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The primary use of the dot plot is visualizing intensity values across samples.
We will invoke a dot plot from a gene list child spreadsheet with genes on rows.
Right-click on the row header of the gene you want to visualize
Select Dot Plot from the pop-up menu (Figure 1)
Figure 1. Creating a dot plot of gene intensity values
A dot plot will be displayed in a new tab (Figure 2).
Figure 2. Simple dot plot of a single gene that shows the distribution of intensities across all samples
There are many customizations that can be made to this simple plot.
Figure 3. Configuring the data shown on the plot
The Configure Plot dialog lets you change how the data is displayed on the plot. We will make a change to illustrate the possibilities.
Set Group by to 4. Tissue using the drop-down menu
This allows us to group the samples by any categorical attribute. These attributes are specified in the parent spreadsheet.
Select OK to modify the plot
We could also have changed the grouping of samples using the Group by drop-down menu above the plot.
The order of the group columns is alphabetical by default, but can be changed to match the spreadsheet order by selecting Categoricals in spreadsheet order in the Configure Plot dialog (Figure 3).
Figure 4. Changing the appearance of a dot plot using the plot properties dialog
The Plot Properties dialog lets you change the appearance of the plot. We will make a few changes to illustrate the possibilities.
Set Shape to 3. Type using the drop-down menu
Select the Box&Whiskers tab
Set Box Width to 15 pixels
Select the Titles tab
Set X-Axis under Configure Axes Titles to Tissue
Select OK to modify the plot
Alternately, we chould have changed the shapes using the Shape by drop-down menu above the plot. The dot plot now shows four columns with thinner box and whisker plots for each and different shapes for different sample types (Figure 5).
Figure 5. The Dot Plot can be modified to optimally visualize your data
Like many visualizations in Partek Genomics Suite, the dot plot is interactive.
Legends can now be dragged and dropped to new locations on the plot. Samples can be selected by left-clicking the sample or left-clicking and dragging a box around samples.
Left-click and drag to move around the plot.
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Select Configure Plot () from the plot command bar to launch the Configure Plot dialog (Figure 3).
Select Plot Properties () from the plot command bar to launch the Plot Properties dialog (Figure 4)
Select () to activate Selection Mode
Select () to activate Zoom Mode
Left clicking on a region will zoom in on it. The zoom level can be reset by selecting ().
After zooming in, select () to activate Pan Mode
Select () to move between rows on the source spreadsheet
Select () to swap the horizontal and vertical axes
The Manhattan plot is a common way to visualize p-values or log-odds ratios for GWAS studies across genomic coordinates.
The starting point for a Manhattan plot is a spreadsheet with SNPs on rows and p-values or log-odds ratios in a column. If beginning with p-values, you will need to convert the p-values to -log10(p-value).
Select the column with p-values
Select Transform form the main toolbar
Select Normalization & Scaling
Select On Columns...
In the Normalization tab, set Base of the Log(x + offset) to 10
Select OK
Go to Transform > Normalization & Scaling > On Columns... again
Select the Add/Mul/Sub/Div tab
Set Multiply by Constant to -1
Select OK
The column now contains -log10(p-value).
We can now invoke the initial plot.
Select View from the main toolbar
Select Genome View
The Genome View tab will open. This plot will need to be configured.
Select the Profiles tab
Remove any unwanted profiles
Select Add profile
Select Column
Select the column with the -log10(p-value) or logs-odds ratio values from the drop-down menu
Select Value for Color by
Select point from the Style drop-down menu
Select OK to add the profile
Select OK to close the Configure Plot Properties dialog
The plot will now show a Manhattan plot (Figure 1).
Figure 1. Customized Genome View showing genomic locations on the x-axis and -log10(P-values) of SNPs on the y-axis (Manhattten plot). Each dot represents a single SNP. The Cytoband is shown along the bottom of the plot
It is also possible to display multiple chromosomes at the same time.
Select Show All in the upper-right hand corner of the plot
This displays all chromosomes vertically. We can display them horizontally for a better view.
Select Genome in line for Layout
Select OK
To further improve the genome-wide view, we can remove the cytoband, remove the genomic position label, color points by chromosome, and increase point size.
