REPEATED_DDCt function performs \(\Delta \Delta C_T\) method
analysis of observations repeatedly taken over different time courses.
Data may be obtained over time from a uni- or multi-factorial experiment. Target genes must be provided as paired
efficiency (E) and Ct columns followed by the E/Ct column pairs of reference genes.
Usage
REPEATED_DDCt(
x,
numOfFactors,
numberOfrefGenes,
repeatedFactor,
calibratorLevel,
block,
p.adj = "none",
plot = FALSE,
analyseAllTarget = TRUE
)Arguments
- x
input data frame in which the first column is
id, followed by the factor column(s) which include at least time. The first level of time in data frame is used as calibrator or reference level. Additional factor(s) may also be present. Other columns are efficiency and Ct values of target and reference genes. In theidcolumn, a unique number is assigned to each individual from which samples have been taken over time, for example seedata_repeated_measure_1, all the three number 1 indicate one individual which has been sampled over three different time courses. See example data sets or refer vignettes, section "Input data structure" for details.- numOfFactors
Integer. Number of experimental factor columns (excluding optional
block).- numberOfrefGenes
Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).
- repeatedFactor
Character string specifying the factor for which fold changes are analysed (commonly
"time").- calibratorLevel
A level of
repeatedFactorto be used as the calibrator (reference level) which is the reference level or sample that all others are compared to. Examples are untreated or time 0.- block
Character or
NULL. Name of the blocking factor column. When a qPCR experiment is done in multiple qPCR plates, variation resulting from the plates may interfere with the actual amount of gene expression. One solution is to conduct each plate as a randomized block so that at least one replicate of each treatment and control is present on a plate. Block effect is usually considered as random and its interaction with any main effect is not considered.- p.adj
Method for p-value adjustment. See
p.adjust.- plot
Logical; if
FALSE, plots are not produced.- analyseAllTarget
Logical or character. If
TRUE(default), all detected target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.
Value
An object containing expression table, lm models, residuals, raw data and ANOVA table for each gene.
- \(\Delta \Delta C_T\) combined expression table
object$Relative_Expression_table- ANOVA table
object$perGene$gene_name$ANOVA_table- lm ANOVA
object$perGene$gene_name$lm- Residuals
resid(object$perGene$gene_name$lm)- log2FC_Plot
object$perGene$gene_name$log2FC_Plot- RE_Plot
object$perGene$gene_name$RE_Plot
Details
Column layout requirements for x:
Target gene columns: E/Ct column pairs located between design and reference columns
Reference gene columns: E/Ct column pairs located at the end
Examples
data1 <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
REPEATED_DDCt(
data1,
numOfFactors = 1,
numberOfrefGenes = 1,
repeatedFactor = "time",
calibratorLevel = "1",
block = NULL)
#> Type III Analysis of Variance Table with Satterthwaite's method
#> Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
#> Time_ 11.073 5.5364 2 4 4.5382 0.09357 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se Lower.se.RE
#> 1 Time_1 1.0000 0.0000 1.0000 0.0000 0.0000 1.4051 0.3776
#> 2 Time_2 vs Time_1 0.8566 -0.2233 0.8166 0.0923 7.9492 0.9753 0.4357
#> 3 Time_3 vs Time_1 4.7022 2.2333 0.0685 . 0.5067 43.6368 0.5541 3.2026
#> Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 2.6483 0.0000 0.0000
#> 2 1.6840 -0.4391 -0.1136
#> 3 6.9040 1.5211 3.2791
#> The level 1 of the selected factor was used as calibrator.
#>
#> Combined Relative Expression Table (all genes)
#> gene contrast RE log2FC pvalue sig LCL UCL se
#> 1 Target Time_1 1.0000 0.0000 1.0000 0.0000 0.0000 1.4051
#> 2 Target Time_2 vs Time_1 0.8566 -0.2233 0.8166 0.0923 7.9492 0.9753
#> 3 Target Time_3 vs Time_1 4.7022 2.2333 0.0685 . 0.5067 43.6368 0.5541
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.3776 2.6483 0.0000 0.0000
#> 2 0.4357 1.6840 -0.4391 -0.1136
#> 3 3.2026 6.9040 1.5211 3.2791
data2 <- read.csv(system.file("extdata", "data_repeated_measure_2.csv", package = "rtpcr"))
REPEATED_DDCt(
data2,
numOfFactors = 2,
numberOfrefGenes = 1,
repeatedFactor = "time",
calibratorLevel = "1",
block = NULL,
p.adj = "none",
plot = FALSE,
analyseAllTarget = TRUE)
#> NOTE: Results may be misleading due to involvement in interactions
#> Type III Analysis of Variance Table with Satterthwaite's method
#> Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
#> treatment 0.6773 0.67728 1 4 0.7987 0.42199
#> Time_ 5.9166 2.95832 2 8 3.4888 0.08139 .
#> treatment:Time_ 6.3186 3.15932 2 8 3.7258 0.07186 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se Lower.se.RE
#> 1 Time_1 1.0000 0.0000 1.0000 0.0000 0.0000 0.7036 0.6140
#> 2 Time_2 vs Time_1 1.2556 0.3283 0.5540 0.4381 3.5987 0.5323 0.8681
#> 3 Time_3 vs Time_1 2.5432 1.3467 0.0351 * 0.8873 7.2895 0.7074 1.5575
#> Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 1.6286 0.0000 0.0000
#> 2 1.8159 0.2270 0.4749
#> 3 4.1528 0.8247 2.1989
#> The level 1 of the selected factor was used as calibrator.
#>
#> Combined Relative Expression Table (all genes)
#> gene contrast RE log2FC pvalue sig LCL UCL se
#> 1 Target Time_1 1.0000 0.0000 1.0000 0.0000 0.0000 0.7036
#> 2 Target Time_2 vs Time_1 1.2556 0.3283 0.5540 0.4381 3.5987 0.5323
#> 3 Target Time_3 vs Time_1 2.5432 1.3467 0.0351 * 0.8873 7.2895 0.7074
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.6140 1.6286 0.0000 0.0000
#> 2 0.8681 1.8159 0.2270 0.4749
#> 3 1.5575 4.1528 0.8247 2.1989