FastQPCR: Fast way to deal with results of qRT-PCR

In shijianasdf/BasicBioinformaticsAnalysisFromZhongShan: Launch Usual Clinical tool for Kind Yield

Description

Fast way to deal with results of qRT-PCR,including compared plot,statistics and quality control.

Usage

FastQPCR(data, sample = "samples", marker = "markers",
  bioRepeat = "biorepeat", parallelRepeat = "parepeat",
  group = "groups", group.control = "control", internal = "GAPDH",
  value = "Ct", plot.type = c(1, 2)[1], palette = NULL, size = 20,
  label.position = c(4, 5), label.type = c("p.signif", "p.format")[1],
  method = "t.test", x.title = "Genes",
  y.title = "The Relative Expression of Genes",
  legend.position = "top")

Arguments

data	the result of qRT-PCR.
sample	the colnames of sample.Note that different samples should use different sample id.
marker	the colnames of marker
bioRepeat	the colnames of bioRepeat.The data in "bioRepeat" would be considered as available repeat in afterward statistics.
parallelRepeat	the colnames of parallelRepeat.The data in "parallelRepeat" would be treated via mean strategy.
group	the colnames of group.Like "treatment" and "control".
group.control	the names of control group.Default is "control".
internal	the name of internal reference gene.Default is "GAPDH".
value	the colnames of Ct value
plot.type	one of 1 and 2.1 is used in multiple markers,but 2 is more suitable for one marker.
palette	the color of groups.The number of color must be equal to the number of groups.Default is NULL,which mean that the strategy of lucky package would be use.
size	ggplot parameters.the size of plot
label.position	ggplot parameters.the position of p significance
label.type	character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value).
method	method use in statistics."t.test" is always recommended.You can also use "wilcox.test" for a try.Alternative choices are "anova" and "kruskal.test".
x.title	ggplot parameters.the x axis title
y.title	ggplot parameters.the y axis title
legend.position	ggplot parameters.legend position

Value

a Lucky Objects

Author(s)

Weibin Huang<[e-mail]>

See Also

stat_compare_means

Examples

library(lucky)
data("qpcr",package = "lucky") # see the example of qpcr result
result.pcr <- FastQPCR(qpcr)
result.pcr <- FastQPCR(data=qpcr,
                       sample = "samples",
                       marker = "markers",
                       bioRepeat = "biorepeat",
                       parallelRepeat = "parepeat",
                       group = "groups",
                       group.control = "control",
                       internal = "GAPDH",
                       value = "Ct",
                       size=20,
                       label.y = 650,
                       method = "t.test",
                       x.title = "Genes",
                       y.title = "The Relative Expression of Genes",
                       legend.position = "top")
## Use t.test.However,it's not recommand.
result.pcr <- FastQPCR(data=qpcr,
                       sample = "samples",
                       marker = "markers",
                       bioRepeat = "biorepeat",
                       parallelRepeat = "parepeat",
                       group = "groups",
                       group.control = "control",
                       internal = "GAPDH",
                       value = "Ct",
                       size=20,
                       label.y = 650,
                       method = "t.test", #t.test
                       x.title = "Genes",
                       y.title = "The Relative Expression of Genes",
                       legend.position = "top")

## View result
View(result_qpcr$Data$statistc$whole)
View(result_qpcr$Data$statistc$pair)

## test
a = result.pcr$Data$metadata
x <- a$fc[a$markers %in% "marker1" & a$groups %in% "control"]
y <- a$fc[a$markers %in% "marker1" & a$groups %in% "treat"]
t.test(x,y)

shijianasdf/BasicBioinformaticsAnalysisFromZhongShan documentation built on Jan. 3, 2020, 10:08 p.m.