Select Cytoband in the upper right-hand corner
Select the Axes tab
Deselect Show Base Pair Labels
Select Profiles
Select Configure
Set Color By to a column with chromosome for each SNP/loci as a category
Set Shape Size to 5.0
Select OK to close the Configure Profile dialog
Select OK to apply changes
The plot will appear as shown (Figure 2).
Figure 2. Full genome Manhattan plot
For details on Genome View see Chapter 6: The Pattern Visualization System in the Partek User's Manual.
This user guide illustrates:
This user guide assumes the user is familiar with the hierarchy of spreadsheets and analysis in Partek Genomics Suite.
Many plots available in Partek Genomics Suite are not discussed in this user guide. A more thorough review of Partek Genomics Suite visualizations can be found in Chapter 6: The Pattern Visualization System of the Partek User's Manual available from Help > User’s Manual in the Partek Genomics Suite main toolbar.
There is no specific data set for this tutorial. You may use one of your own microarray experiments or use a data set from one of our tutorials.
Visualizations are generated using data from a spreadsheet. Some visualizations allow interactive filtering on the plot, but others do not. If you only wish to include certain rows or columns in a visualization, you may need to create a spreadsheet with only the rows or columns of interest by applying a filter and cloning the spreadsheet.
In general, probe(set)/gene intensity values may be visualized from either an ANOVA spreadsheet or a filtered ANOVA spreadsheet. Because intensity data is stored in the parent spreadsheet, the parent and child spreadsheets should be visible in the spreadsheet navigator with the appropriate parent/child relationship (Figure 1).
Figure 1. Down_Syndrome-GE is the parent spreadsheet; ANOVAResults and A are child spreadsheets of Down_Syndrome-GE
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The volcano plot displays p-values and fold-changes of numerous genomic features (e.g., genes or probe sets) at the same time. This allows differentially expressed genes to be quickly identified and saved as a gene list.
Note: the same list can be generated without a visual aid using the List Manager (ANOVA Streamlined tab).
We will invoke a volcano plot from an ANOVA results child spreadsheet with genes on rows.
Select View from the main toolbar
Select Volcano Plot (Figure 1)
Figure 1. Invoking a volcano plot on an ANOVA results spreadsheet
The Volcano Plot Configure dialog will open (Figure 2).
Figure 2. Select the columns to display in the volcano plot
Select the fold-change and p-value columns you would like to visualize from the ANOVA results spreadsheet; here we have chosen 12. Fold-Change(Down Syndrome vs. Normal) for the X Axis and 10. p-value(Down Syndrome vs. Normal) for Y Axis
Select OK
Figure 3. The volcano plot shows each probe(set)/gene as a point. The X Axis shows fold change with no change (N/C) as the mid-point. The Y Axis shows p-values in descending value from a maximum of 1 at the X Axis intersection.
To facilitate analysis, we can add cutoff lines for both fold-change and p-value.
Select the Axes tab
Select Set Cutoff Lines (Figure 4)
Figure 4. Adding cutoff lines to the volcano plot
Set Vertical Line(s) to 1.3 and -1.3
Set Horizontal Line(s) to 0.05
Select Select all points in a section
Select OK (Figure 5)
Figure 5. Setting cutoff lines. The vertical lines are fold-change cutoffs. The horizontal line is a p-value cutoff.
Select OK to close the Plot Rendering Properties dialog
The volcano plot now has cutoff lines for fold-change and p-value (Figure 6).
Figure 6. Cutoff lines facilitate visual analysis of ANOVA results
Because we selected Select all points in a section when adding the cutoff lines, selecting any of the quandrants will select all probe(sets)/genes in that quadrant. If this option is not selected, individual probe(sets)/genes or groups can be selected using selection mode. Gene lists can be generated from selected probe(sets)/genes.
If columns are selected in the ANOVA results source spreadsheet for the volcano plot, only those columns will be included in the created list.
Select the upper right-hand quadrant of the volcano plot
Right click the selected quadrant
Select Create List (Figure 7)
Figure 7. Creating a gene list from a volcano plot
Give the new list a name and description as appropriate
Select OK
The list will be saved as a text file and open as a child spreadsheet in the Analysis tab.
The Violin plot in Partek Genomics Suite is similar to the Profile Trellis plot in that it displays probe(set)/gene intensity values across samples and genes. However, the Violin plot has additional options not shared by the Profile Trellis plot. Here, we will explore one use case for the Violin plot.
