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Apr 18, 2019 8:23 AM
(6085 views)

How could I correlate delta CT values and biomass in grams or seed count in arithmetric numbers.

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Created:
Apr 20, 2019 7:56 AM
| Last Modified: Apr 20, 2019 7:59 AM
(6011 views)
| Posted in reply to message from JLE 04-19-2019

The data is in an Excel workbook. Do you use JMP?

I used multiple regression to assess the relationship between shoot dry weight, root dry weight, seed weight, Myc, 4End, and Bs with the responses Agp shoot CT and Agp root CT separately. I included quadratic terms for the quantitative factors because my initial analysis indicated a non-linearity. I did not include any terms for potential interaction effects.

The Agp Shoot CT shows the strongest effects. The factor Myc is probably not significant. The Profiler is the easiest way to interpret the model.

There is only a weak relationship for the Agp Root CT. There appears to be twice as much variation in this response compared to the first response. The additional variation obscures any effects, if there any. Does it make sense that the response would exhibit a minimum near the middle shoot dry weight?

My observations are:

- This data set is small so there is low power for detecting active effects.
- The experiment varied 6 factors over 10 runs, so many effects are confounded.
- Either the Myc, 4End, and Bs factor levels were recorded incorrectly or this experiment was not designed using statistical principles. There are unnecessary correlations among the factors that reduce the efficiency of the data for estimation.

Learn it once, use it forever!

8 REPLIES 8

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Re: Correlation of Different Data Types

Can you show even a few observations (rows) of data? Hard to picture your problem.

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Re: Correlation of Different Data Types

Few rows of data attached.

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Re: Correlation of Different Data Types

You reply contains no example in any form.

Also, please explain the nature of the variables, delta CT values and biomass in grams or seed count, and the intent of your analysis of correlation.

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Re: Correlation of Different Data Types

I attached an excel spreadsheet that had my data in columns. The CT value is an a.n. or unit received from qPCR results that is used to measure the gene expression in a sample of plant tissue. For example the higher the CT value the more copies of the gene and the higher the expression. What I was asked is there any correlation between the gene expression of the plant tissue and the biomass of the plant at the given treatments. So does Ox+Myc+4End+Bs biomass of the shoot, root, seed wt. and seed count correlate with Agp CT value of the gene expressed by Ox+Myc+4End+Bs and so forth. Someone suggested the R squared values is what I would look at but that does not make sense to me.

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Re: Correlation of Different Data Types

I still do not see any attachment to any reply in this discussion thread.

Learn it once, use it forever!

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I attached an excel spreadsheet that had my data in columns. The CT value is an a.n. or unit received from qPCR results that is used to measure the gene expression in a sample of plant tissue. For example the higher the CT value the more copies of the gene and the higher the expression. What I was asked is there any correlation between the gene expression of the plant tissue and the biomass of the plant at the given treatments. So does Ox+Myc+4End+Bs biomass of the shoot, root, seed wt. and seed count correlate with Agp CT value of the gene expressed by Ox+Myc+4End+Bs and so forth. Someone suggested the R squared values is what I would look at but that does not make sense to me.

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Re: Correlation of Different Data Types

Ok, lets try this again:

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Created:
Apr 20, 2019 7:56 AM
| Last Modified: Apr 20, 2019 7:59 AM
(6012 views)
| Posted in reply to message from JLE 04-19-2019

The data is in an Excel workbook. Do you use JMP?

I used multiple regression to assess the relationship between shoot dry weight, root dry weight, seed weight, Myc, 4End, and Bs with the responses Agp shoot CT and Agp root CT separately. I included quadratic terms for the quantitative factors because my initial analysis indicated a non-linearity. I did not include any terms for potential interaction effects.

The Agp Shoot CT shows the strongest effects. The factor Myc is probably not significant. The Profiler is the easiest way to interpret the model.

There is only a weak relationship for the Agp Root CT. There appears to be twice as much variation in this response compared to the first response. The additional variation obscures any effects, if there any. Does it make sense that the response would exhibit a minimum near the middle shoot dry weight?

My observations are:

- This data set is small so there is low power for detecting active effects.
- The experiment varied 6 factors over 10 runs, so many effects are confounded.
- Either the Myc, 4End, and Bs factor levels were recorded incorrectly or this experiment was not designed using statistical principles. There are unnecessary correlations among the factors that reduce the efficiency of the data for estimation.

Learn it once, use it forever!

Highlighted
##

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Re: Correlation of Different Data Types

Yes, I do use JMP.

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