Hi all,


I am new with RNA-seq data analysis. I have data from illumina DGE 
experiments from 8 different cultivars (duplicates). I also have 4 extra 
samples, the F1 hybrids (duplicates), obtained from crossing the 8 
cultivars as follows (1x2, 3x4, 5x6, 7x8). In total I have 24 DGE 
illumina reactions, where all the samples came from the same 
developmental stage and treatment. So the only different factor I have 
is the genotype.

I am trying to analyze the data with the edgeR package, however I am not 
sure if I can use the GML function for multiple testings or how should I 
made my design matrix.

To find the differences among the 8 parental lines, I know I can made 
two ways testing between the different samples, but I was wondering, if 
I could be able to do a test were the null hypothesis will be there is 
no difference between the cultivars, and the alternative hypothesis, at 
least there is difference for one cultivar.

To do that, it is possible to use one design matrix like these:



Cultivar1 Cultivar2 Cultivar3 cultivar4 Cultivar5 Cultivar6 Cultivar7 
Cultivar8

1-rep1 1 0 0 0 0 0 0 0

1-rep2 1 0 0 0 0 0 0 0

2-rep1 0 1 0 0 0 0 0 0

2-rep2 0 1 0 0 0 0 0 0

3-rep1 0 0 1 0 0 0 0 0

3-rep2 0 0 1 0 0 0 0 0

4-rep1 0 0 0 1 0 0 0 0

4-rep2 0 0 0 1 0 0 0 0

5-rep1 0 0 0 0 1 0 0 0

5-rep2 0 0 0 0 1 0 0 0

6-rep1 0 0 0 0 0 1 0 0

6-rep2 0 0 0 0 0 1 0 0

7-rep1 0 0 0 0 0 0 1 0

7-rep2 0 0 0 0 0 0 1 0

8-rep1 0 0 0 0 0 0 0 1

8-rep2 0 0 0 0 0 0 0 1


I have try it, but to calculate the null hypothesis the gmlLRT seams 
just to remove the last column??


Besides I also want to test the differences between two parent lines and 
the F1 that I have derived from them I have try a design matrix as follows,


Cultivar1 Cultivar2 F1

1-rep1 1 0 0

1-rep2 1 0 0

2-rep1 0 1 0

2-rep2 0 1 0

F1-rep1 0 0 1

F1-rep2 0 0 1


But I again face the above mentioned problem.

Besides I was wondering if I can employ a design matrix where I will be 
modeling how many alleles of each cultivar have my sample, doing 
something like these:


Cultivar1 Cultivar2

1-rep1 2 0

1-rep2 2 0

2-rep1 0 2

2-rep2 0 2

F1-rep1 0 1

F1-rep2 0 1


I will appreciate any advice about the proper way to face these analysis.


An just an extra question, in the manual it is recommended to eliminate 
all the tags with low counts, as they are no going to be sadistically 
significance and they will slow the process. First it is said that the 
tags with less than 5 counts are not useful, but then all the tags with 
lees than one tag per million (TPM) are removed, however in my case 
having 50 million sequences that is all the tags with less than 50 counts.

In fact when I do it my tags decrease a lot


 > dim(d)

[1] 2029802 16


 > d <-d[rowSums(1e+06 * d$counts/expandAsMatrix(d$samples$lib.size, 
dim(d)) >1) >= 2, ]


 > dim (d)

[1] 73310 16


That is normal? Why did you choose to remove less than 1TPM?



Thanks in advance



Laura Pascual


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