User’s Guide
GeneSifter Overview • Login • Upload Tools • Pairwise Analysis • Create Projects 38 7 63 Scatter Plot For more information about a feature see the corresponding page in the User’s Guide noted in the blue circle ( 4 ) . Interactive scatter plot provides visualization of the entire array data set and identification of individual genes. Upload Tools Upload microarray data files. Ontology Report Summarize ontology terms for a gene list and assess the biological significance of the genes within the list.
GeneSifter Overview 59 63 • Project Analysis • Filtering • Function Navigation • Pattern Navigation • Clustering 58 Ontology Report Summarize ontology terms for a gene list and assess the biological significance of the genes within the list. Filtering Apply fold change cutoffs, statistical analysis and quality metrics to create lists of differentially expressed genes. Clustering 62 Identify patterns of gene expression with unsupervised clustering functions.
GeneSifter Introduction and Login 1 Welcome to GeneSifter, the webbased 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. To get started from www.genesifter.net please follow these steps: 1. Select the Login button from the top right corner. 2. Enter your user name and password in their respective prompts and click on the Login button. 4.
Genesifter Online Help 1 Genesifter provides page-specific online help. 1. Click on the help icon ( ) to access page-specific help documents. The help icon can be found in the upper right corner 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.
Uploading Data Upload Tools 1. In order to upload data, select Upload Tools from the control panel on the left. 2. GeneSifter offers four tools to load 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.
Uploading Data Using QuickLoad Wizard Use the QuickLoad Wizard to load your data into GeneSifter. Supported platforms for this tool include: • Affymetrix (native CHP files, or CHP files saved as tab-delimited text) • Codelink • 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.
Uploading Data Using QuickLoad Wizard (continued) 3. 4. Select the array manufacturer or the image analysis software used and then click the Next button. 3 Select your array from the list of available arrays. If your array is not listed, please contact scientific support for information on adding the array. After you have selected your array, click Next. Note for Affymetrix Users: Auto Column Detection should work for data from MAS 5 and GCOS.
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 “Condition” from the pull-down menu. Otherwise, enter your condition using Create New Condition. 7.
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. 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 your GeneSifter account. 11. When the data is successfully uploaded, you will see Success! as the Last Upload status.
Uploading Data Using QuickLoad Wizard Affymetrix Manual Column Detection The Upload Wizard will automatically detect the proper data columns for text files generated from MAS 4, MAS 5 and GCOS. If auto detection fails, you can use 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.
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. 3. Enter information about columns in the data file.
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.
Uploading Data Using Batch Upload (continued) 3. Enter a name and description for the array you are uploading. The pull-down menu has options for the type of data being loaded including: Use Affymetrix Probeset IDs – use if the first column of your file contains Affymetrix Probeset IDs instead of GenBank accession numbers. 3 4 5 This File is a GEO Data Set – use if you downloaded data from GEO as a GEO dataset. Use CodeLink Quality Values – use if your file has the CodeLink flags G, M, L. 4.
Uploading Data Using Batch Upload (continued) 7. The file to be loaded must be a tabdelimited text file (txt). GeneSifter does not accept Excel spreadsheets (xls). 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 The second column (Column B) is for an internal identifier. This is left to the discretion of the user and can be left blank.
Uploading Data Using Batch Upload (continued) 8. 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. In this example the genes in rows 7 and 9 (TRIM9 and GOLGB1) have been designated as housekeeping genes.
Uploading Data Using FlexLoad Wizard Use the FlexLoad Wizard to load data in GeneSifter 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.
Uploading Data Using FlexLoad Wizard (continued) 1. The Protocol Title is the name given 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.
Uploading Data Using FlexLoad Wizard (continued) 5. If you previously loaded the array into your account, select it from the menu list. Alternatively, if you are creating a new protocol, enter the name of the array in the Create New Array field. 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.
Uploading Data Using FlexLoad Wizard (continued) If your data has two channels, from Step 6: 9. Select how you want your data represented: Intensities Data for each channel will be stored separately in GeneSifter. Ratios Generates a ratio of the intensities of the red and green channels. GeneSifter only saves the ratio to your account.
