: the X axis shows all the levels of the trait GERMPLAS IDENTIFICATION (that I selected as X variables), which are the genotypes names. The data points above each level represent all observation units of the particular genotype and their Y axis shows the measurements of the trait, which is indicated on the figures title (Total yield for the first figure). The diamonds middle horizontal line indicate the genotypes mean, and the diamonds length is the confidence interval. Pointing the mouse on each data point will show the observation unit ID and its value. The Show table button will display the table used for this calculation with the possibility to download it to Excel.

Save analysis – the output of each analysis can be saved by clicking on the Save button. A popup window will appear showing previously stored analyses (if any). you can then give a name to the current analysis, select the folder and click Save. 1) Click on Save 2) The popup window show previous analyses (if any). Give a name and click Save. Only registered users can use this functionality and save analyses. Guests are not allowed to do this.

We can go back to the Form tab. Now Ill show an example of multivariate analysis. For this lets pick the bars option under Multivariate (from the left panel). Note that the traits we classified before stayed in their fields, so we can just click the OK button.

In Multivariate analyses, a single figure is generated that shows all traits in it (in contrast to Univariate where it produces a figure for each trait). Here, the zero line represents the mean value for each trait and the bars show the genotypes value for each trait as percent of the total mean. We can generate this figure such that the zero line will represent one of genotypes mean instead of the total mean (Ill show that later). You can point the mouse on each bar to see its corresponding genotype and trait. You can redo this analysis by picking more than two traits. It will show them all.

lets go back to the Form tab, select another multivariate analysis - means heatmap, then select all variates and move them to the Y variables field. Then click OK.

The output shows a heatmap where all genotypes are on the Y axis and all traits are in the X axis. Each little colored rectangle indicates the mean value of the corresponding genotype in the corresponding trait; green color indicate low value, red is high and black is average (see the color scale on the top left of the figure). You can point the mouse on each such rectangle to see its corresponding genotype and trait.

Now Ill redo the Fit Y by X analysis of the trait Total yield, but this time Ill split the output according to additional factor – hormones spray. In order to test the effect of the hormone treatment, some of the plants were treated with an anti-Gibberellic acid hormone spray in the nursery, and some were not. This analysis will generate two figures: the first includes only treated plants and the second non treated plants. For doing this I go to the split by section on the rightmost side and choose the Factor option. Another field will appear (By) below the X variables. in this field I put the hormones spray factor. Then press OK. 1) Split by factor 2) Add hormones spray to the By field

Here are the results: two figures were generated for total yield: the first for plants that were not treated with hormones (indicated on the figures title as hormones spray of level: 0), and the second for plants that treated with hormones.

In this analysis I want to include only some of the genotypes that were included in the study, instead of doing it over all of them. For this I click on the factors filter button in the toolbar (the second button). A popup window appears that display on its top a list of all available factors. As a first step I select the GERMPLASM IDENTIFACTION – breeder name factor and then under Conditions I choose the in operator. This will load all genotype names to the labels table, from which I can select the desired genotypes and add them to the conditions list using the button Add condition. Then click on OK. This filtering functionality is equivalent to selecting of particular rows from the table. Factors filter

This is how the page looks like after I finished the filtering. The factors filter button was changed to filtered mode, and the factor that was filtered is indicated on the status bar (bottom). Now I can click OK. Factors filter changed to filtered mode Indiacation in the status bar

Now the figure shows only the selected genotypes. When you click the button Show table (on the bottom), three tables will appear and the button will change to Hide table. Show / Hide tables

I can perform means comparison by clicking the Compare means link (on the right) that invoke a new window. In the window I select the statistical test (Students t, Dunnetts or Tukey), then the a threshold (defaults to be 0.05), and finally I choose the genotype (level) to compare to. 1) Compare means statistically 2) Select Dunnett 3) Set alpha threshold 4) Select control

The output displays the M82 genotype in red bold that indicates that this is the level that all other genotypes are compared to. Then, sft- stop is in light-red, indicating that it is not significantly different than M82 under the selected a threshold (0.05). All other genotypes are black, meaning they are significantly different from M82.

If we supply the GERMPLASM IDENTIFICATION – SFT combined to the X variables field (instead of breeder name), itll combine all alleles of sft into a single genotype. We select SFT combined instead of breeder name, and put it on the X variables.

All SFT alleles are combined together. Thus sft- 4537, sft-7187 and sft-stop are all named sft. Similarly, sft-4537 x M82, sft-7187 x M82 and sft-stop x M82 are all under sft x M82. The statistical analysis shows all genotypes are significantly different than M82 (as they are written in black and not in red).

Here I select two experiments (in Akko and in Massarik). In case you choose split by: study, the system will output a figure for each study. If you choose split by: none, all studies will be combined together in a single figure

This is the result of the two studies that are combined into a single figure. The red points represent the replications from the first study and the blue from the second one.

Krieger et al (unpub): Figure 1 The next slides will explain how to produce a figure that is equivalent to figure 1 of Krieger et al.

Click on the Studies filter button and select the appropriate study: mutant ODO 09 Akko. 1) Click on the studies filter button to open the studies window. 2) Select the appropriate study and click OK.

1) Click on the factors filter to open the filter window. 2) From the factor list select GERMPLASM IDENTIFICATION – breeder name. 3) Select the in operator from the list. 4) Select the genotypes (mutants) you want to include in the figure: M82, AB2, sft-4537, sft-4537 x M82, sft-7187, sft-7187 x M82, sft-stop, sft x M82. 5) Click on Add condition. Here I define the genotypes I want to include in the figure and add it to the conditions list. 1) Click on the factors filter to open the filter window. 2) From the factor list select GERMPLASM IDENTIFICATION – breeder name. 3) Select the in operator from the list. 1) Click on the factors filter to open the filter window. 2) From the factor list select GERMPLASM IDENTIFICATION – breeder name. 4) Select the genotypes (mutants) you want to include in the figure: M82, AB2, sft-4537, sft-4537 x M82, sft-7187, sft-7187 x M82, sft-stop, sft x M82. 3) Select the in operator from the list. 1) Click on the factors filter to open the filter window. 2) From the factor list select GERMPLASM IDENTIFICATION – breeder name.

Here I add another condition: use only genotypes that were not derived (segregated) from F2 family. 1) select F2 segregation status. 2) Select the in operator from the list. 4) Click on Add condition to add it to the conditions list 3) Select the 0 label which means that this genotype was not derived by segregation.

Sft-stop x M82 plants in this study were segregated from F2 family. There were no plants of this genotype that were produced by a fixed cross of M82 and sft-stop. Nevertheless, we want to include them in our analysis. By adding another group of conditions we define the logic to be: condition1 AND condition2 in the first group OR condition3 from the second group. The logic operator is AND over all conditions within a group and OR among groups. 1) From the factor list select GERMPLASM IDENTIFICATION – breeder name. 2) Select the in operator from the list. 3) Select sft-stop x M82. 4) Add another group of conditions 5) Click on add condition to the new group. 6) finally, click OK on the bottom of the window. This is a group of conditions

After Im done with the conditions, the factors filter icon is changed to filtered mode. Now I can select the Fit Y by X analysis (from the left) and put Total yield and Brix in the Y variables field and GERMPLASM IDENTIFICATION – breeder name in the X variables. Now I can decide to perform statistical comparison in order to compare all genotypes to the control – M82. For this I need to click on the Compare means link. Compare means link

The Compare means opens a window for which I can define the control genotype and the statistical test in order to perform multiple means comparison. 1) Click on the compare means link 2) In the popup window select the desired test 3) Select alpha value 4) Select the control genotype. 5) Click OK.

Now Im ready to invoke the analysis by clicking the OK button. Compare means parameters 1) Click OK to perform the analysis

This is the output of the Total yield trait. The output displays the M82 genotype in red bold which indicates that this is the level that all other genotypes are compared to. Then, sft- stop is in light-red, indicating that it is not significantly different than M82 under the selected a threshold all other genotypes are black, meaning they are significantly different than M82. This is equivalent to figure 1B of Krieger et al. This is equivalent to figure 1C of Krieger et al.

When you click the Show table button, it will display three tables (and then it is changed to Hide table). Table1: shows all observation units. this is the raw data of the analysis Table2: means and confidence interval of all genotypes Table3: ANOVA