CCR Logo
Main
StRAP

Quick Tour of StRAP

  1. Overview
  2. Genes
  3. Cell lines
  4. Arrays
  5. Examples
  6. Frequently Asked Questions
  1. Overview
    The StRAP tutorial provides a brief overview of the main features of the StRAP tool. It is organized around the three main modules of StRAP: Genes, Cell lines, and Arrays. These modules are entry points into meta and experimental data associated with the corresponding entities.
  2. Genes
    The genes module enables gene-centric queries of the StRAP microarray studies. Queries can be based on gene or protein identifiers, synonyms, gene descriptions, or chromosome location. The results include associated arrays and studies, and a compilation of gene-annotation information, spatial localization within the genome visualized in the UCSC Genome browser, and network neighborhood maps generated from protein-protein interaction networks. Queries can also be constructed using gene lists defined by the user or generated, for example, from Gene Ontology (GO) terms.
    To access the Genes module, select "GENES" on the left side frame of the portal page. You should see the following setup:



    There are three main parts to the window: the panel "Search by List of Terms," the panel "Search by Keyword", and the panel "Show/Hide Conversion Tool." The first is used to enter a single term, or a list of terms in order to search genes by identifier, chromosome location, or GO description. The second is used to search genes by a single keyword. The conversion tool is used to convert gene identifiers of one type to another. These are detailed below.
    The search display results depend on the number of genes relevant to the search. When a single gene is relevant, detailed gene information is displayed, in addition to experimental data. The display possibilities are discussed below.
    • Searching genes by identifier
      The identifiers that can be used to search genes are "Gene Symbol," which is the official HUGO symbol of the gene, or any known synonyms, Gene ID, description, or GenBank accession. The type of identifier used is indicated by making the appropriate selection in the radio box of the "Search by List of Terms" panel.
      Examples of each include:
      Gene ID: 25, 135
      Symbol: ABL1, AATK
      Description: tyrosine, receptor
      Accession: M25949.1, AC115099.6
      Lists of terms can be entered either manually in the text area, or uploaded from a text file. The terms in a list can be separated by any space character, commas, or semicolons.
      Pressing the search button of the panel will bring up two possible displays: when a single gene is associated with the search term, detailed gene information will be displayed, as detailed below; in addition, any experimental studies that feature the gene will also be displayed. When more than one gene is associated with the gene term, the display will present all studies that feature any of the relevant genes.
    • Searching genes by chromosome location
      A single chromosome, for example "X", or a list of chromosomes, for example "X, 1, 23" can be used to search for genes. The terms can be separated by any space character, commas, or semicolons. The radio box selection should be set accordingly.
      Pressing the search button of the panel will bring up a display of the studies featuring genes from the relevant chromosomes.
    • Searching genes by GO description
      GO terms (separated by any space, comma, or semicolon character) can be used to focus on lists of genes. Enter, for example, "apoptosis" in the text search area, and make the appropriate selection in the radio box of search type, to view all GO terms that match the word "apoptosis."
      Pressing the search button of the panel will bring up a table with all the GO terms that match the search term. For "apoptosis" the (cropped) table should look as follows:



      Each row of the table represents a GO term that matched the search term, and also shows the number of genes that are associated with that term. For example, "neutrophil apoptosis" involves 3 genes, whereas the more more general "apoptosis" has 647 genes. To display the genes that are associated with each term, click on the expandable "Gene List" icon. We have expanded the gene list for "B cell apoptosis," revealing "BAX, BCL2L11, TRAF3IP2," as the involved genes.
      To display experimental data associated with one or more represented GO terms make a selection on the selection boxes on the left-most column of the table, and press the "Search Selected" button. To search for experimental data for all terms in the table press the "Search All" button. We discuss the display of the experimental data below.
    • Searching genes by keyword
      To search for genes without specifying the type of identifier used, we can use the "Search by keyword" panel of the Genes module. A single term (but which can represent multiple genes) must be entered in the text area. Examples of suitable search terms include "kinase," "apoptosis," "TP53," etc.
      To see the difference between searching for "TP53" by keyword, versus searching for "TP53" as gene symbol, note that in the latter case there will be a single match -- that corresponding to the gene for "tumor protein 53" with ID "7157," and symbol "TP53." When searching for keyword "TP53" the display will show a table of all genes whose information (whether symbol, synonym, or description) contains the match word.



