MS Data Analysis I: Importing Data into Genespring and Initial Quality Control

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1 Homework: Session 2 GENESPRING MS ONLINE TRAINING SESSION 2 MS Data Analysis I: Importing Data into Genespring and Initial Quality Control Introduction and Lab Overview: If you need help during completion of this exercise, there are a number of guides available which you can access through the GSMS Help menu. In addition you can post questions on Basecamp at In this session you will be importing.mhd files into GeneSpring MS (GSMS). The data set is comprised of five replicate ms runs of three samples. These samples represent three experimental conditions from a time course experiment; 0 hour or control, and 2 hour and 4 hour treatments. MS acquisition was performed in ms mode on a QTOF instrument. Mass features were extracted in Qualitative Analysis. In profiling experiments, MS data analysis can be viewed as two primary steps; quality control (QC) and statistical analysis. Because QC is the first step, and subsequent analyses are dependent on the results, it is often more convenient to perform initial QC in Mass Profiler prior to importing data into GSMS. This is largely because opening data files in Mass Profiler is much faster and easier than importing into GSMS. In addition, this QC step provides information that can be useful when importing data into GSMS. For example the spread in retention time between replicates (e.g. 0.2 minutes) can be used as a guide to setting alignment values during data import. Note: Performing QC in Mass Profiler is not required for this homework assignment. If you choose to use Mass Profiler feel free to post discussion questions and comments; your instructor will answer questions and provide support as time allows. GSMS Data Import Before starting you will first need to download the.mhd files used in this exercise. You can do so at Unzip the files to a convenient folder that will be easy to find. Important: I strongly recommend that you don t run any unnecessary applications on your computer during the import process. GSMS requires a significant amount of memory and tends to lock up when competing with too many other programs.

2 When you start GSMS it will automatically load a default data set or Experiment Group (after data import is complete you will want to close this experiment window). From the File menu at the upper left select Import Data. Select just one of the data files and click open. When importing data GSMS creates an Experiment Group that will contain all of your aligned data as well as any mass lists, trees, etc. that you create. After you select your first file, you will be prompted to name your Experiment Group. After you have done so you will see the window shown at right. Click Add All to add the remaining.mhd files. You may see a message saying that some files (e.g. the.zip file) were not added because they are not in.mhd format. Click OK. Then click next to proceed with data import. You will see a small window indicating that the files are being processed. Note: this process can last a minute or two to several minutes. If it takes considerably longer, try moving the window with your mouse. If it does not move, the software has locked up and you will need to stop the application (control-alt-delete) and try again. Data Import: Feature Alignment Parameters After the initial preprocessing step, the Configure Alignments Parameters

3 window will appear. This step allows you to align feature retention time (RT) and mass. Click Show Filters at the bottom of this window to view the Prealignment Filters. Set Mass Defect to Peptide-Like, Charge State to Multiple charge required, and Min Abundance to 15,000. Leave all other values at default settings. Click OK. Under the RT tab, leave Before RT Correction values at the default settings. Change After RT Correction Slope to 0.2. Under the Mass Tolerance tab, change the Slope to 10.0 ppm. Click next. Preprocessing will continue. As above, if this takes more than 10 minutes, move the small window with your mouse to check if the software has locked up. When preprocessing is complete a window showing a RT vs Mass plot of the aligned features will appear. Click Create Samples. Data Import: Sample Attributes The Sample Attributes window allows you to assign values to your data files that are relevant to the experiment. An example of an attribute would be Time for a time course experiment like the one we are working with. In the New Attributes window, select New Attribute. From the list choose Time. If you scroll completely to the left you will see the data file names. In the column for your new Time attribute, enter 0 hour for each 0 hour file, 2 hour for the 2 hour files and so on. Click New Attribute again. This time select Custom Attribute. At the top of the new column type Replicate. Then assign the values a

4 through e for the appropriate data file. When you are finished click Next to continue. The New Experiment Checklist window will appear. Data Import: New Experiment Checklist Normalizations. Click on Normalizations. Select Use a Saved Scenario, choose Standard MS and click Load Scenario. Normalization will commence. Parameters. Click Parameters, then Import Parameter. Choose Select All then click OK. Click Save. Interpretation. Select Interpretation. Set all Parameters except Time to Do Not Display. Set Time to Continuous. Click Save to make this the default interpretation. Congratulations! You ve made it through the data import process.

5 GSMS Data Filtering Filtering: Relative Frequency From the Filtering menu, choose Filter on Relative Frequency. For the mass list use All Masses. For Interpretation use Time. Set Relative Frequency to In all samples, and Minimum Percent to 100. Note the number of features that pass the filter. Save these features as a new mass list. Filtering: Error From the Filtering menu, choose Filter on Error. Use the mass list you generated in the previous step (relative frequency) and the Time Interpretation. Use Coefficient of Variation for Error Type. Set the maximum to 15. Restrict masses to those that appear in 3 out of 3 conditions. Note the number of features that pass your filter.