Entering edit mode
Dear All,
I noticed some confusing meanTarget/meanCalibrator relation to Limma decide test (-1/0/1) in HTqPCR package.
1. I executed "example preprocessed data" from "qPCR analysis in R" vignette - code below.
I extracted whole DE table and "summary" table. I've created the table that refers only to "LongStarve -
Control" comparison [only significant genes]. Please see chunk of resulting table. The values were rounded for clarity.
| feature.pos | t.test | p.value | adj.p.value | ddCt | FC | meanTarget | meanCalibrator | LongStarve - Control |
| M13;N13 | -4.39 | 0.00 | 0.02 | -2.18 | 4.54 | 27.44 | 29.62 | -1 |
| O9;P9 | -6.61 | 0.00 | 0.00 | -3.93 | 15.20 | 23.89 | 27.82 | -1 |
| A8;B8 | -5.28 | 0.00 | 0.01 | -5.80 | 55.58 | 26.36 | 32.16 | -1 |
| G21;H21 | -7.03 | 0.00 | 0.00 | -5.30 | 39.47 | 28.44 | 33.74 | -1 |
| A12;B12 | 6.62 | 0.00 | 0.00 | 3.63 | 0.08 | 24.45 | 20.82 | 1 |
| I23;J23 | 6.36 | 0.00 | 0.00 | 4.33 | 0.05 | 31.13 | 26.80 | 1 |
| K6;L6 | 5.16 | 0.00 | 0.01 | 2.67 | 0.16 | 31.08 | 28.42 | 1 |
| K23;L23 | 4.86 | 0.00 | 0.01 | 5.62 | 0.02 | 31.49 | 25.87 | 1 |
| O17;P17 | 4.97 | 0.00 | 0.01 | 2.69 | 0.15 | 34.05 | 31.36 | 1 |
| C9;D9 | 5.37 | 0.00 | 0.01 | 3.47 | 0.09 | 29.13 | 25.67 | 1 |
| C22;D22 | 5.84 | 0.00 | 0.01 | 2.09 | 0.24 | 30.48 | 28.40 | 1 |
| G6;H6 | 6.57 | 0.00 | 0.00 | 4.98 | 0.03 | 33.99 | 29.01 | 1 |
If the gene in "meanTarget" has lower mean Ct value than in "meanCalibrator" why its marked -1 (down-regulation)?.
Shouldn't be marked as 1 [the lower Ct the higher expression]?
Thank you for your help.
Best,
Pawel
library(HTqPCR)
# Load example preprocessed data
data(qPCRpros)
samples <- read.delim(file.path(system.file("exData",package="HTqPCR"), "files.txt"))
# Define design and contrasts
design <- model.matrix(~0+samples$Treatment)
colnames(design) <- c("Control", "LongStarve","Starve")
contrasts <- makeContrasts(LongStarve-Control, LongStarve-Starve,
Starve-Control, levels=design)
# The actual test
diff.exp <- limmaCtData(qPCRpros, design=design, contrasts=contrasts)
# Some of the results
diff.exp[["LongStarve - Control"]][1:10,]
# Example with duplicate genes on card.
# Reorder data to get the genes in consecutive rows
temp <- qPCRpros[order(featureNames(qPCRpros)),]
diff.exp <- limmaCtData(temp, design=design, contrasts=contrasts,ndups=2, spacing=1)
# Some of the results
names(diff.exp)
diff.exp[["LongStarve - Control"]]
diff.exp[["Summary"]]