For this example, we will use the data set and lists created in the . We have a list of 23 genes that are differentially regulated in tissue samples from patients with Down syndrome and normal controls. We want to display the mean intensity values for Down syndrome and normal samples for each of the 23 genes on a single plot. To do this, we first need to filter the probe intensities spreadsheet to include only the intensity values for the 23 genes of interest.
With the probe intensities spreadsheet and the gene list open in the Analysis tab, follow these steps to filter the probe intensities spreadsheet.
Select the probe intensities spreadsheet in the spreadsheet tree; here, it is Down_Syndrome-GE
Select Filter from the main task bar
Select Filter Columns
Select Filter Columns Base on a List... (Figure 1)
Figure 1. Invoking filter columns by a list
The Filter Columns dialog will open (Figure 2).
Figure 2. Configuring the Filter Columns dialog to filter by probe set ID
Select your gene list from the Filter base on spreadsheet drop-down menu; here, we selected Down_Syndrome_vs._Normal
Select the column of your gene list that matches the column IDs you want to filter from your probe intensities spreadsheet; here, we selected 2. Probeset ID
Select OK to apply the filter
A black and yellow horizontal bar will appear at the bottom of the spreadsheet. This is the filter indicator showing the proportion of columns (genes/probesets) filtered out (black) and retained (yellow). To continue working with the filtered probeset intensities, we can clone the filtered spreadsheet.
Right-click on the filtered probe intensities spreadsheet in the spreadsheet tree
Select Clone... from the pop-up menu (Figure 3)
Figure 3. Cloning a spreadsheet with a filter applied will clone only the retained rows/columns
Name the new spreadsheet; we chosen 2
Select OK
The cloned spreadsheet is a temporary file. To ensure we can use it again if we close Partek Genomics Suite, we should save the filtered probe intensities spreadsheet.
Name the new file; we chose Down_Syndrome_vs_Normal_Probe_Intensities
Now we have a spreadsheet containing only the probe intensity values for our 23 genes of interest (Figure 4).
Figure 4. Filtered probe intensities spreadsheet
We can now invoke the Violin plot. Make sure to have the filtered probe intensities spreadsheet selected (in blue) in the spreadsheet tree as shown (Figure 4).
Select View from the main taskbar
Select Violin Plot from the menu
A Violin Plot tab will open (Figure 5). This plot shows the intensity value ranges of the 23 genes (probe sets) for all samples as violin plots.
Figure 5. Viewing violin plots for 23 genes
Select View from the main taskbar
Select Toggle Properties
We can now see the plot properties panel to the left of the violin plot (Figure 6).
Figure 6. The violin plot can be configured using the plot properties panel
Although it is called the Violin plot, this visualization can also be used to display box and whisker plots, error bar plots, and gradiant plots. For this example, we will generate box and whisker plots, summarized by Type (Down syndrome and normal), for each gene.
Select Box and Whisker Plot from the Plot type drop-down menu
Select Type from the Summarize by drop-down menu; this can be any categorical variable
Select Hide legend from Legend Options
Select Apply to modify the plot
The modified plot shows box and whisker plots, Down syndrome samples in red and normal in blue, for each gene (Figure 7).
Figure 7. Viewing average probe intensity values for two groups across 23 genes as box and whisker plots
To improve our view of the gene symbols, we can modify the X-axis legend.
Select X-Axis from the tabs in the plot properties panel
Set Text angle to 90 under Labels
Uncheck Trucate labels under Labels
Uncheck Show Outline under Blocks
Uncheck Columns under Attributes
Select Apply (Figure 8)
Figure 8. Configuring the X-axis label
The gene symbol for each column should now be visilble (Figure 9). In cases where probe intensities for your genes of interest fall across a wide range, it may be helpful to normalize the probe intensity distributions of each gene. This is equivalent to what is done to display a heat map of probe intensity values.
Figure 9. X-axis now labels with gene symbols for each gene
Select the Style tab
Select Standardize - shift column to mean of zero and scale to standard deviation of one from the Normalization options
Select Apply
The box and whisker plots are now centered with a mean of zero and scaled to have a standard deviation of one (Figure 10). Similar to a heat map, this makes it easier to visualize which genes are upregulated and which are downregulated. Here, we can see that most of the 23 genes are expressed more highly in Down symdrome patients.
