M: fold-change differences
D: absolute expression differences
(M,D) pair for each gene is evaluated based on a null distribution estimated from technical or biological replicates or simulations in 2011GR.
In NIOSEQBIO, theta=(M+D)/2 seems to be the statistic used for null distribution based on my understanding of its manual.
Probability = 0.8 was the cutoff for differentially expressed genes in 2011GR.
Probability = 0.95 (FDR) is recommended for biologically replicated samples.
NOISEQBIO is optimized for biological replicates.
When using noiseq and noiseqbio, normalization and filtering can be done through parameters, 'norm'.
nss = 5, v = 0.02, lc = 1, replicates = "technical")
R1L1Kidney Kidney Kidney_1
R1L2Liver Liver Liver_1
R1L3Kidney Kidney Kidney_1
R1L4Liver Liver Liver_1
R1L6Liver Liver Liver_1
R1L7Kidney Kidney Kidney_1
R1L8Liver Liver Liver_1
R2L2Kidney Kidney Kidney_2
R2L3Liver Liver Liver_2
R2L6Kidney Kidney Kidney_2
mynoiseqbio = noiseqbio(mydata, k = 0.5, norm = "rpkm", factor="Tissue", lc = 1, r = 20, adj = 1.5, plot = FALSE, a0per = 0.9, random.seed = 12345, filter = 2)
Authors stated that noiseq output prob are not equivalent to p-values?
Q: what are "up" and "down" deg referenced to?
mynoiseq.deg1 = degenes(mynoiseq, q = 0.8, M = "up")