A histogram is a plot that summarizes the underlying frequency of a set of data with the variable of interest on one axis and the frequency distribution of that variable in the other axis. In Partek Flow, histogram can be invoked on continuous or categorical variable.
Invoking a histogram
From a data viewer session, drag the histogram icon onto the data viewer canvas (Figure 1)
Figure 1. Drag histogram plot (red rectangle) onto data viewer canvass
Upon dropping the histogram on the canvas, a dialogue opens up with the different data nodes that can be displayed on the histogram. Select your data node of interest (Figure 2).
Figure 2. Select Normalized count data node (red) to display on histogram plot
The first row in the data will be displayed by default in the histogram and in this case, it is the histogram of the expression values for the gene A1BG (Figure 3).
Figure 3. Histogram showing the distribution of AIBG expression (red)
Histogram of Continuous variables
Change the data displayed on the histogram by using the Configuration settings > Content > Data and selecting the desired variable to display. Here the data displayed was switched to “Expressed genes” which is a continuous variable (Figure 4).
Figure 4. Histogram showing the distribution of expressed genes variable (red rectangle)
Use the "Sort by" function to sort the plot. The default sorting is by Value on the x-axis and this default setting is sorted in ascending order. Users have the option to change that by changing the Default to value or frequency in the sort option (Figure 5)
Figure 5. Sort by function can be by Value or Frequency (red)
The sort menu was changed to Value in the case below and user can now sort Value by either ascending or descending order. Here the Value of expressed genes is sorted by descending order (Figure 6).
Figure 6. Histogram of expressed genes sorted by Value in descending order (red)
Users can color the histograms by a categorical attribute using the Color by function (in red below). The bars were colored by the graph-based classifications in the example below (Figure 7).
Figure 7. Histogram annotated by graph-based classifications
Users also have the option to bin by either Count or Size. When binned by Count, the user specifies the number of bins for the data and the distribution is fit into the specified number of bins. Data below is binned by Count (Figure 7).
Figure 8. Histogram of expressed genes with number of bins specified as 5
When binned by Size, the user specifies the number of items in the bin (size of a bin). This is used to calculate the number of bins required for the data. Data below is binned by Size (Figure 8).
Figure 9. Histogram of expressed genes with size of bin specified as 10
Histogram of Categorical variable
In the figure below, a categorical variable (Classifications) was selected to be displayed in the plot and sorted by frequency in ascending order (Figure 9).
Figure 10. Histogram of Classifications variable sorted by ascending order of Frequency
For categorical data, the user can select number of groups in the categorical variable to be binned together. In the figure below, the Classifications variable is binned into groups of 5 (Figure 10).
Figure 11. Histogram of Classifications variable with bin groups set as 5
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