Figure 10. Viewing normalized box and whisker plots
Plots can also be split by categorical variables. We can use this to visualize differential expression of genes between Down syndrom and normal patients in different tissue types.
Select Configure profile
Select Switch to Advanced (Figure 11)
Figure 11. Simple options for configuring profiles in the plot
Select Sub-Plot for Tissue (Figure 12)
Figure 12. Configuring plot properties to split by Tissue
Select OK
Several options will need to be reconfigured before we apply this change.
Select Standardize - shift column to mean of zero and scale to standard deviation of one from the Normalization section
Select the X-axis tab
Set Text Angle to 90
Deselect Truncate labels
Deselect Show outline
Deselect Columns
Select Apply
There should now be a sub-plot for each category, in this case there are four sub-plots, one for each tissue (Figure 13). There are no error bars for several plots because there are not enough samples in those categories.
Figure 13. Splitting a plot by a categorical factor, Tissue, and grouping by another categorical variable, Type
These sub-plots can be displayed all together, or individually.
Select 1 from the Items/Page drop-down menu
You can now move through the sub-plots by selecting Next >.
Select All from the Items/Page drop-down menu to return to the 2x2 view
This data can also be displayed as a gradient plot (Figure 14) or error bar plot (Figure 15) by changing the Plot type using the drop-down menu in the Style tab. By default, the shading range in the gradiant plot and the error bars show +/-1 standard deviation from the mean.
Figure 14. Gradient plot
Figure 15. Error bar plot
The final option, violin plot, cannot be used to display samples grouped by a categorical variable. To view a violin plot, we must remove the Summarize by selection.
Select (One profile per sub-plot) from the Summarize by drop-down menu
Select Violin plot from the Plot type drop-down menu
Select None - do not adjust values for Normalization
Select Apply
The plot now displays violin plots for each gene showing the distribution of probe intensity values for each tissue in a separate sub-plot (Figure 16).
Figure 16. Violin plots for each gene, sub-plots for each tissue
Sort Rows by Prototype is a function that can identify genes with similar expression patterns. For example, if a gene with an interesting expression pattern has been detected, using Sort Rows by Prototype makes it possible to find other genes that have a similar pattern of intensity values. Although this is most commonly used for changes in gene expression over a time course, it can be applied to other experimental designs as well.
To invoke Sort Rows by Prototype_,_ probe(sets)/genes must be on rows. If you want to use this tool to analyze the main intensity values spreadsheet, the spreadsheet must be transposed prior to analysis. A common way to view and analyze gene expression in a time-series experiment is to include means or LS means in the ANOVA spreadsheet.
Configure the ANOVA dialog to include the factor or interaction of interest
Select Advanced... from the ANOVA dialog
Select LS-Mean or Mean
Use the drop down menus to select the factors or interaction you want the LS mean / mean of
Select Add for each
Select OK (Figure 1)
Figure 1. Using Advanced ANOVA setup to include group means in the ANOVA output
Select OK to close the ANOVA configuration dialog and open the ANOVA spreadsheet
The Sort Rows by Prototype function uses every non-text column in a spreadsheet to build and compare patterns; any columns you do not want to include in the pattern similarity analysis need to be removed before running the function.
If you want to preserve the ANOVA spreadsheet contents, clone the ANOVA spreadsheet prior to deleting columns.
Select columns you want to remove
Right-click on a selected column headers
Select Delete from the pop-up menu
We can now invoke Sort Rows by Prototype on the modified spreadsheet.
Select Tools from the main toolbar
Select Discovery
Select Sort Rows by Prototype... (Figure 2)
Figure 2. Invoking Sort Rows by Prototype on spreadsheet with LS mean values for conditions/time points
The Sort Rows by Prototype dialog will launch (Figure 3).
Figure 3. Sort Rows by Prototype dialog
This dialog allows you to configure the pattern, or prototype, that all probe(sets)/genes will be compared to by Sort Rows by Prototype_._
The Select Dissimilarity Measure drop-down menu allows to select from a wide variety of parametric and non-parametric measures of dissimilarity.
