GeneSifter.Net User s Guide

Transcription

1 GeneSifter.Net User s Guide 1

2 2

3 GeneSifter.Net Overview Login Upload Tools Pairwise Analysis Create Projects For more information about a feature see the corresponding page in the User s Guide noted in the blue circle ( 4 ). 5 7 Upload Tools Upload microarray data files Scatter Plot Interactive scatter plot provides visualization of the entire array data set and identification of individual genes. 57 Ontology Report Summarize ontology terms for a gene list and assess the biological significance of the genes within the list. 39 User Login Access your secure account from any computer (PC or Mac) with Internet access. Sign up for a free trial account at or contact us. info@genesifter.net Toll free: Create New Project Create user-defined projects with two or more groups. See next page for project analysis options. Pairwise Analysis Define two groups and apply normalization, statistical analysis and quality metrics to create lists of differentially expressed genes. 37 Export Results Export data and gene annotation to Excel. One-Click Gene Summary Provides a synopsis of the most current information available for the genes on your array. It includes information from UniGene, LocusLink, Gene Ontology terms and more. 3

4 GeneSifter.Net Overview Project Analysis Filtering Function Navigation Pattern Navigation Clustering Filtering Apply fold change cutoffs, Clustering statistical Identify patterns of gene expression analysis and with unsupervised clustering quality metrics functions. to create lists of differentially 49 expressed * genes. 57 Ontology Report Summarize ontology terms for a gene list and assess the biological significance of the genes within the list Pattern Navigation Define and identify patterns of gene expression with supervised clustering. 39 Cluster Samples Use hierarchical clustering to determine the relationship of samples based on a gene list. Project Analysis Project analysis functions allow analysis across all conditions in a project. Function Navigation Rapidly identify and group genes based on function using Gene Ontology terms. Ontology Report, Cluster Samples and the One-Click Gene Summary are available for all types of project analysis. * One-Click Gene Summary Includes information from UniGene, LocusLink, Gene Ontology terms and more. 4

5 GeneSifter.Net Introduction and Login Welcome to GeneSifter.Net, the Web-based microarray data management and analysis system, which relies on VizX Labs BIOME bioinformatics software engine. This document gives an overview of some of the features available in GeneSifter.Net. To get started from please follow these steps: Select the User Login button from the top right corner. 2. If you are a trial user, you will need to select either GS1 User s Login or GS2 User s Login as directed by VizX support. Otherwise, the user should select the option assigned by their account manager. 3. Enter your user name and password in their respective prompts and click on the Login button. 4. A successful login should show a screen with control panel on the left and the most recent announcements concerning GeneSifter.Net. 4 GeneSifter.Net Support and Sales support@genesifter.net Toll-free: WEB-GENE Direct:

6 GeneSifter.Net Online Help GeneSifter.Net provides page-specific online help Click on the help icon( ) to access page-specific help documents. The help icon can be found at the upper right of most pages. 2. Clicking on the help icon will open a new browser window which will list the help available for that particular page. Select the document you wish to view. 2 6

7 Uploading Data Upload Tools 1. In order to upload data, select Upload Tools from the control panel on the left. 2. GeneSifter.Net offers three options to upload data. The application you use will depend on the format and origins of the data being loaded. QuickLoad Wizard, Batch Upload, FlexLoad Wizard and Advanced Upload Methods are further described in the following pages

8 Uploading Data Using QuickLoad Wizard Use the QuickLoad Wizard to load your data into GeneSifter.Net. Supported platforms for this tool include: Affymetrix MAS 4, MAS 5 and GCOS Pathways 2 & 4 Spot-On Mergen arrays scanned with GenePix (all other GenePix files may be loaded using FlexLoad) Data Files may be archived (zipped) prior to upload. 1. Select Upload Tools from the control panel on the left. 2. Select Run QuickLoad Wizard from the Upload Tools page. A new window will instruct the user in the upload process

9 Uploading Data Using QuickLoad Wizard (continued) 3. Select the array platform or the image analysis software used and then click the Next button. If your software is not listed, please let us know and we will add it. 4. Select your array from the list of available arrays. If your array is not listed, please contact us and we will add it. After you have selected your array, click Next. 3 Note for Affymetrix Users: Auto Column Detection should work for data from MAS, MAS 5 and GCOS. 4 9

10 Uploading Data Using QuickLoad Wizard (continued) 5. Now you will enter information about the sample (referred to as Target ) that was hybridized to the array. If you have already entered information about the target, select it from the Select Target pull-down menu, otherwise enter your target using Create New Target. 6. If you create a new target, you will need to select an appropriate experimental condition from the pulldown menu. Otherwise enter your condition using Create New Condition. 7. You can create a new condition by selecting Create New Condition and entering the necessary information. Select Next when you have entered all needed information. 8. If your array has two channels, repeat steps 5, 6, and 7 for the second channel

