Modeling:Idealize
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Contents |
Idealization
The Idealize (Idl) button describes sampled data as a sequence of events. This idealization is overlaid on the Data display, and is available to algorithms such as MIL which operate on idealized data.
Idealization also generates measurements such as the mean amplitude and duration of closed and open events, along with all-points amplitude histograms.
To idealize, we decide which class each data point belongs to, then join adjacent points of the same class into dwells (aka events or sojourns). Two methods of classification are provided:
- Half-Amplitude
- half-amplitude threshold detection
- a data point is assigned to the nearest class, using only distance from the mean.
- with two classes (closed and open) this is like drawing a line midway between the two means, and calling everything below the threshold closed, everything above open.
- typically applied to filtered data
- SKM
- segmental k-means
- Qin, F. Restoration of Single-Channel Currents Using the Segmental k-Means Method Based on Hidden Markov Modeling Biophys. J. 2004 86: March 2004
- SKM repeatedly iterates two steps:
- Viterbi detection of the most likely state sequence
- For every time t, SKM computes the likelihood that the data came from each state, and the most likely previous state. The idealization is found by taking the most likely final state and following the chain of m.l. previous states.
- re-estimation of rate constants and class amp/std using the MLSS
- Viterbi detection of the most likely state sequence
- fails on over-filtered data
Both methods require you to prepare a Model and initialize (Grab) the class amplitudes. For details and examples, see
Related Actions
- Modeling:Amps idealizes as a side-effect of amplitude estimation
- Modeling:Idl/Base idealizes while tracking baseline drift, and can idealize Staircase data
- Modeling:Stat computes measurements and histograms for data that's already idealized
Properties
| Data channel index | which A/D channel contains the data to idealize, typically 0 |
|---|---|
| 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 |
| Thread count | number of segments/selections to idealize at once. This will only have a significant effect if your Data Source has more than one segment or selection, and your computer has more than one processor. |
| Clear existing 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. |
| Hist bin count | number of bins for amplitude histograms, displayed after idealization in the Results window |
| Half amplitude | selects the Half-amplitude (threshold) method described above |
| Compute stats | (if Half amplitude is selected) generate all the measurements and histograms. Otherwise, Half-amplitude generates idealization only (which is much faster). |
| SKM | selects the Segmental K-Means method described above |
| LL conv | stop iteration if LL improves by less than this much |
| Max iter | stop after at most this many iterations |
| Drop first/last event | omit the first and last dwell from the idealization. Useful when idealizing selections that begin and end with partial events (such as a piece of a long closed event) |
| If class | omit the first/last event only if they are in a certain class, typically class 1 (black/closed) |
| Re-estimate | after finding the event sequence, re-estimate model paramters and repeat until max iterations. Otherwise, yield the first event sequence, using initial model parameters, and don't iterate. |
| Fix kinetics | when/if re-estimating, don't re-estimate state transition probabilities. |
| Class | (the following "fix" settings apply to this class. #1 is black) |
| Fix Amp | when/if re-estimating, don't re-estimate the mean amplitude of this class |
| Fix Std | when/if re-estimating, don't re-estimate the standard deviation of this class |
| Apply dead time to statistics | compute statistics as though the dead time (main window upper-right) had been applied (by joining events shorter than the dead time with the previous event). This way stats such as "lifetime" will be more in agreement with MIL. |
| Apply dead time to idealization | modify the idealization by joining events shorter than the dead time with the previous event. This is non-reversible. It's useful to see what MIL sees when it internally applies a dead time, but in general leave this un-checked so you can change the dead time later for MIL. |
Results
In the textual Report window: the measurements of each segment/selection
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.
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