1H NMR Analysis (SIMCA) - Revision history

Mjeppesen at 05:14, 20 January 2022

2022-01-20T05:14:16Z

← Older revision		Revision as of 05:14, 20 January 2022
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	9. Click Autofit button on the toolbar		9. Click Autofit button on the toolbar

	~~[[category:Protocols\|H NMR Analysis (SIMCA)]]~~
	[[category:Data_Processing_and_Analysis\|H NMR Analysis (SIMCA)]]		[[category:Data_Processing_and_Analysis\|H NMR Analysis (SIMCA)]]

Mjeppesen: LearningSorting

2021-09-11T04:21:04Z

LearningSorting

← Older revision		Revision as of 04:21, 11 September 2021
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	9. Click Autofit button on the toolbar		9. Click Autofit button on the toolbar

	[[category:Protocols\|~~Protocols~~]]		[[category:Protocols\|H NMR Analysis (SIMCA)]]
	[[category:Data_Processing_and_Analysis]]		[[category:Data_Processing_and_Analysis\|H NMR Analysis (SIMCA)]]

Mjeppesen: Added to Category

2021-09-11T03:22:50Z

Added to Category

← Older revision		Revision as of 03:22, 11 September 2021
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	9. Click Autofit button on the toolbar		9. Click Autofit button on the toolbar

			[[category:Protocols\|Protocols]]
			[[category:Data_Processing_and_Analysis]]

Jcatazaro at 15:30, 20 March 2017

2017-03-20T15:30:24Z

← Older revision		Revision as of 15:30, 20 March 2017
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	~~[[Category:Protocols]]~~
	~~[[Category:Metabolomics]]~~
	==Excel Processing==		==Excel Processing==
	Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.		Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Bozhang at 05:59, 3 October 2012

2012-10-03T05:59:43Z

← Older revision		Revision as of 05:59, 3 October 2012
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	==Excel Processing==		==Excel Processing==
	Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.		Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

	~~Note:~~

	Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.		Use the standard method to remove instrument noise. Click here [http://bionmr.unl.edu/wiki/Noise_removal_for_PCA] for details.
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	4. Click “Open”		4. Click “Open”

	5. Click on the comma option. The data formatting should now look correct		5. Click on the comma option The data formatting should now look correct.

	6. Click “OK”		6. Click “OK”
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	It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.		It is the row of labels that was inserted using Excel. You can delete the blank row to avoid this.

	10. Use the “Commands” button (bottom left) to “transpose” the data set.		10. Use the “Commands” button (bottom left) to “transpose” the data set
	The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.		The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

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	7. Next, the discriminator values should be set as 1 or 0.		7. Next, the discriminator values should be set as 1 or 0.

	8. Selected data may still be excluded prior to the statistical analysis.		8. Selected data may still be excluded prior to the statistical analysis

	9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.		9. The dataset is “fit”, additional components can be added/substracted and the results are visualized using the same commands as for PCA analysis described above.

Bozhang at 05:58, 3 October 2012

2012-10-03T05:58:28Z

← Older revision		Revision as of 05:58, 3 October 2012
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	[[Category:Protocols]]		[[Category:Protocols]]
	[[Category:Metabolomics]]		[[Category:Metabolomics]]

	~~=1H NMR Analysis (SIMCA)=~~

	==Excel Processing==		==Excel Processing==
	Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.		Secondary observation ID labels can be added by inserting a blank second row and filling in the desired labels. This does NOT affect the raw data. It merely makes “viewing” easier in the SIMCA output.

Bozhang at 05:58, 3 October 2012

2012-10-03T05:58:09Z

Bozhang: /* OPLS-DA Analysis */

2012-10-03T05:54:09Z

OPLS-DA Analysis

← Older revision		Revision as of 05:54, 3 October 2012
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	==OPLS-DA Analysis==		==OPLS-DA Analysis==

	1. This approach applies orthogonal signal correction prior to PLS analysis. ~~I need to do more reading… But, I believe that I have basic command sequence needed to explore the approach using~~ SIMCA-P+ ~~Version~~ 12.0.		1. This approach applies orthogonal signal correction prior to PLS analysis. This manual is for SIMCA-P+ 12.0 or higher version.

	2. Start from an Excel file that has NOT been ~~noise corrected~~ and ~~NOT been autoscaled~~.		2. Start from an Excel file that has NOT been autoscaled and any outliers should be removed prior to OPLS analysis.

	3. The data file should be first analyzed using either PCA or PLS as described above.		3. The data file should be first analyzed using either PCA or PLS as described above to find two unsupervised, separated groups for comparison. Wild-type or control groups should be assigned as "0", mutant or treatment group should be assigned as "1". This column of "0" and "1" should be taken as Y values.

	4. ~~The analysis is reportedly hindered by extreme outliers found in~~ PCA ~~2D scores plot (where did I read this…?...~~the ~~Umetrics manual maybe…). So, any outliers should be removed prior to OPLS analysis~~.		4. Follow the same procedure as PCA to import the data.

	5. ~~Open~~ the ~~Excel file in SIMCA, go~~ to ~~the Dataset pull-down menu~~. ~~Choose Spectral Filters~~. ~~In the available column, scroll down and select OCS~~. ~~Press the button labeled as => to move OCS into the selected column~~.		5. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance. A new Workset window will appear.