11 Uploading Data Using QuickLoad Wizard (continued) 9. Select Browse and find your data file on your local computer. Select the file and then click the Next button to upload the file to GeneSifter.Net. Currently, the file must be a tabdelimited text file. Note to Affymetrix Users: The system will not accept.chp files. The data in the.chp file must be exported to Excel and saved as tab-delimited text in order to upload into GeneSifter.Net. 10. You will now see a summary of the information you have provided. You can enter a description for the experiment(s) being uploaded. Select Save Data to save the data in GeneSifter.Net. Note for Spot-On Users: Currently, GeneSifter.Net saves the intensity data for each channel as a separate experiment. The experiments will have the same default title, with channel 1 or channel 2 appended to the title. 11. When the data is successfully uploaded, you will see Success! as the Last Upload status. Common reasons for failure include: data not saved as text or selecting the wrong array at step After saving, you can either upload more data by selecting Next or exit the QuickLoad Wizard by selecting Done

12 Uploading Data Using QuickLoad Wizard Affymetrix Manual Column Detection 1 The Upload Wizard will automatically detect the proper data columns for text files generated from MAS 4, MAS 5 and GCOS; however, if auto detection fails, you can try using Manual Column Detection. 1. Affymetrix MAS 5 data format. This is an example of a file from the U34B GeneChip. Formats may vary due to differences in export from MAS 5. Generally the first column will contain the probeset ID. This identifies each gene on the array. The two columns that are needed by GeneSifter.Net for analysis are the columns containing the signal and quality value for that probe set. In this example, column B, which is labeled Signal, contains the derived signal value and column C, labeled Detection, contains the quality value. There may be additional columns present in a data file and the heading for the signal and quality values may be different than what is presented here. In general the signal column will either be labeled Signal or will have the word Signal at the end of the column name. This column may contain both positive and negative numbers. The quality column will generally be labeled Detection or will have the word Detection at the end of the name. This column will contain the letters A, M and P. 12

13 Uploading Data Using QuickLoad Wizard Affymetrix Manual Column Detection (continued) 2. Select Run QuickLoad Wizard as usual (see preceding description). Select Manual from pull-down menu Enter information about columns in the data file. In the sample file the probeset ID is in the first column (column A), the signal is in column B and the detection call value is in column C, so A would be entered for Probeset Column, B would be entered for Signal Column and C would be entered for Detection Call Column. In the sample file the data begins on the second line of the file so 2 is entered for Data starts on line. 4. Select Next and continue as usual for QuickLoad Wizard. The setting will be saved and used to correctly upload the data

14 Uploading Data Using Batch Upload Use Batch Upload to load multiple data sets stored in a spreadsheet as a tabdelimited text file. Note: See step 7 for a description of required file format. 1. Select Upload Tools from the control panel on the left. 2. Select Run Batch Upload

15 Uploading Data Using Batch Upload (continued) 3. Enter a name and description for the array you are uploading. Indicate in the check box whether Affymetrix Probeset IDs will be used as the gene identifier. 4. Browse your computer to find the file containing your data. Use the Select Array pull-down if you have previously loaded data from this array and you wish to add to that data set. 5. Select Upload Data. 6. Data uploaded. You can exit Batch Upload by selecting Done or select Upload More Data

16 Uploading Data Using Batch Upload (continued) 7. File format for Batch Upload: The first column (Column A in the figure) should contain an identifier for that gene. Accepted identifiers are: Accession Number Image Clone ID Affymetrix Probeset IDs 7 The second column (Column B) is for an internal identifier. This is left to the discretion of the user and can be left blank. The next 2 columns contain the intensity (Column C) and QC values (Column D) for that gene in the first experiment to be loaded. Additional experiments are added in the same way (one column for intensity, one for QC). The first row must contain this information. A: This cell can be empty. B: This cell can be empty. C: Target name for Experiment #1. D: Condition for Experiment #1. E: Target name for Experiment #2. F: Condition name for Experiment #2, etc. 16

17 Uploading Data Using Batch Upload (continued) 1. File format for Batch Upload using housekeeping genes. The format is the same with one exception: the third column must be labeled HKG (housekeeping genes). The genes that are designated as housekeeping should be marked with an x in this column. The intensities and quality values should follow as stated for Batch Upload without housekeeping genes. 8 In this example the genes in rows 7 and 9, (TRIM9 and GOLGB1) have been designated as housekeeping genes. 17

18 Uploading Data Using FlexLoad Wizard Use the FlexLoad Wizard to load data in GeneSifter.Net if the array you are using is not included in the QuickLoad Wizard. Familiarity with the layout of your files is advised before going any further. You will be asked: to provide information about the file structure, e.g. what column describes absolute intensity, background intensity, etc. how you want the data transformed, e.g preserve channel intensities or express as a ratio of the two channels. how you want the data normalized, e.g. LOWESS. 1. Select Upload Tools from the control panel on the left. 2. Select Run FlexLoad Wizard

19 Uploading Data Using FlexLoad Wizard (continued) 1. The Protocol Title is the name that you give to a protocol (i.e. the settings for a specific type of file to be uploaded). You can select a protocol you have already generated or create a new one. 2. If creating a new protocol, replace Untitled Protocol with a Protocol Title. 3. Enter an optional Description for the protocol. 4. Click on Create New to begin the creation of a new protocol