      Clicking on individual gene identifiers from the table will display detailed gene information as described below. Whereas selecting multiple, or all genes from the table will display experimental data associated with those genes. Selection is done by ticking the selection boxes on the left-most column on the table and pressing the "Search Selected" button.
    • Converting gene identifiers of one type into another
      Sometimes we might want to convert gene identifiers of one type into another. For example, we have the gene IDs of some genes, and would like to know their official symbol. To do this, expand the "Show/Hide Conversion Tool" panel of the Genes module to see:



      To convert, select the type of the input identifier (the default is "Gene ID"), and check the boxes of desired output (the default is "Symbol"), and press the "Search" button. For example, the display for converting "25, 7157" gene IDs into symbols, and description is as follows:



    • Displaying detailed gene information
      Detailed gene information is accessed by searching the Genes module for specific genes (as described above), or made available as hyperlinks of gene symbols relevant in other visualizations (such as Array-related metamaps). In particular, when searching for genes produces a single relevant gene, the following screen is displayed to the user (here we searched for "TP53"):



      The tabbed content provides access to the following information:
      • Arrays&Studies: This is the default tab and presents a list of studies featuring the gene.
        To access visualizations of the expression data associated with the gene, select the studies of interest among the available ones by ticking the selection boxes to the left of the studies list and press the "Compare" button. Lets select, for example, studies 7,8, and 10.
        The next screen displays the visualization options:



        The expression of the gene in the selected studies can be compared using boxplots or barplots.
        Boxplot: Clicking on the "Boxplot" image of the visualization options generates the following graph:



        The boxplot depicts the normalized expression values of the gene TP53, averaged over each experimental condition (control and treatment levels) for each of the selected studies. The cell line distinctions are removed here, aiming to give a quick overview of any difference in the expression values due to treatment. For example, ionizing radiation seems to increase the expression level of TP53 in study 8, whereas it seems to have a mixed effect on the expression levels of TP53 in study 15, with the subcutaneous model showing decrease in levels, while the other two models showing increase in the levels. The controls are depicted by green lines, and the treatments by red lines.
        The p-values are computed at the study-level (the same way as for barplots). For more information read How are the p-values computed at the study level? in the Frequently Asked Questions section.
        To access the numeric data associated with the graph press the "Numeric Data" button below the graph.
        Barplot: Clicking on the "Barplot" image generates the following graph:



        The barplot depicts the normalized, and mean-centered expression values of the gene TP53 for each of the selected studies. Each study has its own panel on the plot, which is separated by other studies (when more than one study has been selected) by a dotted vertical line. As the legend suggests, the bars are colored according to the cell lines used in the studies. If the same cell line is used in more than one selected study, the corresponding bars will be colored the same, making it possible to compare the effect of treatment on the same cell line in different studies. For each cell line, controls are distinguished by treatments by striped bars of the same color. The cell line names in the legend are preceded by tissue keywords; for example, the cell lines "U87" and "U251" are both derived from tissue "CNS." The controls and treatments are labeled accordingly, with the treatments typically indicated the dose of the treatment and how long after treatment the sample is analyzed; for example "3Gy_5H" indicates irradiation with dose 3Gray, and sampling at 5 hours after irradiation. Sometimes the controls, and treatment labels are also proceeded by keywords indicating certain conditions or treatments of the cell lines. For example, "IC," "IV," and "SC" above indicate the manner of implantation of the tumors for experimentation, and mean "intra cranial," "in vitro," and "subcutaneous," respectively. Typically, this information is detailed in the source of the study, which can be accessed by following the hyperlinks on the study number; for example "Study:8". The order of the bars in the plots is sorted according to study, tissue, control versus treated, and then cell line.
        A p-value indicating the statistical significance of the difference between the expression values is given for each study. These p-values are computed at the study level, by performing a two-way ANOVA on treatment levels, and cell line levels. For more information read How are the p-values computed at the study level? in the Frequently Asked Questions section.
        To access the numeric data associated with the graph press the "Numeric Data" button below the graph.
      • UCSC Browser: The UCSC Browser tab displays genomic location information associated with the gene, available from UCSC Browser. Typical information includes position of the exons and introns of the gene in the genome, known SNPs, conservation among species, and even association with disease.
      • Gene Info: This tab gives a quick overview of the gene info typically found on the NCBI Gene database, such as official symbol, known synonyms, and gene nomenclature from other databases. It also shows the gene association in human disease, as documented in the OMIM database.
      • GO Terms: This tab shows the characterization of the gene products according to the Gene Ontology.
      • GeneRIFs: This tab shows annotation information of the function and regulators of the gene as curated from publications.
      • PubMed Info: This tab shows a list of PubMed publications associated with the gene.
      • Pathways: This tab shows detailed functional and interaction information from the Pathway Commons database. Of special interest can be the network neighborhood maps that can be accessed from here, showing how the gene products interact with other molecules. In the example of "TP53," to access the network neighborhood map, first select among the different proteins, the one from homo sapiens. Then scroll down, and expand the "Network neighborhood map" pane of the left side panel. You should see a screen similar to this:



