Preferences

In order to perform succesfull loading, detection and classifying, the values in the preference dialog should be set to values that apply to your data set. The preference dialog can be opened by selecting Edit | Preferences or by clicking on the gear icon in the tool bar. The preference dialog will open, and while it is open the main interface can not be used. The current settings are saved when the preference dialog is closed. The preference dialog has 4 main panels, that apply to the different stages of processing. It has a panel for the loading phase, plotting, detection and classification:

Preference Dialog
Figure 1: The preference dialog, with panels for loading, plotting, detection and classification

Loading

The first option, when enabled, will use the preferences from a previous session file when a session file is loaded. The current settings will be overwritten with the settings that were used during the creation of the loaded session. Use this option when you wish to use the same settings for multiple sessions. The default is not the re-use the settings. The checkbox for profiling will measure the performance of loading the data and detection of events: when checked, a report will be generated showing the CPU cycles and memory usage for selected functions. The Auto Save option, when selected, stores the data automatically to a temporary file or a user-specified session file. This is done everytime the user modifies the data structure, and when the program is closed. When the program is closed and no session file has been saved, the program will ask for a location and file name. When this is not supplied, the data will be stored in a file called tempSession.tmp, located in the main application folder. By changing the extension from .tmp to .mat, the file can be loaded as a regular session file.

Plotting

The second panel has currently a single option, which is disabled by default. This setting controls which metric is used when plotting grouped data: when enabled, the option Use median, not mean will force plots to use the median as a grouped metric and the interquartile range (IQR) as a measure of variability (to be precise: 1.58·IQR/√). This option applies to all plots except box plots, which use a median by default. Enabling this option will affect all plots generated after closing the preference dialog.

Detection

The third panel has options for detecting fusion events using the automated algorithm. The checkbox for Multiple detection allows the user to select whether multiple events in one trace can be identified or not. When it is enabled, each event within a site that meets the detection criteria will be detected and added to the data structure. The popup menu for detrending allows the user to select a method that will be used for detrending the data. The detrending method can be used if the data is severely corrupted by trends in the baseline: this option should not be used in general, and is only usable in extreme cases. See the information about detrending on the algorithms page. The detection parameters can be set using the button Set Detection Params..., and these values are the values that will be used by default in the explore detection dialog. The outlier cutoff allows the user to set a maximum number of events per cell before labeling it as an outlier. This value is best determined after the initial detection and examining the box plot for the number of events. Finally, there is an option to enable a manual synapse level when detecting synaptic sites. When the option is enabled, a site is classified as synaptic when its value is at least the value in the detection level field.

Classification

The fourth panel allows the user to set parameters that influence the classification of the detected fusion events. The values of these fields are used to separate events in transient and persistent events, and sub-classify them as fast and slow transient/persistent. Values for these parameters are usually set after initial detection, and using the histogram and boxplots for event duration to establish the different parameters based on (possible) gaps in the histogram data. See the plotting documentation for more information. Please note that the parameters are only applied when the classification is performed. This classification is an optional processing module and can be skipped. See the classification documentation for more details.