20 Uploading Data Using FlexLoad Wizard (continued) 5. If entering a new protocol, enter the name of the array in the Create New Array field. Alternatively, if you already have this array loaded into your account, select from the menu list. 6. Select the number of Channels. 7. Enter the number of files you will be uploading (the maximum allowed at any one time is 30). 8. If you know that the genes are all listed in the same order in every file (experiment) then select Same Order. If you select Unique IDs, FlexLoad will not assume all files are in the same order and use a Unique ID. If Unique IDs is selected every ID for that array must be unique and there cannot be any blank data rows. Ideally, you should use a GenBank Accession Number or an Image Clone ID as the Unique ID to populate the One-Click Gene Summary

21 Uploading Data Using FlexLoad Wizard (continued) If Dual Channels was selected in Step 6: 9. Select how you want your data represented. Intensities Preserves the values for each channel. The output will be two sets of values per file. Ratios Generates a single set of values per file. The output is a ratio of the intensities of the red and green channels. GeneSifter.Net only saves the ratio. 9 21

22 Uploading Data Using FlexLoad Wizard (continued) 10. Provide the column number or letter that contains the gene ID. 11. Identify the origin of the gene ID by selecting either Auto Detect, Accession Number, IMAGE Clone ID, or Other. If Accession Number or IMAGE Clone ID is selected and the data file contains other identifiers or blank rows, errors may occur. In general Auto Detect should be used. 12. Optionally, indicate the column number for gene annotation, if available. 13. Indicate the row number where the intensity data begins. Do not include the column headings. 14. Provide the column numbers that contain the respective data. For example, if column 8 contained the absolute value and column 10 the background for the cy3 channel, then 8 and 10 would be entered into Col 1 and Col 2 for cy-3 intensities respectively. Op refers to the operation you want to perform between the two values, e.g. subtraction. If you want to use values already provided in the file, then enter that column number in Col 1, OP as none and leave Col 2 blank

23 Uploading Data Using FlexLoad Wizard (continued) This window appears only if you chose Ratios in step Perform LOWESS Normalization on the data. 16. Select how you want to normalize the data. 17. Determine ratio calculation method. Per file basis allows you to take into account any dye-flip experiments you might have

24 Uploading Data Using FlexLoad Wizard (continued) 18. If your targets already exist, you can select them from the pulldown menu. If you need to create them, use Advanced Settings. 19. Select Browse to find the files for upload. This shows how it would look if you were uploading two files. If you were uploading more files, additional rows would be present. 20. For two color ratios where you have selected Per file basis for step 17 you need to indicate whether you want the ratios to be cy5/cy3 (5/3) or cy3/cy5 (3/5). 21. A summary of parameters that have been previously selected can be viewed. 22. Select Advanced Settings if you need to enter the target and condition info or if you need to increase the number of files being uploaded

25 Uploading Data Using FlexLoad Wizard (continued) 23. Upon selecting Advanced Settings, the user can Create New Targets/Conditions by selecting this option. 24. Enter target and condition information for each file to be uploaded (Create New Targets/Conditions must be selected; see 23). Otherwise, simply, use pre-existing targets. 25. You can save the output as a Batch Upload file by selecting Save as File or load it directly into GeneSifter.Net by selecting Upload Files

26 Uploading Data Advanced Upload Methods RMA Robust multi-array average (RMA) is a method for deriving expression measurements from the probe level data contained in an Affymetrix.CEL file. GeneSifter.Net users have the option to perform RMA during the upload of.cel files. The normalized data is saved in the user s account for further analysis Before using the Advanced Upload feature to upload.cel files and perform RMA, several user preferences must be set. Select Preferences from the Administration section of the Control Panel Click on the General tab. 3. Make sure Show Advanced Methods is On under the Uploading section. 4. Click on the Analysis tab Make sure Advanced Pairwise Settings is On. This will allow you to analyze log 2 -transformed data (performed as part of RMA) in GeneSifter. 26

27 Uploading Data Advanced Upload Methods RMA (continued) 6. Locate the Affymetrix.CEL files you want to upload on your local computer. 7. All the files to be uploaded and transformed using RMA need to be archived (.zip) into a single file for upload. 8. Select Upload Tools from the Control Panel. 9. Click on Run Advanced Upload Methods to begin using the RMA wizard

28 Uploading Data Advanced Upload Methods RMA (continued) 10. Select the Affymetrix array used in your experiments. If your array is not listed, please let us know and we will promptly add it. 11. Click the Next button to continue. 12. Click Browse to locate the.zip archive containing the.cel files on your local disk. Please note that all the files to be uploaded need to be contained within a single.zip file. 13. Choose how you want to create targets and conditions for the uploaded files. 14. Click Next to continue

29 Uploading Data Advanced Upload Methods RMA (continued) 15. Enter information about the data set, including a title and description. If you chose to pick groups for your experiment, you will now be prompted to create Conditions to assign to your groups group Click the radio buttons to place experiments into the appropriate group (in the example shown experiments 3 and 4 are assigned to group 2 FL). You may also change the names for the experiment and target at this point. Click Next to continue GeneSifter will now complete the uploading and RMA transformation of the data. If the upload is Successful, you can click Done to exit the wizard. Any error messages will also be displayed here. Please contact us if you receive an error message

