How to script calibration curve

JD23

Hi, my level of scripting is really basic and I hope you can help me.

How do I write a script to achieve the following?

I have a table with the known concentrations of the calibrators. In each assay (one table per assay) I have the signal of several unknowns as well as the signal for the calibrators, it is basically an internal calibration curve for each assay.

Is it possible to create a script that takes the values from the table of the known concentrations, plots them against their signals for that specific assay (assay table) and then uses the linear regression to calculate the concentration of the unknowns?

Thanks in advance for the help!

Mark_Bailey

OK, thanks for clarifying.

 

Names Default to Here( 1 );

// check for sufficient tables
n tables = N Table();

If( n tables < 2,
	Throw( "Two data tables are required for this analysis" )
);

tables = List();
For( t = 1, t <= n tables, t++,
	Insert Into( tables, Eval( Data Table( t ) << Get Name ) );
);

// identify the calibration and unknown data tables
dlg = New Window( "Assay Unknown Samples", << Modal,
	H List Box(
		lb = List Box( tables ),
		Line Up Box( N Col( 2 ),
			Button Box( "Calibrator Table",
				sel = lb << Get Selected;
				cal lb << Append( sel );
			),
			cal lb = List Box( {},
				N Lines( 1 )
			),
			Button Box( "Unknown Table",
				sel = lb << Get Selected;
				unk lb << Append( sel );
			),
			unk lb = List Box( {},
				N Lines( 1 )
			)
		)
	),
	H List Box(
		Button Box( "OK",
			cal table = cal lb << Get Items;
			Show( cal table );
			unk table = unk lb << Get Items;
			Show( unk table );
		),
		Button Box( "Cancel" )
	)
);

If( dlg["Button"] == -1,
	Throw( "User cancelled" )
);

// unload the dialog
cal table = Data Table( cal table[1] );
unk table = Data Table( unk table[1] );

// include AU 2 values for calibrators from calibration table
combined = cal table << Join(
	With( unk table ),
	Copy formula( 0 ),
	Select With( :Sample, :Peps, :AU 1 ),
	Select( :AU 2 ),
	By Matching Columns( :Peps = :Peps ),
	Drop Multiples( 0, 0 ),
	Include Nonmatches( 0, 1 ),
	Preserve Main Table Order( 0 )
);
Close( cal table, No Save );
Close( unk table, No Save );

// distinquish peps
Current Data Table( combined );
combined << New Column( "Status", "Character", "Nominal" );
For Each Row(
	:Status = If( 1 <= Num( Regex( :Peps, "\w{3}(\d+)", "\1" ) ) <= 29, "Calibrator", "Unknown" );
);


// fit the calibration curve
dlg = combined << Fit Model(
	Y( :AU 1 ),
	Effects( :AU 2 ),
	Personality( "Standard Least Squares" ),
	Emphasis( "Minimal Report" ),
	Where( :Status == "Calibrator" )
);
fit = dlg << Run;

// determine the assay for the unknown samples from the calibration curve
For Each Row(
	fit << Inverse Prediction( Response( :AU 1[] ) );
);

// extract the assays into a new data table
assays = Report( fit )["Inverse Prediction"][TableBox(1)] << Make Combined Data Table;
fit << Close Window;
assays << Delete Columns( Column( assays, 1 ) );

// add the assays to the unknown table
final = combined << Join(
	With( assays ),
	Copy formula( 0 ),
	By Matching Columns( :AU 1 = :Specified AU 1 ),
	Drop Multiples( 0, 0 ),
	Include Nonmatches( 1, 0 ),
	Preserve Main Table Order( 1 )
);
Close( combined, No Save );
Close( assays, No Save );

final << Sort(
	By( :Peps, :Sample ),
	Order( Ascending, Ascending ),
	Replace Table
);

 

Some of the predicted AU 2 values are missing due to the random measurements of AU 1.