Hi,
I have one doubt. In my project, i have reads from 2 conditions (Control/Infected) for leaf and root. I am using Cuffdiff to normalize the data and make differential gene expression, but i saw something.
I testes my datas in two different forms:
1. In Cuffdiff's parameters i put 4 conditions ( LeafControl, LeafInfected, RootControl and RootInfected), each condition has 3 replicates, and the Cuffdiff show 350 differentialy expressed genes for LeafControl and LeafInfected.
2. In Cuffdiff's parameters i put 2 conditions ( LeafControl and LeafInfected), each condition has 3 replicates, and the Cuffdiff show 101 differentialy expressed genes.
Why? I think that is the way that him normalizes. In first form the normalization includes all conditions, including Leaf and Root replicates, and the second use only one condition.
In my opinion, if you want to see the DE between one condition (Leaf or. Root), you have to normalize the read mapping coming from Cufflinks separadaly.
Somente could help me and say why i have different values for the same thing? What is the right value, 350 or 101? I don't if it helps, but the results have 98 genes in common.
I have one doubt. In my project, i have reads from 2 conditions (Control/Infected) for leaf and root. I am using Cuffdiff to normalize the data and make differential gene expression, but i saw something.
I testes my datas in two different forms:
1. In Cuffdiff's parameters i put 4 conditions ( LeafControl, LeafInfected, RootControl and RootInfected), each condition has 3 replicates, and the Cuffdiff show 350 differentialy expressed genes for LeafControl and LeafInfected.
2. In Cuffdiff's parameters i put 2 conditions ( LeafControl and LeafInfected), each condition has 3 replicates, and the Cuffdiff show 101 differentialy expressed genes.
Why? I think that is the way that him normalizes. In first form the normalization includes all conditions, including Leaf and Root replicates, and the second use only one condition.
In my opinion, if you want to see the DE between one condition (Leaf or. Root), you have to normalize the read mapping coming from Cufflinks separadaly.
Somente could help me and say why i have different values for the same thing? What is the right value, 350 or 101? I don't if it helps, but the results have 98 genes in common.
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