30 Pairwise Analysis Set-Up Pairwise Analysis allows you to set up two groups of data and look for genes that are differentially expressed between the two groups Select Pairwise under the Analysis heading from the Control Panel. 2. A list of available arrays will show on the screen, along with a description if one was entered upon upload. 3. Select the Analyze icon ( ) to perform a pairwise analysis for a particular array. 4. Upon selecting the Analyze icon, a list of experiments performed using that array is displayed. The experiments are grouped by condition. Select the experiments for Group 1 by checking the corresponding boxes in the Group 1 column. 5. Likewise, select the experiments for Group For an overview of experiment details, select the Document icon ( ). 7. You can now select Analysis Settings parameters to be used in the Pairwise Analysis

31 Pairwise Analysis Set-Up (continued) 8. Select a normalization method from the Normalization pull-down menu. Options are global median, global mean, house keeping genes (predetermined prior to data upload) or None. 9. Select a method for determining reliability and consistency between replicates of each gene from the Statistics pull-down menu. Options include t-test (two tailed, unpaired Student T), Welch s t-test, Wilcoxon (non-paramteric) or none. 10. Select Threshold value from the pull-down menu. This number sets the threshold for display of up or down regulation (e.g. setting to 1.5 would select only genes that are differentially expressed by at least a factor of 1.5 in one group compared to the other). 11. You can select a Quality cutoff value for your data. This is optional and the value is calculated differently for different array types. 12. You can choose to display Upregulated and/or Down-regulated genes. 13. Perform your analysis by selecting the Analyze button

32 Pairwise Analysis Advanced Settings 13. If Advanced Pairwise Settings was turned on in the Preferences section, additional parameters are made available for use in analysis. 14. Select an Upper threshold if you would like to set an upper cutoff for the fold differences between Groups 1 and You may decide to include a Correction factor for multiple testing. Options include Bonferroni (step adjusted p-value), Holm (step down adjusted p-value), BH (Benjamini and Hochberg, false discovery rate), MaxT or None. If you select a correction method, you must also be performing a statistical test The Data Transformation field allows users to specify the following: The data should not be log transformed. The data should be log transformed for analysis. The incoming data has already been log transformed prior to upload. Note: Transformation is to log base Perform your analysis by selecting the Analyze button. 32

33 Pairwise Analysis Results 1. After submitting pairwise comparison analysis parameters, a list of differentially expressed genes is returned. The genes are ordered by ratio, listing those genes which are most differentially expressed at the top of the list. 2. The colored arrow indicates whether expression is higher (red up arrow) or lower (green or blue down arrow; color dependent on Preferences) in Group 2 compared to Group The total number of genes (results found) is indicated next to the Search button. The default gene list display is twenty genes per page; to show more, increase the number in the Show pull-down menu and select Search. Sort by p-value If a statistical test was included in the pairwise comparison, the list can be sorted by p-value from the Sort By pull-down menu and then selecting the Search button. Genes will be sorted by ascending p-value. If multiple corrections, have been applied, that p-value is used. p Cutoff The user can view results that are less than or equal to the selected p cutoff. A statistical test (t-test, Welch s t-test or Wilcoxon) must be performed in order to use the p Cutoff

34 Pairwise Analysis Results (continued) 4. Select the Export Results link to export the results of the pairwise comparison. Depending on your Preferences, data can be exported directly into Excel or saved as a tabdelimited text file. 5. To view the One-Click Gene Summary about any gene in the list, select the gene name. 6. To view more genes within the list, click on the [range] hyperlink

35 Scatter Plot After performing a pairwise comparison, the data can be viewed as a Scatter Plot with the log intensities for Group 1 plotted against the log intensities for Group 2. This plot displays the data for all of the genes and color codes the differentially expressed genes. The first time Scatter Plot is selected, there may be a few second delay due to the initial intensive calculation Select Scatter Plot from the Pairwise Comparison Results page. A new window will be displayed. 2. Clicking print plot in the blue control box will print the scatter plot or save it to a file. 3. The middle toggle line button allows the user to view/hide the identity line. 4. All the data for that comparison will be displayed on the plot (if multiple experiments were included for a group, the log of the mean intensity for the group is plotted). Red spots represent genes that are more highly expressed in Group 2 and green represents genes that are expressed at a lower level in Group 2. Gray spots represent genes that are not differentially expressed based on the criteria selected for the pairwise comparison. Note that the color scheme of spots can be selected within Preferences

36 Scatter Plot (continued) 5. To identify spots, drag the box over the region of interest and select zoom. The chosen region will be magnified in the box in the upper right frame (see 6). If the box titled hide unchanged is selected in the blue control box prior to zooming, the magnified box will only show genes that passed the threshold and statistical parameters. When zooming regions that are dense with spots, hiding the unchanged genes will generate the zoom box in significantly less time. 6. You can identify spots by mousing over them in the zoom box. The name will be displayed at the top. 7. Clicking on a spot will bring up experimental information in the lower right panel. 8. At the bottom right corner of the main scatter plot window, there is a yellow box to Open static scatter plot. Selecting this will generate a full scatter plot that can be copied, pasted or saved and inserted into reports, notebooks, etc

