5. Plotting

One-Dimensional Histograms

The histogram feature of tMAVEN is primarily useful for visualization of data and not diagnostics. Navigate to Plots/Histogram 1D to generate a one-dimensional histogram of all E\(_{fret}\) emission data, the peaks represent states and should look like K gaussians.

A typical one dimensional histogram. Note the number of traces used N in the top right. If a model has been calculated, a population-weighted set of emission distributions is plotted as the solid black with the dotted lines as the individual populations. These lines are not a Gaussian fit of the data, but come directly from the models. Theoretically, better models/processing will result in a better fit of the histogram.

Notable preferences the user might want to change include:

  • hist_force_y to force the y axis value
  • hist_log_y puts the y-axis in log scale
  • model_on will toggle the model as described in Figure
  • hist_false toggles the histogram itself, blue in the figure
  • hist_color will change the color of the histogram

The save icon below the graph can be used to export the figure.

Two-Dimensional Histograms

The two-dimensional histograms in tMAVEN also have the axis of time. Generate by navigating Plots/FRET hist 2D The histogram is automatically smoothed by a median filter which setting can be changed (see below). One of the most useful capabilities is the choice of using post-synchronization. In this case, t=0 represents the time of transition from one specified state to another, see below. Note that post-synch can only operate once a model is run and transitions have been detected.

A two dimensional histogram with post-sync on using the het color map.

Useful preferences:

  • color_ceiling sets the ceiling for the color mapping
  • color_dbl enables two floors
  • color_floor_color is the lower floor
  • color_cmap determines the color map, all matplotlib maps are supported
  • hist_smooth_med toggles the use of a median filter on the graph and hist_smoothx/y determine the widths of this filter
  • time_dt changes the time ticks, set to acquisition time to yield the same axis as traces while time_nbins changes the length of time shown.
  • hist_normalizeframe will normalize the histogram to mitigate photobleaching or additional transition at different times, see Figure

For post synchronization, tMAVEN will use the existing model which has identified transitions so users can essentially monitor all traces after a specific transition in a data set. Notice that N, number of traces used, will likely change since not all traces have all transitions. Small n represents the number of transitions measured not number of traces.

  • sync_postsync toggles post synchronization
  • sync_singledwell set to true shows only the dwell before and after transition. In other words, a false setting will also show future transitions.
  • sync_hmmstate_1 shows the pretransition state while sync_hmmstate_2 shows the post transition state, input “-1” for any other state

The same graph as previous figure with normalization on.