20.309:DNA Melting Data Analysis Advice: Difference between revisions
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In broad outline, the steps to take for data analysis are: | In broad outline, the steps to take for data analysis are: | ||
#Filter out noise | |||
#Convert raw data from voltages to temperature and percent hybridized | #Convert raw data from voltages to temperature and percent hybridized | ||
#If desired, reduce the amount of data (optional) | #If desired, reduce the amount of data (optional) | ||
#Ensure that the resulting dataset is single valued | #Ensure that the resulting dataset is single valued | ||
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In addition, you may choose to model other effects such as bleaching. What model will you use and how will you work it into the data analysis? | In addition, you may choose to model other effects such as bleaching. What model will you use and how will you work it into the data analysis? | ||
===Filtering=== | |||
Time domain filtering of the raw data significantly reduces noise. Useful Matlab functions include: <code>conv</code> and <code>filter</code>. Remember to account for the edge effects of these functions. You can pad your data on either end with the initial and terminal values to reduce the edge effects. | |||
<code>Resample</code> is not a good function for low pass filtering. | |||
===Converting=== | ===Converting=== | ||
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In order to convert to relative fluorescence (or percent hybridized), you must make some sort of assumption. | In order to convert to relative fluorescence (or percent hybridized), you must make some sort of assumption. | ||
=== | ===Data reduction=== | ||
Useful | Here is where the <code>resample</code> function comes in handy. | ||
===Single value=== | |||
In will be necessary to take the (discrete time) derivative of the data, dF/dT. As such, T (temperature) must be a single valued function. (Otherwise, the derivative will blow up.) | |||
It is possible that (after filtering and reduction) there will be identical values of T. If there is more than one sample with the same temperature value, it will be necessary to transform your dataset into a single valued function. Useful functions for this purpose include: <code>sortrows</code> and <code>for ... end</code>. | |||
===Differentiation=== | |||
You will probably find the Matlab function <code>diff</code> quite useful. | |||
</div> | </div> | ||
Revision as of 09:00, 4 October 2007
Overview
In broad outline, the steps to take for data analysis are:
- Filter out noise
- Convert raw data from voltages to temperature and percent hybridized
- If desired, reduce the amount of data (optional)
- Ensure that the resulting dataset is single valued
- Take the (discrete time) derivative
In addition, you may choose to model other effects such as bleaching. What model will you use and how will you work it into the data analysis?
Filtering
Time domain filtering of the raw data significantly reduces noise. Useful Matlab functions include: conv and filter. Remember to account for the edge effects of these functions. You can pad your data on either end with the initial and terminal values to reduce the edge effects.
Resample is not a good function for low pass filtering.
Converting
Transforming the raw data is straightforward. Only simple mathematical operations should be required.
In order to convert to relative fluorescence (or percent hybridized), you must make some sort of assumption.
Data reduction
Here is where the resample function comes in handy.
Single value
In will be necessary to take the (discrete time) derivative of the data, dF/dT. As such, T (temperature) must be a single valued function. (Otherwise, the derivative will blow up.)
It is possible that (after filtering and reduction) there will be identical values of T. If there is more than one sample with the same temperature value, it will be necessary to transform your dataset into a single valued function. Useful functions for this purpose include: sortrows and for ... end.
Differentiation
You will probably find the Matlab function diff quite useful.