37 Export Results The results generated from GeneSifter.Net can be exported directly into Excel or saved as a tabdelimited file Click Export Results from Pairwise analysis. 2. Click Export Results from Projects. 3. If the Export Files setting in Preferences is set to Excel, upon prompt the application is opened with the data loaded. 4. If the Export Files settings in Preferences is set to Text, a pop-up box appears allowing the user to open a new window with the data printed to the screen OR to save the data as a.txt file

38 Create Project from Pairwise Analysis 1 Any gene list generated in Pairwise Analysis can be saved and further analyzed using additional features in Project Analysis. 1. Select Save as Project to from Pairwise Analysis. 2. Provide a Title and Description for the new (sub)project. 3. Provide optional Notes, for example, how the gene list was generated

39 One-Click Gene Summary The One-Click Gene Summary is a powerful feature in GeneSifter.Net that provides a synopsis of the most current information for each gene on an array. 1. Click on a gene of interest from the results list. 2. A new window will appear with details about that gene. Accession Number This ID is from the GenBank database. Clicking on it will display the GenBank record. Specific arrays may have additional IDs, such as Affymetrix will have a Probe Set ID (to NetAffx). 2 1 Cluster ID This ID is from the UniGene database. Clicking on it will display the full UniGene record. UG Title This is the title of the gene cluster in UniGene that contains this gene. Gene ID This is the gene Nomenclature ID as curated by the Nomenclature Committee. Clicking on it will display the GeneCard record for the gene. 39

40 One-Click Gene Summary (continued) Homologene Lists genes from the same or other organisms that show high sequence similarity. Chromosome Indicates the chromosome on which the gene is present (from UniGene). Cytoband The cytogenic marker to which the gene belongs (from UniGene). Seq Count The number of sequences in the UniGene cluster. LocusLink The ID from the LocusLink database. By clicking on it, the LocusLink entry is displayed. Gene Name The gene name (taken from LocusLink). 40

41 One-Click Gene Summary (continued) KEGG Pathways A link to the Kyoto Encyclopedia of Genes and Genomes (KEGG), a repository for genes from several different organisms. It provides information on pathways that gene products are involved in. OMIM ID number from the OMIM database. Clicking on this link will display the OMIM record for this gene. RefSeq mrna ID from the NCBI Reference Sequence database of comprehensive, non-redundant mrna transcripts. If you click on (FASTA) the sequence appears in FASTA format in a new window. 41

42 One-Click Gene Summary (continued) RefSeq Prot The ID is from the NCBI Reference Sequence database of comprehensive, nonredundant protein sequences. If you click on (FASTA) the sequence appears in FASTA format in a new window. Summary A short paragraph summarizing what is known about the function of the gene (from LocusLink). Gene Ontologies Gene Ontology terms for the gene (from LocusLink). Each term is hyperlinked to show the GO heirarchy. PubGene Link to the PubGene Network Browser providing a tool to explore both literature and sequence co-associations networks (from PubGene Webdatabase and Tools). 42

43 KEGG Pathways 1. Click on a KEGG Pathway to open a new window. 2. A new browser window appears with an image of the pathway in which the gene product participates. There may be more than one Pathway ID if a gene product is present in more than one pathway (metabolic and/or regulation)

44 BLAST Analysis A BLAST search can be performed on any gene sequence within the secure GeneSifter.Net environment. 1. Click on a gene from the results list of either a pairwise (1a) or project analysis (1b). 2. At the bottom of the One-Click Gene Summary window, click on Perform Sequence Analysis. 3. A new window will open with the selected sequence in a form ready for BLAST analysis. Select the type of BLAST search to perform (blastn or blastx) from the buttons at the bottom left corner of the window. 4. To determine suitable primers for a selected nucleotide sequence, there is a link to primer3, again with the sequence already loaded into the application. 1a 1b Note: Use blastn to BLAST the sequence directly against a nucleotide database. Use blastx to BLAST against a protein database. The latter works by translating your nucleotide sequence in all six reading frames (three for each direction) and blasting each result against the protein database

45 Project Analysis 1. Select Projects from the Analysis menu in the Control Panel. 2. Select the Analyze icon ( ) for the project you want to analyze. You will be prompted to Create New Project if no projects are available. 3. A new window will open with analysis options for that project. There are four general types of analysis available and the ability to export for a fifth: Gene Navigation Gene Function Pattern Navigation Unsupervised Clustering Export data

46 Project Analysis Gene Navigation Gene navigation allows you to view the expression profile from selected genes in your project. This is the default analysis method for projects. There are three ways that genes may be selected Search by Name Enter a gene name or part of a gene name in the text box and search for genes in the selected project. 2. Search by Accession or UniGene ID Use an Accession number from Genbank or UniGene cluster ID to filter probes that share the same ID. Multiple Accession numbers may be used to search by separating each with a space or two colons. 3. Search by Chromosome Searches for and displays genes found on the selected chromosome

