I have results from SDS 2.4 Absolute Quantification from Applied Biosystems 7900 RT PCR assay.
There are Ct values for samples by targets but there is no Efficiency column (or slope or Intercept information) in the data files.
I have two experimental groups, G1 (n=7) and G2 (n=9). Within each experimental group I have pre- and post-treatment samples extracted from same patient. Hence, the pre- and post-samples are paired within each group. So total of 32 samples each ran in triplicates. I do not have a control or calibrator sample. I have multiple (n=4) housekeeping genes and ~30 target genes.
I want to find target genes expression fold change:
(A) between pre- and post- treatment overall regardless of the experimental group.
(B) in pre-treatment between G1 and G2.
(C) in post-treatment between G1 and G2.
(D) Between G1 (post-treatment relative to pre-treatment) and G2 (post-treatment relative to pre-treatment).
Given these are small number of targets and samples, I thought of applying manual deltaCT method with the following steps:
(1) First take an average CT value (with standard error) i.e., mean of the three technical replicates for each gene for each sample
(2) Identify most stable housekeeping genes using existing packages (e.g., geNorm).
(3) Perform normalization using selected stable housekeeping genes from (2). Compute delta CT as Ct (target gene) – Ct (geometric mean of stable hsk genes) for each sample.
(4) Then compute delta delta CT based on the defined comparisons as:
(A) delta Ct (post) – delta Ct (pre) in a pairwise fashion across samples. The pre- and post- are paired samples.
(B) delta Ct (post) – “Avg delta Ct (pre)” within G1 group. Using average delta Ct of G1 pre-treatment samples as reference sample; these are not paired
(C) delta Ct (post) – “Avg delta Ct (pre)” within G2 group, using average delta Ct of G2 pre-treatment samples as reference sample; these are not paired
(D) compute delta Ct (post) – delta Ct (pre) in a pairwise fashion within G1; and
delta Ct (post) – delta Ct (pre) in a pairwise fashion within G2; should I then compute average delta delta Ct value of G1 [delta Ct (post) – delta Ct (pre)] minus average delta delta Ct of G2 (delta Ct (post) – delta Ct (pre))? With this, I just get single delta delta CT value for each target. is this correct?
(5) Compute 2^-delta delta CT values from the delta delta Ct matrix by comparison as listed in (4) above
(6) Compute average 2^-delta delta CT values for testing group and reference group to output fold change.
(7) Use 'delta CT' values to compute statistical pvalue significance for each comparison fold change using 2-sample 2-tailed t-test (except for (A) comparison will do paired t-test). But how to compute significance for comparison (D)?
(8) Adjust p-values using Benjamin-Hochberg FDR.
Is this a reasonable approach? Or do I need a model-based normalization or differential expression analysis here? The comparison (D) I am most confused about as it is sort of a 2 factor design. I would greatly appreciate your help and suggestions.
Thank you so much.