Uploading Data Using FlexLoad Wizard (continued) 10. Provide the column number or letter that contains the gene ID. 11. Identify the type of 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.
Uploading Data Using FlexLoad Wizard (continued) This window appears only if you chose Ratios in Step 9. 15. Perform LOWESS Normalization on the data. 16. Select how you want to normalize the data. 17. Method for calculating the ratio of intensities. Per file basis allows you to take into account any dyeswap experiments you may have.
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 new targets, use Advanced Settings. 19. Select Browse to upload the file(s). If you were uploading more files, additional rows would be present. In this screen, two files are being uploaded. 20. For two-color arrays, if Per file basis was selected in step 17, indicate whether you want the ratios to be cy5/cy3 (5/3) or cy3/cy5 (3/5). 21.
Uploading Data Using FlexLoad Wizard (continued) 23. Upon selecting Advanced Settings, you can change the number of files to be loaded. 24. You also have the ability to “Create New Targets/Conditions”. Enter target and condition information for each file to be loaded in the top portion of the screen. Otherwise, select “Use Pre-existing Targets”. 25. You can save the output as a text file formatted for Batch Upload by selecting Save as File or load it directly into GeneSifter by selecting Upload Files.
Uploading Data Advanced Upload Methods Robust multi-array average (RMA) is a method for deriving expression measurements from the probe level data contained in an Affymetrix CEL file. 1 2 GeneSifter users have the option to perform RMA or GC-RMA during the upload of CEL files. The normalized data is saved in the user’s account for further analysis. 1. Locate the Affymetrix .CEL files you want to load on your local computer. 2.
Uploading Data Advanced Upload Methods (continued) 5. 5 Select the normalization method. Select the type of Affymetrix array used in your experiments. If your array is not listed, please contact scientific support (support@genesifter.net) for help loading your array format. 6. Click the Next button to continue. 7. Click Browse to locate the .zip archive containing the CEL files on your local disk. Please note that all the files to be loaded need to be contained within a single .zip file. 8.
Uploading Data Advanced Upload Methods (continued) 10. Enter information about the data set, including a title and description. If you chose to create groups for your experiment, you will now be prompted to create titles for each group. 11. 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/sample at this point. Click Next to continue. 12.
MIAME MIAME is the Minimum Information About a Microarray Experiment that should be associated with public microarray data. Genesifter can store MIAME, by providing information through a user-friendly interface. The Genesifter V1.5 MIAME interface is designed for extraction and labeling protocols. Subsequent versions will have an interface where more MIAME can be provided. 3 4 5 1. Access the MIAME interface by clicking on MIAME under Inventories. 2.
MIAME (continued) 8. Many protocols may have additional information. This can be included in the Full Protocol Description text box (e.g. providing a complete PCR protocol). 9. Select Create to save a new MIAME protocol. 8 10. The newly created MIAME object should appear under MIAME>Inventories. 9 11. To edit or delete a MIAME protocol click on the protocol and select the appropriate button.
MIAME (continued) 12. To associate MIAME information with a target click on Targets under Inventories, choose and click a Target. 13. Click on Edit and select the appropriate MIAME object. Note: All Experiments that have this target, will automatically be associated with the newly created MIAME object.
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. 1. 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 it was entered upon upload. 3. Select the Analyze icon ( ) to perform a pairwise analysis for a particular array. 4.
Pairwise Analysis Set-Up (continued) 8. Select a normalization method from the Normalization pull-down menu. Options are All Mean, All Median 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.
Pairwise Analysis Set-Up (continued) 12. You can choose to display Upregulated and/or Down-regulated genes. 13. 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 2. 14. Perform your analysis by selecting the Analyze button.
Pairwise Analysis Advanced Settings 15. 16. 17. 18. If Advanced Pairwise Settings was turned on in the Preferences section, additional parameters are made available for use in analysis. 15 16 Select an Upper threshold if you would like to set an upper cutoff for the fold differences between Groups 1 and 2. You may decide to include a Correction factor for multiple testing.
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 1. 3.
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.
One-Click Gene Summary™ The One-Click Gene Summary is a powerful feature in GeneSifter that provides a synopsis of the most current information for each gene on an array. Each gene within a gene list has additional annotation, curated from various data sources. 1 1. Click on a gene of interest from the results list. 2. A gene summary appears in a new window. Within the upper panel, the mean expression values for each condition (group) are displayed, as well as the raw data for the individual arrays.
