Endpoint analysis can be set-up using the provided wizards or the comprehensive library of transforms can be used to set-up more advanced analysis.
A transform is essentially a layer of analysis which takes one or more matrices of data as input, performs some operation/analysis and results in calculated output matrices of data and/or report content. With this powerful concept the extensive tool-box of transforms offers total flexibility in assay analysis design combined with minimum configuration.
An unlimited number of transforms can be set-up. Outputs from one transform can be used as input to other transforms. Transforms can operate on data from a single microplate or with assays across multiple plates.
As Dazdaq develop new transforms they will be made available for download from the Dazdaq web-site for seamless integration into existing applications.
Available endpoint transforms include:
Automatic set-up of typical blank correction requirements:
One blank group on a plate
One blank group for each unknown group
Complete customisation of associations between blank and sample types to correct
Plot a concentration curve from standard measurements and specified concentration values:
Use a selection of curve fit methods:
Use data across multiple plates:
Set interpolation and extrapolation rules:
Optionally show where calculated points fall on the fit:
Apply a specified factor to groups:
For qualitative assays specify a single cut-off point (a numeric value, a measurement from the plate, or mathematical expression of these terms) to label your unknowns:
Write custom-expressions with references to wells, groups, transform outputs, calculated curve-fit coefficients using standard mathematical and list operators:
Auto-flag by Well
Automatically flag wells which satisfy or fail a specified condition:
Other included Endpoint transforms
||Specify labels between 2 cut-off points.
|Dual user-defined expression
||Write expressions in terms of 2 input matrices.
||Export data to text files.
||Apply a factor to all endpoint values.
||Proportionalise the data.
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