ExPCRt Read Me
Version: 1.0
November 2, 2009

Introduction
Quantitative, real-time PCR experiments have become the preferred method for quickly and cheaply quantifying gene expression in a typical molecular biology laboratory setting. There is a need to quickly and accurately normalize and evaluate the data generated from qPCR experiments. Comparing quantitative data in graphical form is a powerful way to uncover trends in gene expression studies. This package aims to ease the process of evaluating and making these comparisons by automatically normalizing, evaluating and graphing qPCR results across multiple experiments.

Tutorial
1. Load the dataset by clicking on the Load File button.  If you have multiple worksheets in the excel workbook they will appear in the Worksheets dropdown box.  Slected th worksheet that has you data and click the Get Data button.  Input files must be in Excel 2003 file format with a *.xls extension.  Excel 2007 files are not supported at this time.  If you are using Excel 2007, simply save your dataset as an Excel 97-2003 file with a *.xls extension.
2.  Your dataset must be formatted by these rules:
	There must be header data in Row 1 
	Column A is for Experiment Name
	Column B is for TargetName
	Column C is for Sample Name 
	Column D is for Ct values
	All Data starts in Row 2
	
You can see the file format by clicking on the File Format Help button.
Be careful that each target and sample replicate have the EXACT same name.  The program supports multiple replicates provided they are labeled correctly.
3. You need to select the housekeeping and control genes for each experiment.  Click on the experiment to load the targets and samples.  Click on the target to be housekeeping gene.  Click on the sample to act as a control.  You will see the selections next to the experiment name.  More than one housekeeping gene is supported.  You can only choose one control gene per experiment.  When you have selected your housekeeping gene and control gene you, the experiment label should turn from red to green.  It is ready to be analyzed.

4.  Click on the "run analysis" button to run the analysis.  The program will prompt you to save the final analysis worksheet.
5.  Navigate to the location you saved your file and open it.  You will see your normalized gene expression data with graphs.

6.  The output file has three worksheets.  The first shows the results organized by sample on the x-axis.  The second graph shows the results organized by target on the x-axis.  The third worksheet shows p-values for each measurement.  All of that data is contained in the spreadsheet and can be manipulated using standard excel techniques.

Cautions:
Be extremely careful labeling your data.  The program uses the text names of each row to normalize and analyze data - replicates must be identical.

Watch out for non-text labels.  For instance, Oct4 is often converted by excel into a date, October 4th.  If the excel gene labels are not text values the program will not work.  Either rename the gene or add a "'" before the gene, ex: 'Oct4.  You can also put the gene name in quotes with an equals sign, ex: ="Oct4".  Both methods will force excel to treat the gene as a text field.

Contact Information:
Jonathan Monk
jonathan.m.monk@gmail.com
www.expertqpcr.com