        Options to download, and open the interaction networks in Cytoscape are also available.
    • Displaying experimental data associated with selected genes
      Experimental data associated with a number of genes is accessed by searching the Genes module for gene lists, or terms that generate multiple related genes (such as searching by chromosome location, GO terms, or keywords). For example, searching for GO term "cell cycle," and selecting "cell cycle checkpoint" as the relevant GO term, generates the following list of relevant studies:



      The screen shows that at least some of the 131 genes associated with the cell cycle checkpoint GO term are featured in all 21 studies. Now, suppose we are interested on the effects of radiation on the genes related to cell cycle checkpoint as studied by studies 6, 7, and 8 (these studies feature, 111, 120, and 120 of the genes, respectively). We select the associated boxes and press the "Compare" button. The following screen shows the visualization options available:



      Metamap: The metamap displays differential expression significance of genes as represented by p-values. The significance of p-values is represented using a color scheme where the darker the red the smaller (more significant) the p-value is. Black represents that a gene is not featured in the given study. Genes that satisfy the p-value criterium for at least one study are included in the metamap.
      Clicking the "Metamap" option of the visualization displays a paginated metamap, similar to the following:



      Note that the presented screen corresponds to the last page of the metamap, which is navigated using the navigation buttons on the bottom of the table. The first page, which is what you will be see at first, will show the genes with the lowest p-value, which in the case of the example be 0.
      The gene symbols are hyperlinked; following these hyperlinks will lead to the detailed gene information display discussed above. Similarly, the "Boxplot" and "Barplot" icons can be pressed to generate boxplots and barplots for the corresponding gene and selected studies.
      Another point worth noting is that the metamap displays genes that satisfy the default p-value threshold (<= 0.05); so you will see that in the given example, 98 of the genes do satisfy the condition. If a different p-value threshold is desired, this can be set by entering a new p-value in the "Change p-value max" box and pressing "Enter". The p-values displayed are computed at the study level as described in How are the p-values computed at the study level?
      Heatmap: Gene expression profiles are clustered according to their similarity, in selected studies. The gene list can be filtered according to the p-value of individual genes. In the cases where no results are shown, the heatmap could not be constructed due to there not being a sufficient number of genes (two or more) for the clustering procedure to work. Relax the p-value threshold and or increase the number of genes to be considered. Clicking the "Heatmap" option of the visualizations generates the following screen:



      The clustering procedure is computationally intensive and therefore can take a long time to complete. Start by using a very small p-value and increase it as needed for best results.
      Press the "Jree Heatmap" button to display the clustered expressions using the Java Tree View app. For our example, the heatmap will be similar to the screen below. Note that you may need to increase the intensity of the colors (by going to Java TreeView -> Settings -> Pixel Settings and sliding the "Contrast" bar to the left); moreover, you also need to make a selection box in the heatmap, using your mouse, in order to generate the close up view of the middle pane.



      The selected genes -- the portion of the heatmap visible in the middle pane -- represent some of the cell cycle checkpoint genes most affected by radiation. This is visible by the intensity of the colors, and the changes of expression levels between controls and treated samples.
      Grayed out strips on heatmap indicate that some of the genes are not featured in the corresponding studies.
      If the "Jtree Heatmap" button is not displayed, or Jtree is having problems starting, check that you have an uptodate version of Java as described in Why is Jtree not starting?.
  3. Cell lines
    The cell lines module provides meta data on available cell lines and associated studies. Queries in this module are tailored to allow selection of complete studies, by tissue of origin, or individual cell line. Comparisons can be made for samples within a study or across studies.
    To access the Cell lines module, select "CELL LINES" on the left side frame of the portal page. You should see the following screen:



    There are three main parts to this screen: a panel showing the list of available studies, a panel showing a list of cell lines with data available in the portal, and the top panel of selection options.
    Two main selections can be made at this point: (1) Focusing attention on a single cell line and displaying the meta data and experimental data associated with it, and (2) Focusing attention on a tissue of origin and displaying experimental data associated with it. We explore these options below.
    • Displaying cell line meta data
      Each cell line has meta data associated with it: these show the source of the cell line (for example, the demographic characteristics of the patient from whose sample the cell line was immortalized), histological information, the contributor, etc.
      To display the meta data associated with a cell line follow the hyperlinked name of the cell line from the "CELL LINES" module. For example, clicking on "U87" should display the following screen:



    • Displaying experimental data associated with selected cell line
      This option is arrived at from the Displaying cell line meta data scenario above. To compare one or more studies associated with the given cell line, select among the studies listed, and press the "Metamap" button. To display data associated with a single study, follow the hyperlinked study name. Metamap: The metamap displays differential expression significance of genes as represented by p-values. The significance of p-values is represented using a color scheme where the darker the red the smaller (more significant) the p-value is. Black represents that a gene is not featured in the given study.
      As an example, select studies 11, and 13 associated with cell line "U87" and press the "Metamap" button. You should see the the following paginated table:



      The p-values are computed at the cell line level, as detailed in the How are the p-values computed at the cell line level? from the Frequently Asked Questions section.
      Following the "Barplot" and "Boxplot" icons associated with displayed genes, will generate barplot and boxplot graphs of the gene expressions in the selected studies, for the selected cell line. Note that, the p-values displayed in those graphs will again correspond to the ones computed at the cell line level.
      Single Study Data: Follow the hyperlinked study name "MulticancerCells_RadiationEffectOnPolysomeRNA." You should see the following screen:



      This display gives information on the experimental conditions studied in the study. To continue with visualizations press the "Submit" button. You should see the following visualization options.



      Barplot: This option will show a simple barplot representing the number of significantly differentiated genes for the given cell line in study 15. The p-value threshold can be customized (the default is <= 0.05). In our case, it will simply show a bar representing 1418 significantly differentiated genes.
      The p-values are computed at the cell line level, as detailed in the How are the p-values computed at the cell line level? from the Frequently Asked Questions section.
      Heatmap:This option will show a heatmap clustering of the gene expressions profiles for samples from cell lines "U87." The clustering procedure is computationally intensive and therefore might take a while to complete.
      Pressing the "Heatmap" option of the visualization options, produces (after clustering is complete) the following display:



      P-values are computed using two-class comparisons (control-treatment) for the given cell line; see How are the p-values computed at the cell line level? in the Frequently Asked Questions section for more details.



      Note that the gene profiles included are for samples from the given cell line "U87" (study 15 also features cell line "U251").
    • Searching studies and cell lines by tissue of origin To display experimental data associated with a tissue of origin, select one of the options from the drop down menu entitled "Select Tissue." Here we show the selection results for the "CNS" (Central Nervous System) tissue:



      The studies displayed in the list feature cell lines with origin from "CNS." Similarly, the display of cell lines is updated to show only cell lines with origin from "CNS." The cell lines and their associated data can be explored as already discussed in Displaying cell line meta data and Displaying experimental data associated with selected cell line sections above.
    • Displaying experimental data associated with selected tissue
      Once a tissue is selected, a list of studies that feature cell lines with that tissue as origin is displayed for selection. In our example, we have selected "CNS" as tissue of origin. We are presented with a list of studies 11, 13, 14, 15, and 16.
      Two scenarios arise: (1)We are interested in displaying results from a single study, and (2) We are interested in comparing results of more than one study. We consider these separately.
      • Displaying results from one study: Let us select study 11 as our study of interest. First note that this study features samples from 18 cell lines, however, our results will be focused to those with tissue of origin the central nervous system. Press the "Compare" button.
        The following visualization options are presented:



        Barplot: This option will show a simple barplot representing the number of significantly differentiated genes for the given tissue in study 11. The p-value threshold can be customized (the default is <= 0.05). In our case, it will simply show a bar representing 1615 significantly differentiated genes.
        The p-values are computed at the tissue level, as detailed in the How are the p-values computed at the tissue level? from the Frequently Asked Questions section.
        Heatmap:This option will show a heatmap clustering of the gene expressions profiles for samples from cell lines of "CNS" origin. The clustering procedure is computationally intensive and therefore might take a while to complete.
        Pressing the "Heatmap" option of the visualization options, produces (after clustering is complete) the following display:



        Only genes whose differential expression satisfies the p-value threshold (default of <= 0.05) will be included for clustering. The p-value can be customized by entering a new desired p-value max and pressing enter.
        The p-values are computed at the tissue level, as detailed in the How are the p-values computed at the tissue level? from the Frequently Asked Questions section.
        Pressing the "View JTree Heatmap" button, launches the TreeView applet:



        Again, note that although the study features samples from 18 cell lines, only the gene profiles for samples from "CNS" are considered for clustering.
      • Comparing results of more than one study: Let us select two of these studies for comparison, studies 13 and 14 and press the "Compare" button.
        The following visualization options are presented:



        Barplot: This option will show a simple barplot representing the number of significantly differentiated genes for the given tissue in the selected studies. The p-value threshold can be customized (the default is <= 0.05).