One-Click Gene Summary™ (continued) 3. In the lower pane of the One-Click Gene Summary: Accession No This ID is from the GenBank database. Clicking on it will display the GenBank record. Specific arrays may have additional IDs, such as Probe Set ID for Affymetrix (links to NetAffx™). Cluster ID This ID is from the UniGene database. Clicking on it will display the full UniGene record. UG Title The title of the gene cluster in UniGene that contains this gene.
One-Click Gene Summary™ (continued) 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). OMIM™ ID number from the OMIM database. Clicking on this link will display the OMIM record for this gene.
One-Click Gene Summary™ (continued) 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 hierarchy. KEGG Pathways A link to the Kyoto Encyclopedia of Genes and Genomes (KEGG). It provides pathway information for gene products.
BLAST® Analysis A BLAST search can be performed on any gene sequence within the secure GeneSifter environment. 1. Click on a gene from the results list of either pairwise or project analysis. 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 pasted 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.
Ontology Report The Ontology Report is available for any gene list created in GeneSifter. In a 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.
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.
Ontology Report (continued) 9. The pie chart of ontology term distribution is a display of the frequency of GO terms in the summary table. 10.
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 2 1. Click on Z-score Report to list the genes by z-score 2.
Pathway Report The Pathway Report is available from any gene list created in GeneSifter by selecting the link for Reports | KEGG. Note: The pathway report is currently available only for human microarrays. The pathway source is from the Kyoto Encyclopaedia of Genes and Genomes (KEGG). 1. All pathways from the gene list are shown. 2. The Genes link displays all genes from the list that are associated with the corresponding pathway. 3.
Pathway Report (continued) 5. 6. z-score. Indicates whether a pathway 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.
Scatter Plot 1 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. 1. Select Scatter Plot from the Pairwise Comparison Results page. A new window will be displayed. 2.
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.
Export Results The results generated from GeneSifter can be viewed in Excel or as a tabdelimited text file. You can export results from either pairwise or project analysis. 1 1. Within Pairwise Analysis, select Results: Export. 2. Within Project Analysis, select Results: Export.
Export Results (continued) 3. If the Export Files setting in Preferences is set to Excel, the exported data will be displayed using Excel. 4. If the Export Files settings in Preferences is set to Text, the data will be displayed as tab-delimited text.
Create Project from Pairwise Analysis 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.
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.
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.
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.
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. 1. 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 pull-down menu.
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. 4 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.
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.
Boxplots Simple boxplots are commonly used to summarize five aspects of a data set: the minimum, 1st quartile, median, 3rd quartile and maximum. Boxplots are useful when comparing two or more sets of data. Differences in the median and the spread of the datasets are clearly visible with a boxplot. In addition, the presence of outliers can often be inferred from boxplots. 1. Boxplots are available for “Projects” and “Sub-Projects” and can be accessed in the Project Details view. 2.
Boxplots (continued) Interpreting boxplots 6. The labels on the figure indicate the five attributes of the dataset that are represented in the boxplot. The 1st and 3rd quartiles indicate the inter-quartile range for the dataset. 50% of the data values lie within this range. The two datasets represented in the figure show very similar ranges (inter-quartile range) and centers (medians). 6 Maximum data value 3rd quartile for dataset Note: Genesifter uses “simple” boxplots, i.e.
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. 1. 1 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.
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.
Project Analysis Pattern Navigation Pattern Navigation allows the user to search for genes that match 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. 1. Select Pattern Navigation.
Project Analysis Search by Threshold Search by threshold allows the user to search the current project for genes that are greater than a specified fold change. 1. Select the desired fold change cutoff by using the pull-down menu next to Threshold. As long as one condition passes the desired cutoff, the gene will be included in the results list. Statistical options are also available.
Project Analysis Search by Gene Pattern 2. Set a pattern using the pull-down menus for each condition in a project. 2a. 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. 2b. 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.
Project Analysis Search by Gene Pattern (continued) 5. To edit the pattern and search again select the Search Pattern button. 6. Profile displays the user-defined pattern indicating condition and expression ratio.