47 Project Analysis Gene Function Clicking Gene Function in the Project Analysis tool bar, the Search by Gene Ontology and Search by KEGG Pathway options are available. 1. Search by Gene Ontology Select an ontology from the pulldown list of gene ontology terms. Only those ontologies present on your array are displayed in this list. Note that you can search within Biological Process (default), Cellular Component, or Molecular Function. 2. Search by KEGG Pathway Select a KEGG pathway from the pull-down list to search for genes that are part of that pathway. The list only contains pathways for genes present on your array

48 Project Analysis Search Parameters 1. Sort Determines how genes are sorted for display. Sort by Expression puts genes that are expressed at higher levels than the control at the top of the list and those expressed at lower levels at the bottom. 2. Mask (SEM/Mean) Allows the user to mask out intensity values where the SEM is large relative to the mean for a particular expression. Entering 0.25 would gray out conditions where the ratio of SEM to the mean is greater than Statistics Allows user to perform a multivariate statistical algorithm to determine whether there are significant differences in expression for that gene across the project. Significance is determined at p 0.05 and is indicated by a blue star to the right of the expression profile. 4. Show Determines how many genes will be displayed on the page at one time

49 Project Analysis Pattern Navigation Pattern Navigation allows the user to look for genes whose expression profile matches a user-defined expression profile. For example, this type of analysis could be used to identify genes that are highly expressed at the early stage of a time-course study, but might be minimally expressed at a later stage Select Pattern Navigation for pattern analysis. 49

50 Project Analysis Search by Gene Pattern (continued) 2. Set a pattern using the pull-down menus for each condition in a project. 2a. The condition title that has a green [C] beside it is the control. If you wish to use another condition as the control you must create a new project. 2b. The first variable column allows the user to set the sign (<, >, =) of the equation for each point of the pattern. Each point refers to the relationship of a condition to the control condition. 2c. The second variable column refers to the ratio desired for the comparison. It can be set to ratios ranging from 0.3 to 5. 2d. If you want all the conditions set to the same direction and threshold you can use the Set All menu rather than setting each condition individually. 2d 2b 2a 2c 50

51 Project Analysis Search by Gene Pattern (continued) 3. Parameters 3a. The comparison uses the Pearson correlation coefficient to determine whether gene expression patterns match the user-defined pattern. This coefficient can be calculated two ways, Centered and Uncentered. Generally, Uncentered will return more hits, but this can depend on the number of groups in the project. 3b. The Correlation coefficient is the p-value threshold for this method. The closer the value is to 1, the better the match. The genes listed after searching are sorted by p- value, so the best matches are always at the top of the list. Using values between 0.95 and 0.99 will ensure good matches. See tutorials for more information on the Pearson correlation coefficient. 3c. Statistics Selecting ANOVA will perform analysis of variance for each gene profile to determine whether there are significant differences in expression for that gene across the project. Significance is determined at p 0.05 and is indicated by a blue star to the right of the expression profile. Kruskal-Wallis is similar to ANOVA but is a non-parametric test. 3c 3e 4 3b 3d 3a 3d. Mask (SEM/Mean) This setting behaves as previously described (pg. 40). 3e. Show Controls how many genes will be displayed on a page at one time. 4. Click Search to initiate search. 51

52 Project Analysis Search by Gene Pattern (continued) 5. To change the pattern and search again select the Search Pattern button. 6. Profile displays the user-defined pattern indicating condition and expression ratio

53 Project Analysis Search by Threshold 1. Allows user to set the minimum threshold for inclusion of a gene in the gene list. If the fold difference between the control and a condition is greater than the minimum, this gene will be included. 2. Profile displays the user-defined cutoff indicating direction and magnitude of differential expression

54 Project Analysis Unsupervised Clustering Cluster analysis can be used to group genes together based on their expression profiles. GeneSifter.Net offers a variation of k-means clustering, called partitioning around medioids, or PAM. 1. Select Cluster as the analysis option for project analysis. 2. Select parameters to be used for PAM analysis and then select the Search button

55 Project Analysis Unsupervised Clustering 3. When the cluster analysis is completed a series of graphs will be displayed. These graphs represent the expression profile for the gene that is the center of that cluster. Individual genes within the cluster have similar expression profiles, which are centered around the profile shown. An average silhouette width is calculated for the entire dataset and this number can guide the user in selection of cluster number. 4. The number of genes in a given cluster is displayed below the graph for that cluster. Select the graph for a particular cluster to view information about the genes in that cluster. 5. A heatmap figure summarizing the expression profiles for genes in the selected cluster will be displayed

56 Cluster Samples Hierarchical Clustering of Samples 1. Select the Cluster Samples link. This link is available for any gene list generated by project analysis. Clustering will only be performed for projects which include more than two conditions. 2. A new browser window will open and a dendrogram will be displayed

