I'm working on a dataset which is giving me trouble. I have 200 samples (Sample 1, Sample 2...). An example dataset (limited to 15 samples) is here. My dataset is structured with the following columns (I'll try to simplify):

1. Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.
2. Sequencing technique. 4 categories: A,B,C,D (non numeric values)
3. Salinity. 3 categories: salt, brackish, freshwater
4. Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.
5. (through 10) Abundance of phyla, expressed as % of the considered domain.

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity, etc.) and the abundance of the phyla.

Summarizing, the question is: "What type of analysis can be useful to see the relation between Phyla abundance and abiotic variables like salinity and depth (see dataset example at link) ?"

I'm working on a dataset which is giving me trouble. I have 200 samples (Sample 1, Sample 2...). An example dataset (limited to 15 samples) is here. My dataset is structured with the following columns (I'll try to simplify):

1. Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.
2. Sequencing technique. 4 categories: A,B,C,D (non numeric values)
3. Salinity. 3 categories: salt, brackish, freshwater
4. Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.
5. (through 10) Abundance of phyla, expressed as % of the considered domain.

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity, etc.) and the abundance of the phyla.

What type of analysis can be useful to see the relation between Phyla abundance and abiotic variables like salinity and depth (see dataset example at link)?

I'm working on a dataset which is giving me trouble. I have 200 samples (Sample 1, Sample 2...). An example dataset (limited to 15 samples) is here. My dataset is structured with the following columns (I'll try to simplify):

1. Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.
2. Sequencing technique. 4 categories: A,B,C,D (non numeric values)
3. Salinity. 3 categories: salt, brackish, freshwater
4. Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.
5. (through 10) Abundance of phyla, expressed as % of the considered domain.

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity, etc.) and the abundance of the phyla.

Use of Excel is preferable, I own the license for XLSTAT extension also. If it's not possible with Excel I can write some appropriate R code.

I'm working on a dataset which is giving me trouble. I have 200 samples (Sample 1, Sample 2...). An example dataset (limited to 15 samples) is here. My dataset is structured with the following columns (I'll try to simplify):

1. Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.
2. Sequencing technique. 4 categories: A,B,C,D (non numeric values)
3. Salinity. 3 categories: salt, brackish, freshwater
4. Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.
5. (through 10) Abundance of phyla, expressed as % of the considered domain.

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity, etc.) and the abundance of the phyla.

Summarizing, the question is: "What type of analysis can be useful to see the relation between Phyla abundance and abiotic variables like salinity and depth (see dataset example at link) ?"

First of all sorry for any spelling/vocabulary mistake, I'm not a native speaker but i'll try to be as clear as possible.

I'm working on a dataset which is giving me trouble. It is structured like this (i'll try to simplify):

1st column: Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.

2nd column: Sequencing technique. 4 categories: A,B,C,D (non numeric values)

3rd column: Salinity. 3 categories: salt, brackish, freshwater

4th column: Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.

5th-10th columns: Abundance of phyla, expressed as % of the considered domain.

I have 200 samples (Sample 1, Sample 2...)

So dataset is like this (i limited the example to 15 samples)

 https://www.dropbox.com/s/8m3tfv78nh18k83/Dataset%20example%20for%20stackoverflow.xlsx?dl=0

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity etc.) and the abundance of the phyla.

Use of excel is preferable, I own the license for XLSTAT extension also. If it's not possible with excel I can write some appropriate R code

ANY help will be really appreciated, sorry if the question is trivial

Thank you in advance

I'm working on a dataset which is giving me trouble. I have 200 samples (Sample 1, Sample 2...). An example dataset (limited to 15 samples) is here. My dataset is structured with the following columns (I'll try to simplify):

1. Sample source. Type of data: dichotomic. It can be SEDIMENT or WATER.
2. Sequencing technique. 4 categories: A,B,C,D (non numeric values)
3. Salinity. 3 categories: salt, brackish, freshwater
4. Depth. 5 categories: surface, epi, meso, bathy and abyssopelagic.
5. (through 10) Abundance of phyla, expressed as % of the considered domain.

In my opinion a PCA and a MDS are not useful for this kind of data. I'm trying to evaluate the relation between the abiotic variables (salinity, etc.) and the abundance of the phyla.

Use of Excel is preferable, I own the license for XLSTAT extension also. If it's not possible with Excel I can write some appropriate R code.