        The p-values are computed at the tissue level, as detailed in the How are the p-values computed at the tissue level? from the Frequently Asked Questions section.
        Metamap: The metamap displays differential expression significance of genes as represented by p-values. The significance of p-values is represented using a color scheme where the darker the red the smaller (more significant) the p-value is. Black represents that a gene is not featured in the given study.
        Clicking the "Metamap" option of the visualizations displays the following paginated table:



        The p-values are computed at the tissue level, as detailed in the How are the p-values computed at the tissue level? from the Frequently Asked Questions section.
        Following the "Barplot" and "Boxplot" icons associated with displayed genes, will generate barplot and boxplot graphs of the gene expressions in the selected studies, for the selected tissue. Note that, the p-values displayed in those graphs will again correspond to the ones computed at the tissue level.
  4. Arrays
    The arrays module provides an overview of the current contents of the database, including the number of studies, information on platforms, contributors, and available meta-information. Pre-processed data can be downloaded from this module. Integrated queries from this module allow performing comparison of studies by common samples or union of genes within the selected studies.
    To access the Arrays module, select "ARRAYS" on the left side frame of the portal page. You should see the following screen:



    The display gives an overview of the experimental data contents of the data base. Specifically, the number of studies, and information such as the number of genes features in each study, the contributors, and a direct link to the source of the data. The preprocessed data are also available for bulk downloads for each study.
    The arrays can be filtered by stress stimulus by selecting a stimulus from the drop down list above the table. For the moment, most of the studies have "Radiation" as stimulus; this stems from our special interest on the effects of ionizing radiation. However, the contents will be expanded to include studies with more sources of stress.
    Two main scenarios are possible from this module: (1) Focusing attention on a single study, and visualizing differentially expressed genes in the study, and (2) Comparing differential expression of genes in multiple studies. The latter has two subscenarios: comparisons of differential expression of genes for each cell line in each study, and comparisons of differential expression of genes at the study level. We consider each of these possibilities below.
    • Displaying experimental data from a single study
      To display data associated with a single study, check the appropriate box in the overview table and press any of the "Compare" buttons on the bottom of the table. We use study 1 here as an example.
      You are presented with the following screen, giving a summary of the experimental conditions studied (such as dose and time):



      Next, one may choose a level of statistical significance desired for filtering. The default is a p-value of <= 0.05. The p-values here are computed at the study level, as detailed in How are the p-values computed at the study level? of the Frequently Asked Questions section.
      Press the "Heatmap" button, and wait for the clustering procedure to compute the similarities of the gene profiles, for genes satisfying the set threshold. Note that the clustering procedure is computationally intensive and therefore it may take a while for it to complete. You should see the following screen:



      Launch the TreeView applet to display the heatmap by pressing the "View JTree Heatmap" button.



    • Comparing array data by cell lines To compare how genes are affected in different cell lines in different studies, select the studies of interest and press the "Compare Studies by Cell Line" button. We select studies 1-4. You will be presented with the following screen:



      Metamap: The metamap displays differential expression significance of genes for each cell line of each study as represented by p-values. The significance of p-values is represented using a color scheme where the darker the red the smaller (more significant) the p-value is. Black represents that a gene is not featured in the given study.
      You should see the following paginated display; it can be navigated using the navigation buttons at the bottom of the table.



      The p-values here are computed at cell line level, as detailed in How are the p-values computed at the cell line level? of the Frequently Asked Questions section.
      Barplot: This option simply shows the number of genes that are significantly differentially expressed, according to the p-value significance of choice.
    • Comparing array data by genes To compare how genes are affected in different studies, select the studies of interest and press the "Compare Studies by Cell Line" button. We select studies 1-4. You will be presented with the following screen:



      Metamap: The metamap displays differential expression significance of genes for each study as represented by p-values. The significance of p-values is represented using a color scheme where the darker the red the smaller (more significant) the p-value is. Black represents that a gene is not featured in the given study.
      You should see the following paginated display; it can be navigated using the navigation buttons at the bottom of the table.