57 Ontology Report The Ontology Report is available for any gene list created in GeneSifter.Net. In any gene list from either Pairwise or Project analysis, select the link for Ontology Report in the upper right corner. 1. Gene Ontology (GO) terms are divided into three general categories. The default display will be the Biological Process ontologies. To see the report for either Cellular Component or Molecular Function, select the appropriate link. 2. You can view the data as an Ontology Report or Z-score Report. 3. Ontology summary table. Summarizes GO terms for genes in the list. See section 5-9 for additional details. 4. Pie chart of ontology distribution. Summarizes the frequency of GO terms listed in summary table. See 8 for details

58 Ontology Report (continued) 5. Genes link displays the genes in the list that correspond to the current ontology. 6. GO link displays the Gene Ontology tree structure for a GO term. 7. Ontology Totals: List displays how many genes with the specific ontology term are in the gene list. For pairwise analysis this list is further divided into upregulated (red arrow) and downregulated (green arrow). Array displays how many genes with the specific ontology were measured in the comparison. Typically this is the number of genes with the specific ontology that are present on the array being used z-score. Indicates whether the specific GO term occurs more or less frequently than expected. Extreme positive numbers (greater than 2) indicate that the term occurs more frequently than expected, while an extreme negative number (less than -2) indicates that the term occurs less frequently than expected. See 11 for calculation details. 58

59 Ontology Report (continued) 10. Ontology term frequency graph is a display of the frequency of GO terms in the summary table. The frequency is calculated by dividing the number of genes with the specific ontology (number in the List column of the table) by the total number of genes in the table (the sum of the List column). 11. z-score calculation: R = total number of genes meeting selection criteria N = total number of genes measured r = number of genes meeting selection criteria with the specified GO term n = total number of genes measured with the specific GO term

60 Z-score Report The Z-score Report is generated from the same data as the Ontology Report. It displays those GO terms that have a z-score greater than 2, or less than -2. In a z-score report generated from a Pairwise analysis, a score is calculated for both up and down-regulated genes. Only one of these scores needs to pass to be included in the cut off. 1. Click on Z-score Report to list the genes by z-score 2. The default view sorts the terms according to the ontology that is most highly represented within the list. Click on List to sort or re-sort the list in this fashion. 3. Alternatively, to sort the list by z-score in: a) Pairwise - Click on the arrows. b) Projects - Click on z-score a 3b 60

61 Export GeneSifter.Net offers the ability to export data in a format compatible with two freely available tools clustering tools, EPClust and Cluster. EPClust is a webbased tool that allows the user to submit data over the Internet. Cluster is a desktop application that users can download. GeneSifter.Net provides links to the EPClust site as well as a link to download Cluster. To export data for clustering, the user must have already created and selected a project. From the initial project analysis screen: 1 1. Select Export from the upper right corner. 61

62 Export (continued) 2. Select the format corresponding to the clustering tool of interest. 3. Optional: Select the link to the EPClust site or the link to download Cluster. 4. Select ANOVA if you want to export only those genes that show significant differences in expression between conditions. 5. Select whether you want your data normalized. 6. Select Log transform, if desired Click Export. 8. Save the export file as a tab delimited text file, you can do this by opening the Save As Type and selecting.txt if it is an option on the menu, otherwise select All Files and type in.txt. 9. Select Save

63 Saving a Project Result Set A filtered gene list returned after performing Project Analysis can be saved and further analyzed using different Project features. 1b 1. Select Save Result Set to create a sub-project from Project Analysis. 2. Provide a Title and Description for the new (sub)project. 3. Provide optional Notes, for example, how the gene list was filtered

64 Create New Condition Using the Create New section of the control panel you can create additional conditions, targets, and new projects. 1. To add a new condition begin by selecting Condition from the control panel. 2. Enter the condition s title, optionally a description, or any notes and click Create. 3. This will then add the condition to the list of conditions that may be used for uploading data and analysis

65 Create New Target 1. To create a new target, select Target from the control panel. 2. Enter the title along with a description (optional), then either select the corresponding condition or Create New. Add any notes (optional), and click Create. 3. Any MIAME information that is associated with a target is displayed 4. This will add the target to the list of targets that may be used when uploading data or changing the target in a previously loaded experiment

66 Create New Project A project is a user-defined set of experiments grouped by experimental Condition or by user-defined categories. Setting up a project allows users to analyze expression across two or more groups. Create New Project Using Conditions: 1. Select Project from the Create New section of the Control Panel. The default for Create New Project is Use Conditions to create project. You will see a list of available arrays. 2. Enter a Project Title (required) and Description (optional). 3. Select an array, or arrays, for use in the Project. When you select a single array, you will see a list of experimental conditions that have been examined on that array. If you select more than one array you will see a list of conditions that are common to all arrays selected. Select Continue after an array or arrays have been selected Note: An array appears on this list only if it has experiments with two or more different conditions. 66

67 Create New Project Using Conditions (continued) 4. You can modify Group Name and enter a Description, if desired. 5. Select a normalization method for each array to be included in the Project. Optionally, select Data Transformation method. 6. Select a group of conditions to include in the project. Click on the condition you want to include in your project. Click the > button to move it to the Selected Conditions box on the right. Once conditions have been moved, select a condition and use the Up and Down buttons to reorder it if desired. Conditions can be removed by using the < button. Click Create Group after conditions have been selected and ordered Note: The order of the conditions in the list will determine how the conditions are displayed when the project is analyzed. The first condition in the list will be treated as the control value. Expression values for other members of the project will be expressed relative to this condition. In the example shown, Follicular Lymphoma (FL) will be the control. 67