After configuring the prototype and selecting a dissimilarity measure, select Sort to run the function
Select Cancel to close the dialog
A new column 1 will be added to the spreadsheet and the rows will be reordered (Figure 4). The new column contains the dissimilarity score for each row; the lower the value, the more similar the row is to the prototype. The row with the highest similarity to the prototype is listed first, with the other rows listed in descending similarity to the prototype.
Figure 4. Result of sorting by prototype. The prototype gene is in the first row, while the other genes are listed based on their similarity to the prototype gene. Smaller proximity values imply more similarity to the selected shape
To view the results, we can generate a profile plot of several of the rows. For example, here we will show the top five most similar probe(sets)/genes.
Select the row headers of the top 5 rows by selecting each while holding the Ctrl key or selecting the first then fifth while holding the Shift key
Select View from the main toolbar
Select Profiles
Select Row Profiles
Select Select for both Plots and X-Axis in the Configure Data Source dialog
The profile plot will open as a new tab (Figure 5).
Figure 5. Profile plot of 5 probe(sets)/genes most similar to the prototype used in Sort rows by prototype
A scatter plot is a simple way to visualize differentially expressed genes. We can plot a scatter plot with gene expression values for two samples at one time. While most probe(sets)/genes fall on a 45° line, up- or down-regulated genes are positioned above or below the line.
To draw a scatter plot, you first need to transpose the original intensities spreadsheet so that the samples are on columns and probe(sets)/genes are on rows.
Select the main spreadsheet
Select Transform from the main toolbar
Select Create Transposed Spreadsheet...
Select the column with sample IDs from the drop-down menu
Select OK
A new temporary spreadsheet will be created with probe(sets)/genes on rows and samples on columns.
Select the two sample columns you would like to compare
Select View from the main toolbar
Select Scatter Plot (Figure 1)
Figure 1. Invoking a scatter plot from a spreadsheet with probe(sets)/genes on rows and samples on columns
Select Yes when asked if you want to only use the selected columns
Select Yes when asked if you are sure you would like to draw the scatter plot
The scatter plot will open in a new tab. We can add a regression line to the plot.
Select Axes
Select Set Regression Lines
Select Regression line of y on x
Set Line Width to 5
Select OK (Figure 2)
Figure 2. Configuring a regression line
Select OK to close the Plot Rendering Properties dialog
The scatter plot now features a regression line dividing the probe(sets)/genes (Figure 3).
Figure 3. Each dot on the plot represents the intensity value of a probe(set)/gene
The MA plot can be used to display a difference in expression patterns between two samples. The horizontal axis (A) shows the average intensity while the vertical axis (M) shows the intensity ratio between the two samples for the same data point. In essence, an MA plot is a scatter plot tilted to the side so that the differentially expressed probe(sets)/genes are located above or below the 0 value of M. An MA plot is also useful to visualize the results of normalization where you would hope to see the median of the values follow a horizontal line.
The MA plot is invoked on the original intensities spreadsheet with any need for transposition.
Select View from the main toolbar
Select MA Plot
The MA plot will launch in a new tab showing the first two rows as the comparison (Figure 4).
Figure 4. MA plot comparing the expression levels between two samples. Each dot on the plot represents a single genomic feature (gene or probe set). The average signal for each genomic feature is shown on the horizontal axis (A), while the ratio is shown on the vertical axis (M).
The samples displayed can be changed using the select sample menus on the left-hand side.
Select () from the plot command bar
Select to open the Configure Plot dialog
Select
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The volcano plot will open in a new tab (Figure 3). Control and color options for the volcano plot are largely similar to those described for a . On volcano plots with many probe(sets)/genes, the shapes and sizes of individual probe(sets)/genes will not be visible until they are selected.
Select ()
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Select ()
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
Select () from the main command bar to save the modified spreadsheet
The Pattern Type options () allow preset shapes to be applied to the prototype within the range specified by the Begin, End, Min, and Max parameters. The final option From Row allows you to select any row number in the spreadsheet to serve as the prototype. This is a useful option if you have a particular gene of interest and want to find other genes with similar expression profiles in your data set. You can also manually configure the prototype by dragging the points.
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Select () from the plot command bar
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The profile plot displays probe(set)/gene intensity values across samples and genes.
We will invoke a profile plot from a gene list child spreadsheet with genes on rows.
