Hi,
In my experiment i did 3 replicates for 2 conditions.(in total 6 samples)
I am interesting in a group of 16 genes that when i checked them in the Genome Browser i found that they have low expression (the number of counts is ~5-20 or even less in general conditions).
In total i have 32,300 genes in my data.
i used rsem to find counts to my data.
The "i" is replicates for the first condition where the i* is the replicates for second condition
name i i* i i* i i*
A 92 119 378 201 116 57
B 2 0 2 1 0 1
C 20 12 28 28 22 16
D 5 3 13 0 2 3
E 0 0 8 4 3 0
F 1 0 1 0 5 0
G 1 0 1 0 5 0
H 1 1 0 0 1 0
I 341 655 939 470 529 389
J 341 655 939 470 529 389
K 9 12 13 9 10 5
L 1003 1268 2729 1039 1196 929
M 2 2 16 8 2 4
N 3 9 20 3 6 5
As we can see by eyes, there is no a strict rule(at list i didnt found one..)
When i used DESeq to find differential expression between the 2 condition, i had a table of all the genes of my data.
At the end, i was looking for significant in those genes , but i didnt find.
i used rsem as:
rsem-calculate-expression -p 20 --paired-end R1_001.fastq R2_001.fastq reference out &
i used DESeq as:
conditions <- c("1","2","1","2","1","2")
cds <- newCountDataSet(x,conditions)
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds,method="per-condition",sharingMode="maximum",fitType="local")
res <- nbinomTest(cds,condA="1",condB="2")
i am wondering if i know that i am interesting in genes with low counts, do i have to do something different in my commands? (both rsem & DESeq)
i am also know that the different between the condition should be around X 2 in the expression of the genes for most of our genes of interest. (we did some wet tests with pcr )
I will really appreciate any recommend
Thanks,
Pap
In my experiment i did 3 replicates for 2 conditions.(in total 6 samples)
I am interesting in a group of 16 genes that when i checked them in the Genome Browser i found that they have low expression (the number of counts is ~5-20 or even less in general conditions).
In total i have 32,300 genes in my data.
i used rsem to find counts to my data.
The "i" is replicates for the first condition where the i* is the replicates for second condition
name i i* i i* i i*
A 92 119 378 201 116 57
B 2 0 2 1 0 1
C 20 12 28 28 22 16
D 5 3 13 0 2 3
E 0 0 8 4 3 0
F 1 0 1 0 5 0
G 1 0 1 0 5 0
H 1 1 0 0 1 0
I 341 655 939 470 529 389
J 341 655 939 470 529 389
K 9 12 13 9 10 5
L 1003 1268 2729 1039 1196 929
M 2 2 16 8 2 4
N 3 9 20 3 6 5
As we can see by eyes, there is no a strict rule(at list i didnt found one..)
When i used DESeq to find differential expression between the 2 condition, i had a table of all the genes of my data.
At the end, i was looking for significant in those genes , but i didnt find.
i used rsem as:
rsem-calculate-expression -p 20 --paired-end R1_001.fastq R2_001.fastq reference out &
i used DESeq as:
conditions <- c("1","2","1","2","1","2")
cds <- newCountDataSet(x,conditions)
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds,method="per-condition",sharingMode="maximum",fitType="local")
res <- nbinomTest(cds,condA="1",condB="2")
i am wondering if i know that i am interesting in genes with low counts, do i have to do something different in my commands? (both rsem & DESeq)
i am also know that the different between the condition should be around X 2 in the expression of the genes for most of our genes of interest. (we did some wet tests with pcr )
I will really appreciate any recommend
Thanks,
Pap