68 Create New Project Using Conditions (continued) 7. Select individual experiments to include for each experimental condition. Click the check box to include an experiment. Select Create New Project when all desired experiments have been selected. The values used for analyzing a project will be the mean of all the experiments selected for that condition. 8. You can now add another group, analyze, or create a new project by selecting the appropriate link from the list of choices. 9. The project is summarized and the control is marked with [C]

69 Create New Project Using New Categories This feature allows users to create new categories for grouping experiments when creating a project. Users do not have to group experiments by Condition using this method After selecting Project from the Create New menu in the Control Panel, select the Create new categories for projects link at the upper right of the page. 2. Enter a title for the project (required) and a description (optional). 3. Select an array from the Arrays pulldown menu. Enter the number of new categories you wish to create (minimum of 2)

70 Create New Project Using New Categories (continued) 4. Select a normalization method, data transformation (optional) and enter titles for each of the categories you are creating. Select Continue. 5. All of the experiments associated with the array you have chosen will be displayed. Assign categories using the Category pull-down. You do not have to assign a category to each of the experiments, only those you want to include in the project. Select Continue

71 Create New Project Using New Categories (continued) 6. Select the experiments you want to include by checking the corresponding boxes, or select all experiments by clicking the Select All Runs link. Select Create Project to finish. 6 71

72 Preferences 1. Select Preferences from the Control Panel on the left. Preference options are subdivided into General, Analysis, User and Account Info and can be selected from the tabbed display. Analysis is the default preference display. General Preferences 2. General Use Folders- Toggles the use of folders in Inventories to help organize your data. Return Results- Choose length of the default return for gene lists. Language- Select text language (currently English and Japanese are available). Color Scheme- Choose colors to be used to indicate up and downregulation in displays. Export Files- Choose whether results are exported as a text file or as an Excel spreadsheet (.xls). 3. Uploading Allows the user to select the Default Array Type when using the QuickLoad Wizard To save desired changes in Preferences click on the Save button in the lower right corner. 72

73 Preferences Analysis Preferences 4. Advanced Pairwise Settings 4a. When Off is selected, only the basic parameters for Pairwise Analysis are available. 4b. When On is selected for Advanced Pairwise Settings, the pairwise analysis screen will include these additional parameters: Data Transformation- the ability to log transform or account for data already log transformed Upper- maximum threshold for expression Correction- The Bonferroni, Holm, BH and maxt statistical corrections for multiple testing 4 4b 4a 73

74 Preferences Analysis Preferences (continued) 5. Extended Project Data 5a. When Off is selected, only a basic data summary will be displayed for genes within Project Analysis 5b. When On is selected, a detailed data summary will be displayed for selected genes within the Project Analysis. An additional table will display all data points used in the calculation. In addition to the information in the basic summary, the detailed summary will include the following: 5 Grp Min - The smallest mean intensity for that condition Grp Max - The largest mean intensity for that condition Grp Ave - The mean of all intensities within the condition Grp Med - The median of all intensities within the condition 5a 5b 74

75 Preferences Analysis Preferences (continued) 6. Row Center Project Data displays data for a selected gene as the intensity for that gene over the mean intensity for that gene across all groups. 7. Quality Cutoff defines whether all groups must pass the selected quality cutoff or just one of the entire set must pass. 8. Project Gene Title allows the user to select whether gene titles or accession numbers are displayed in the project analysis gene lists

76 Preferences Analysis Preferences (continued) Heatmap Figure 9,9a Heatmap Figure graphically displays gene regulation in gene lists. The colors are modulated by the Color Scheme under General preferences. Number Each Row If the option is selected, each row of the Heatmap figure will be consecutively numbered. Open In New Window Upon clicking View As Image in the project results page, the Heatmap figure will be opened in a new browser window. Image Type Selects the file format that the Heatmap image can be saved as. Available formats are PNG and JPEG. Title Upon clicking View As Image in the project results page, the resulting Heatmap rows can either be labeled by the gene title or by the accession number. 10. Gene Titles Sets the display to either accession number or the UniGene title within gene lists. Note that Project Gene Title under Analysis preferences will override this selection. 9a

77 Preferences User Preferences 11. User Info Provides contact information for sending secure within GeneSifter.Net. 12. Change Password To change password, fill in the fields of old and new password To save desired changes in Preferences click on the Save button in the lower right corner

78 Preferences Account Info Preferences 14. Bar graph indicates total account space used

79 User s Forum 1. Select User s Forum from the gray control panel on the left. Note: To post a message, the user needs to register for this feature. Unregistered users will see a prompt to register. Fill out the registration form, typically using the GeneSifter.Net username and password. Once registered, it is not required to reenter the user name and password each time a user visits the site. For additional information please contact GeneSifter.Net Support and Sales support@genesifter.net Toll-free: WEB-GENE Direct: