Modeling:Idl/Base
From QuB
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Idl/Base (IB) does four related things:
- customizable idealization with the best elements of SKM and Amps
- constraints on amplitudes and noise see this forum post
- idealization while tracking baseline drift, using a Kalman filter
- idealization of Staircase data
- onscreen visualization (movies) of idealization, to help understand the algorithm
The details are explained in Dr. Lorin Milescu's thesis.
Contents |
Idealization
Idealization consists of one or more iterations of
- find the most likely state sequence (MLSS) given the model and sampled data
- use the MLSS to re-estimate model parameters
The MLSS can be computed by
- Viterbi
- google:Viterbi
- Gamma
- the most likely state s at time t maximizes Γs,t as computed by Baum-Welch
- Forward-Viterbi
- google:Forward-Viterbi
Re-estimation is optional. Repeated re-estimation and iteration can improve the idealization, but if the model parameters are already optimal, it's much faster to just perform step 1 once. Your choices are
- None
- skip step 2 and don't iterate
- Seg K-Means
- as in SKM
- Baum-Welch
Baseline
Idl/Base can track a drifting baseline while idealizing, using a Kalman filter. It can also correct the baseline by adding baseline nodes. Check "bline apply" at the top to subtract the node-baseline from the data.
The theory is an extension of the Viterbi and Forward algorithms. When evaluating P(s1 at t, given s0 at t-1), we calculate the emission probability using (datat - predicted next baseline point from s0,t-1) and bring the filter forward with (datat - amps).
Staircase
A staircase process has jumps in only one direction. An example is fluorescence position data from molecular motors such as actin.
TODO: how is the model interpreted?
Movies
Idl/Base can show the progress of idealization and baseline tracking as a movie in the Data window. If you're trying the movie option, use a short selection of data.
Properties
| Data channel index | which A/D channel contains the data to idealize, typically 0 |
|---|---|
| Replace Idealization | completely remove any idealization, even in parts of the file that are not part of the data source. Otherwise, idealization outside the Data Source is kept. |
| Quiet output | prints significantly less information to the Report window |
| Every iteration | prints information about each iteration to the Report window |
| Data source | |
| Pre-process data | none: use raw data as displayed: use the same baseline correction and filtering as the Data display as such: apply the specified filter and baseline |
| Make movie | show a movie of the idealization's progress in the Data window |
| Save bitmaps | saves the movie as a sequence of .bmp files somewhere |
| Show titles | |
| Max frame count | |
| IDL | |
| BX | |
| BP | |
| Colors | |
| Re-estimation | described above |
| A -> Q | after re-estimating the A matrix (sampled transition probabilities), calculate rate constants (Q matrix) which give rise to that A matrix, and update the model |
| Every iteration | update the model's rates from the A matrix every iteration, or just at the end |
| Fix kinetics Fix amps Fix noise | These checkboxes do nothing since we switched to the amplitude constraint system |
| Max iter | repeat (find MLSS, re-estimate params) at most this many times |
| LL conv | stop iterating when the log-likelihood improves by less than this much |
| Most likely sequence | described above |
| Baseline Track | track baseline drift with a Kalman filter |
| Baseline Std | baseline drift from sample to sample is modeled by a Gaussian distribution |
| Baseline Correct | add baseline nodes to the Data corresponding to the tracked baseline |
| Min dy | add a node when the baseline changes by more than this many A/D units |
| Back Kalman | |
| F-K Count | |
| F-B-K Count | |
| Reest params | |
| Staircase | |
| Max jump | |
| Right only | |
| Global est | |
| LL add Jump | |
| B-W reest | |
| BaseJumpV | |
| UpdateRefState | |
| every | |
| Step Amp reest. | |
| Steps | |
| Init | |
| SSj / SSm | |
| Weighed | |
| Cons. re-est +/- | |
| Merge dwells if dA < [frac] | |
| Split dwells if dA > [frac] | |
| Kinetics re-est | |
| Init | |
| kf | |
| kb | |
| Step Std reest | |
| Initial value | |
| Min Gamma | |
| Weighed | |
| Amp(0) initial value | |
| Meas std reest | |
| Meas std Initial value |
Results
In the textual Report window: exhaustive details of the computation
In the Results window:
Summary:
| amp | the (re-estimated) mean amplitude of each class |
|---|---|
| sd | the (re-estimated) standard deviation of each class |
Segments (and Select, Criteria): all the measurements of each segment/selection
Models: the model used for idealization
Histograms: An amplitude histogram for each segment/selection, overlaid with the Gaussian distribution (amp, std) of each class. You can quickly identify segments that were mis-idealized by looking for histograms whose distribution curves don't match the histogram bars.
See Also
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