Project Analysis Unsupervised Clustering Cluster analysis can be used to group genes together based on their expression profiles. Genesifter offers PAM (partitioning around medoids) and CLARA (clustering large applications). Both are k-medoids methods, which are variations on the popular k-means algorithm. 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.
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. 3 An average silhouette width is calculated for the entire dataset and this number can guide the user in selection of cluster number. 4 4.
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. 1 2. A new browser window will open and a dendrogram will be displayed.
Cluster Genes Hierarchical Clustering of Genes Hierarchical cluster analysis can be used to group genes together based on their expression profiles. 1. Select the Cluster: Genes link. This option is available for any gene list generated in ‘Project Analysis’. 2. A new browser window will open containing the heat map and dendrogram of clustered genes. A heat map view of the whole gene list is displayed at the top of the screen.
Saving a Project Result Set A filtered gene list returned after performing Project Analysis can be saved and further analyzed using different Project features. 1. Select Results: Save to create a sub-project from Project Analysis. 2. In the new window, provide a Title and Description for the new (sub)project. 3. Provide optional Notes, for example, how the gene list was filtered.
Export GeneSifter offers the ability to export data in a format compatible with several other packages including: EPClust, Cluster, and GeneSpring. EPClust is a webbased clustering tool that allows the user to submit data over the Internet. Cluster is a desktop application that is freely available. Exporting to these packages is only available within Project Analysis. From the initial project analysis screen: 1. Select Export from the upper right corner.
Export (continued) 2. Select the File Format for the tool of interest. Optional: Select the link to the EPClust site, or to download Cluster. 2 3 Select ANOVA if you want to export only those genes that pass an ANOVA (p<0.05). 3. If EPClust was chosen, select whether to normalize your data. Likewise, select Log transform if desired. 4. Click Export. 5. You may save the exported data as a tab-delimited text file, or other file type. 6. Select Save. 4 5 .
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.
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 also 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.
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. General Preferences 2. General Use Folders- Toggles the use of folders in Inventories to help organize your data. Browser Warning- Warns user of unsupported browser. MIAME- Allows creation and assignment of MIAME protocols. Return Results- Choose length of the default return for gene lists.
Preferences (continued) 3. • • 4. 5. Export Include Ontology/ Chromosome SummariesIncludes this information in the exported file. Export Files- Choose whether exported results are opened with a text editor or Excel. Uploading • Show Advanced MethodsEnables the Advanced Upload Methods which allows loading of CEL files, and normalization with RMA or GC-RMA. • Default Array Type- Allows the user to set the Default Array Type when using the QuickLoad Wizard.
Preferences Analysis Preferences 4. Advanced Pairwise Settings 4a. When Off is selected, only the basic parameters for Pairwise Analysis are available. 4b.
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.
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.
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.
Preferences User Preferences 11. User Info Provides contact information for sending secure email within Genesifter. 12. Change Password To change password, fill in the fields of old and new password. 13. To save desired changes in Preferences click on the Save button in the lower right corner.
Preferences Account Info Preferences 14. Bar graph indicates total account space used.
Appendix GEO Database Genesifter can upload GEO formatted files from the Affymetrix platform. The Gene Expression Omnibus (GEO) is a public database of microarray data, hosted by the NCBI (http://www.ncbi.nlm.nih.gov/geo/) 1. Query GEO accession number or the DataSet ID. 2.,3 Alternatively, Browse the database to find datasets of interest by selecting “DataSets”. 4.
GEO Database (continued) 5. Browse through the datasets generated using the Affymetrix platform [Note:GPL91 is the Affymetrix Platform Accesion no in GEO]. Select“go to full dataset record”. 6. To download all the experiments of the dataset, mouse over “download” and select “complete DataSet” and save the file.
GEO Database (continued) 7. Select Batch Upload option within Genesifter from the Control Panel. Enter an “Array title”, “Description” and select “This file is a GEO Data Set” from “Options”. 8. After uploading the GEO file, Genesifter automatically fills the Target, Conditions and Descriptions for each experiment of the dataset. These field values can be changed from Inventories. Select “Save Data” button to complete the GEO dataset import.