Select the rows to be visualized
Right-click on a row header of one of the selected rows
Select Profile Plot (Orig. Data) from the pop-up menu (Figure 1)
Figure 1. Selecting Profile Plot for selected rows
The profile plot will be displayed in a new tab (Figure 2). Lines are probe(sets)/genes and columns are samples from the parent spreadsheet.
Figure 2. Basic profile plot. Each line represents a different prob(set)/gene; each column represents a sample from the parent spreadsheet
A basic profile plot will likely need customization. The plot configuration, properties, and control options are the same as shown for a dot plot. We will illustrate a few modifications here.
We can change the row labels to show each sample ID.
Select the Axes tab
Set Grid to 1
Select Rotate X-Axis Labels and set to 90 degrees (rotates counter-clockwise)
Set Label Format to Column and select 5. Subject
We can add symbols to show which group each sample belongs to.
From the Shape by drop-down menu, select 3.Type
Select OK
Symbols have now been added to each profile line plot (Figure 3).
Figure 3. The profile plot can be modified to facilitate analysis or presentation
Note that samples present on the parent spreadsheet cannot be excluded from the profile plot. To plot only a subset of the samples you must filter the parent spreadsheet.
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The XY plot / bar chart displays the intensity of one probe(set)/gene across two categorical variables. Only one probe(set)/gene may be visualized at a time.
We will invoke an XY plot from a gene list child spreadsheet with genes on rows. The parent spreadsheet should include the categorical variables you want to chart.
Right-click on the row header of the gene you want to visualize
Select XY Plot (Orig. Data) from the pop-up menu (Figure 1)
Figure 1. Invoking an XY Plot from a gene list child spreadsheet
An XY plot will be displayed in a new tab (Figure 2).
Figure 2. By default, an XY plot invoked from a gene list will have the first categorical variable as columns and the second categorical variable as shapes/colors
To display the change in gene expression over time for each treatment condition, we need to modify this plot.
Set X-Axis to 3. Time using the drop-down menu
Set Separate by to 2. Treatment using the drop-down menu
Select OK
To help visualize the connection between time points, we can add connecting lines.
Set Plot Style to lines using the drop-down menu
Select OK
The plot now shows time on the x-axis, plots treatments, and connects treatments across time points with lines (Figure 3). Each point is the LS mean value of all samples with the same values for the two selected categorical variables. The error bars are standard error.
Figure 3. Modifying the XY plot to enable analysis of gene expression changes in a treatment condition over a time course. In this experiment, only the control was measured at time 0.
While most of the plot controls are shared with the dot plot, XY plot does have a few unique options.
This feature is useful when performing visual analysis of patterns in gene expression changes in a list of genes.
It is also possible to invoke an XY plot from the parent spreadsheet using the main toolbar.
Select the parent spreadsheet in the spreadsheet tree
Select View from the main toolbar
Select XY Plot / Bar Chart ...
The Create XY Plot / Barchart dialog will open (Figure 4).
Figure 4. Invoking an XY Plot from the main toolbar
An XY plot will be displayed in a new tab (Figure 5).
Figure 5. The gene name associated with the probe(set) column is displayed as the chart title by default
To switch this plot from to one of the gene lists we have created, we can use the drop-down menu next to the previous/next controls.
The displayed by a XY plot can instead be displayed as a bar chart with overlayed bars, vertically stacked bars, or horizontally stacked bars. A bar chart can be directly invoked or an XY plot can be converted into a bar chart (and vice versa).
Invoke the plot from a gene list using the Bar Chart (Orig. Data) option in the pop-up menu (Figure 1)
Invoke the plot from the main toolbar by selecting one of the bar chart options in the Line Style drop-down menu (Figure 4)
Figure 6. An XY Plot can be converted to a Barchart using the Plot Rendering Properties dialog
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Select ()
Select () from the plot command bar
Select () from the plot command bar
Select () to automatically cycle through each row (gene) in the source spreadsheet
Select () to stop the cycling
The drop-down menu adjacent to the previous/next () controls lets you switch source spreadsheets.
Lines, but not points, can be selected when using Selection Mode ().
Selecting previous/next () will nagivate along either rows or columns, whichever has probe(set)/gene information.
Invoke the plot as an XY plot, select (), then select one of the bar chart options from the Plot Style drop-down menu in the Plot Rendering Properties dialog (Figure 6)
