The best Matrix choice for one experiment

[MultiD Support]

My question is about what is the best option for “matrix design” taking into account that I am Converting to relative quantities with average.

As I said earlier Smile , my experiment is in plants and I am interested in compare the expression of four genes in leaves and root (tissue= first factor), of three varieties of this plant (varieties = second factor). For each tissue in each variety I have three biological replicates (18 samples)

In this way, what is the best matrix (or matrices) among the follow option:

1. One big matrix of 18 x 5 (5 = genes plus housekeeping). In this way, with this Matrix I will do the data pre-processing (Converting to relative quantities with average)
2. Three matrices for each variety. Each matrix is of 6 x 5 (6 = 2 tissues with three biological replications). Independently each matrix is worked in pre-processing.
3. Six matrices for Tissue and for variety. Each matrix is of 3 x 4 (three biological replicates). Independently each matrix is worked in pre-processing.

With this pre-processed data I want to do:

1. Univariable comparisons: a. Leafs to Roots in each variety (pair experiment), b. Leafs among varieties and c. Roots among varieties.

2. Multivariable comparison among the four genes (if is possible).

Thank you very much for you help.

Best Regards!!!
Morgan

[MultiD Support]

It does not really matter if you pre-process the data as a large matrix or as three smaller once. You get different values on the relative quantities, but as the name indicates, they are relative numbers. The ratio between ant pair stays the same, and therefore the downstream analysis is not affected.

Working with a large matrix saves some time and I would usually do that. But you may initially analyze the smaller matrices because its easier to keep track on the operations. Whatever you chose make sure to use classification columns to index the different factors and also to indicate the sample pairs for paired testing. When you create groups for paired testing in the Data Manager you use two classification columns: one that indexes the groups and one that indexes the pairs.

Its difficult to say if profiling with only four genes will work. It depends on how specific the genes are for the parameters you varied. Sometimes it works very well. It is worth a try.

Note, there are some additional pre-processing options for paired testing in GenEx ver. 4.3.6.

Good luck!