Given two conditions (control and treated) with 4 replicates. How can I get genes with FPKM for each replicate?
Control Replicates: C1, C2, C3, C4
Treated Replicate: T1, T2, T3, T4
For example, for a gene TP53 I need the following
TP53 FPKM(c1), FPKM(c2),FPKM(c3),FPKM(c4), FPKM(t1),FPKM(t2),FPKM(t3),FPKM(t4)
I tried the following command cuffdiff -o diff_out -b genome.fa -p 8 -L CR,TR -u merged_asm/merged.gtf CR_R1_thout/accepted_hits.bam,CR_R2_thout/accepted_hits.bam,CR_R3_thout/accepted_hits.bam,CR_R4_thout/accepted_hits.bam TR_R1_thout/accepted_hits.bam,TR_R2_thout/accepted_hits.bam,TR_R3_thout/accepted_hits.bam,TR_R4_thout/accepted_hits.bam
But, I got combined FPKM for two conditions not for replicates
How can I get FPKM for each replicates?
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Karma
Hi, please note that cuffdiff is used to identify the dfferent expression genes based on the individual sample FPKM and the group informations. So if you means to get the individual sample FPKM value, just simple use cufflinks instead. for example :
cufflinks -g yourGTF -u -o output_name individual.bam
Though cuffdiff is for differential expression, it generates the FPKM/RPKM values
So, if I use this, I am going to get fpkm/rpkm from each replicate. If I create a matrix of fpkm values from replicates and calculate fold change it should be equal to the fold change generated by the command cuffdiff -o diff_out -b genome.fa -p 8 -L CR,TR -u merged_asm/merged.gtf CR_R1_thout/accepted_hits.bam,CR_R2_thout/accepted_hits.bam,CR_R3_thout/accepted_hits.bam,CR_R4_thout/accepted_hits.bam TR_R1_thout/accepted_hits.bam,TR_R2_thout/accepted_hits.bam,TR_R3_thout/accepted_hits.bam,TR_R4_thout/accepted_hits.bam
Right?
Yes, I think so. Meanwhile, if you have the individual sample expression value, you could apply another method or statistics model to identify DE genes. Also you should refer to the manual : cufflinks package manual
If you inspect all the files, there will files with rpkm for each replicate.
From cuffdiff website:
Cuffdiff calculates the expression and fragment count for each transcript, primary transcript, and gene in each replicate. The results are output in per-replicate tracking files in the format described here.
isoforms.read_group_trackinggenes.read_group_trackingcds.read_group_trackingtss_groups.read_group_tracking
Otherwise,
Quantify the genes using featureCounts and feed the matrix to edgeR's and use rpkm() function. featureCounts output contains the gene length information in one of the columns, which edgeR needs.
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