	6. Click OK.		6. Click "Yes to All" and then another workset window will pop-up with "model number", "type of model", etc.

	7. ~~The OSC panel should appear. Refer to your NMR spectral data to identify bins~~ that ~~contain strong peaks for metabolites (e.g., citrate peaks in mouse urine spectra)~~.		7. Double click it and click "Workset" icon on that pop-up window.

	8. ~~Highlight several of these bins (maybe 5-10)~~.		8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

	9~~. Click on Y to change the state of these to Y values. Click on the “Next >” button. There may be a message regarding exclusion of variables with no variance.~~		9. Choose the Analysis pull-down menu and select Autofit (…or just use the Autofit button on the toolbar).

	~~10. The result, both in terms of the scores plot and the plots showing the difference between PC score points, depends which peaks are assigned as the Y values.~~

	~~11. A new OSC panel will appear.~~
	~~There should be a table with columns labeled No, Angle in Degreees, Remaining SS in % and Eigenvalue.~~

	~~12. Click on the “next component” button. Generally, two components are recommended by Umetrics.~~

	~~13. Click on the Next button.~~

	~~14. Check the destination folder and file name. Click on the “Finish” button.~~

	~~15. Read and close the OSC message box.~~

	~~16. The current model will probably say “PLS <unfitted>” in the Type column. Fit the data using the usual commands for autofit and plot visualization~~

	~~17. Go the Analysis pull-down menu and select Change Model Type.~~

	~~18. From the list, choose OPLS/O2PLS.~~

	19. Choose the Analysis pull-down menu and select Autofit (…or just use the Autofit button on the toolbar).

Bozhang at 05:23, 3 October 2012

2012-10-03T05:23:31Z

Show changes

Bozhang at 04:34, 3 October 2012

2012-10-03T04:34:02Z

Show changes

← Older revision		Revision as of 05:58, 3 October 2012
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	10. Use the “Commands” button (bottom left) to “transpose” the data set.		10. Use the “Commands” button (bottom left) to “transpose” the data set.
	The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.		The rows should now correspond to a given NMR spectrum. Each row is an “observation”. The integral values contained in each row are referred to as “variables”.

	11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”		11. Click on the button at the top of the column to set the first column as the “primary observation ID’s”
	It may already be labeled as primary		It may already be labeled as primary.

	12. Click on the “Observation IDs primary” button		12. Click on the “Observation IDs primary” button
	The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).		The observation IDs should now be color coded with the observation ID primary color. If secondary observation IDs were inserted using Excel, then click on the second column button and then click on “Observation IDs secondary”. Again, the column should be color coded to the correct color (light yellow).

	12. Click on the butoon for the first row		12. Click on the butoon for the first row
	It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.		It may already be labeled as primary and then click on the “Variable IDs primary” button to color code the first row (green). Next, repeat for the second row containing the ppm ranges that define each bucket. These will be the “Variable IDs secondary” and are turquoise. Click “Next” button in the lower-right.

	13. Click the “Finish” button		13. Click the “Finish” button
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	19. View the results, click on the “Create four overview plots” button.		19. View the results, click on the “Create four overview plots” button.
	This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.		This produces the “Score Scatter” plot in the upper-left hand corner. The lower-left hand corner contains the “Loading Scatter” plot.

	20. Expand the score scatter plot for better viewing.		20. Expand the score scatter plot for better viewing

	21. Click on data point		21. Click on data point
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	23. Choose the color tab and choose coloring type by “identifiers”		23. Choose the color tab and choose coloring type by “identifiers”
	The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.		The default then is to color by secondary observation IDs. This uses the labels inserted using Excel. If desired, change the default colors.

	24. Click “Apply”.		24. Click “Apply”

	25. Click “OK”.		25. Click “OK”

	==PLS Analysis==		==PLS Analysis==
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	2. The Excel file is opened in SIMCA as described above for PCA analysis.		2. The Excel file is opened in SIMCA as described above for PCA analysis.

	3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.		3. The opened file should then be transposed. Again, the commands button found in the lower left provides access to this command.

	4. The label information for observations and variables should be processed as described for PCA analysis above.		4. The label information for observations and variables should be processed as described for PCA analysis above.
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	6. The region containing the bucketed intensities is highlighted.		6. The region containing the bucketed intensities is highlighted.
	These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.		These values are labeled as “x-variables” by clicking on the VARIABLE button in the left hand frame and choosing x-variables.

	7. Next, the discriminator values should be set as 1 or 0.		7. Next, the discriminator values should be set as 1 or 0.
	~~Normally, "0" is assigned to wild-type or control group while "1" is assigned to mutant or treatment group.~~

	8. Selected data may still be excluded prior to the statistical analysis.		8. Selected data may still be excluded prior to the statistical analysis.
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	8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".		8. Select all the variables, and use "Par" for scaling. Model type "OPLS/O2PLS".

	9. ~~Choose the Analysis pull-down menu and select Autofit (…or just use the~~ Autofit button on the toolbar).		9. Click Autofit button on the toolbar