      The p-values here are computed at the study level, as detailed in How are the p-values computed at the study level? of the Frequently Asked Questions section.
      Barplot: This option simply shows the number of genes that are significantly differentially expressed, according to the p-value significance of choice.
    • Downloading data files Pre-processed normalized data for each array is available to download from "Download" link. The output contains 2 files: 1) standardized (z-score) gene expression values and experiment level t.test pvalues and FDR values for each case-control experiment within that study. 2) ANOVA p.values and FDR values at tissue level and study level.
  5. Examples
    • Displaying apopotosis-associated genes significantly affected by ionizing radiation in the NCI60 panel
      Start by searching genes by GO description, using the term "apoptosis" as shown in the following screen; press the "Search" button:



      Select the "apoptosis" match among the possible GO terms list, and press the "Search Selected" button:



      Select study 16: NCI60_radiation_response among the list of studies featuring genes from the initial selection and press the "Compare" button:



      Select "Heatmap" between the visualization options:



      Press the "View JtreeHeatmap" button to launch the TreeView applet:







  6. Frequently Asked Questions
    • What is StRAP?
      StRAP stands for Stress Response Array Profiler, and is an open-source, web-based resource for storage, profiling, visualization, and sharing of cancer genomic data. The resource supports research on the molecular basis of cancer response to treatment. In particular, the focus is on response to cellular stress, such as induced by ionizing radiation, hypoxia, or cytotoxic drugs. For now major emphasis has been put on response to ionizing radiation, which is a major component of cancer treatment, but which has been relatively understudied.
    • What type of input does StRAP accept?
      StRAP accepts plain text and plaint text file inputs.
    • What file formats can be uploaded/downloaded by StRAP?
      Text files and archived text files are the main data formats available in StRAP.
    • Does StRAP limit the maximum number of genes in a list?
      No, but file size is limited to 100Kb and text areas do not have a defined limit.
    • Who can use StRAP?
      StRAP is a free, open-access resource.
    • Where does StRAP's knowledge base come from and how current is it?
      Four main data repositories reside at the backend of StRAP: (1) Gene associated annotation information derived from the NCBI Gene database, (2) Pre-processed gene expression microarray molecular profile data (including pre-computed statistics), (3) Metadata on cell lines, and (4) Metadata on platform-associated information.
      The gene associated annotation information from NCBI Gene is periodically updated. The sources of the raw microarray data are referred to in the overview of available studies from the Arrays module.
    • What if my input identifiers do not work?
      The gene search options require that the type of input identifiers is known (for example, whether they are gene symbols, gene IDs or general terms used in gene descriptions). In a list, the identifiers should be of the same type. If a mixture of types is available, the convertion tool described in Converting gene identifiers of one type into another can be used to convert identifiers of one type into another.
    • What are the system requirements to run StRAP?
      StRAP can be run on any web browser. The main requirement is an up-to-date Java installation, which is needed for proper functioning of the heatmap TreeView applet. See Why is Jtree not displaying? if you have problems with the applet.
    • What computing technologies are used in StRAP applications?
      The StRAP data repositories reside in an mysql database. The statistical computations and graph plots are performed using the R statistics suite, and Python packages. The web front is developed using PHP and Java Script.
    • How are the p-values computed at the study level?
      At the top level, each study is subjected to a two-way ANOVA analysis performed between all controls and cases to give an overall significance of the study design. The two ANOVA levels used are treatments and cell lines. Therefore, the p-value associated with a gene expresses how significantly the treatment affects the expression of the gene, or how significant the difference of expression of the gene in the studied cell lines is.
    • How are the p-values computed at the tissue level? A tissue level ANOVA analysis is performed between all the controls and cases for each tissue type in a study. Therefore, the p-value associated with a gene expresses how significantly the treatment affects the expression of the gene in the given tissue.
    • How are the p-values computed at the cell line level?
      At the experiment level, for each cell-line/sample, a case-control comparison is performed by t-test analysis. Therefore, the p-value associated with a gene expresses how significantly the treatment affects the expression of the gene in the given cell line.
    • Why is Jtree not displaying?
      Check/Update your Java version if JTree is not displaying correctly; you can do this by following the check/update Java link.
Created for Radiation Oncology Branch @ NCI 2022.
For any suggestions or technical difficulties with this site please contact Dr. Uma Shankavaram
Disclaimer