Question: test qPCR data for differential expression
gravatar for Andreia Fonseca
6.4 years ago by
Andreia Fonseca810 wrote:
Dear all, I have to test for differential expression some qPCR experiments made for 7 genes. I am using the Delta Cts and I am fitting a linear model to acess if cell type (2 levels) has a significant effect in gene expression. I would like to understand the results that I m getting. When testing the control gene, I am using the Ct values and I am fitting a linear model lm(Ct~cell_type) this gives me a p-value <0.05, meaning that I should refuse the null hypothesis and that is being differentially expressed by cell type, however when fitting the model lm(Ct~0+cell_type), I get what is supposed, p>0.05, so this gene is not affected by cell type, what is what I want. When testing for the target genes, I am getting for the model lm(Ct~0+cell_type) p_value<0.05, what is what I expect since my previous microarray experiment showed this genes are differentially expressed. the model with the intercept is not significant. So these results seem to show that qPCR fits should pass into the origin. Is this true? If it is why is that? Thanks in advance for the help. Kind regards, Andreia ---------------------------------------------------------------------- ------------------------- Andreia J. Amaral, PhD BioFIG - Center for Biodiversity, Functional and Integrative Genomics Instituto de Medicina Molecular University of Lisbon Tel: +352 217500000 (ext. office: 28253) ; [[alternative HTML version deleted]]
qpcr • 741 views
ADD COMMENTlink written 6.4 years ago by Andreia Fonseca810
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 347 users visited in the last hour