LFQDataStats: Decorates LFQData with methods to compute statistics of...

In wolski/prolfqua: Proteomics Label Free Quantification

Decorates LFQData with methods to compute statistics of interactions

Description

Decorates LFQData with methods to compute statistics of interactions

Decorates LFQData with methods to compute statistics of interactions

Details

compute stdv, mean and CV per peptide or protein and condition.

Public fields

lfq

LFQData

stat

either CV or sd (if is_transformed)

statsdf

frame with statistics.

Methods

Public methods

• LFQDataStats$new()

• LFQDataStats$stats()

• LFQDataStats$stats_wide()

• LFQDataStats$stats_quantiles()

• LFQDataStats$density()

• LFQDataStats$density_median()

• LFQDataStats$violin()

• LFQDataStats$violin_median()

• LFQDataStats$stdv_vs_mean()

• LFQDataStats$power_t_test_quantiles()

• LFQDataStats$power_t_test()

• LFQDataStats$clone()

---

Method new()

create analyse variances and CV

Usage

LFQDataStats$new(lfqdata, stats = c("everything", "interaction", "all"))

Arguments

lfqdata

LFQData object

stats

if interaction - within group stats, if all then overall CV, if pooled - then pooled variance using grouping information (t.b.d.)

---

Method stats()

access data.frame with statistics

Usage

LFQDataStats$stats()

Returns

data.frame with computed statistics

---

Method stats_wide()

access data.frame with statistics in wide format

Usage

LFQDataStats$stats_wide()

Returns

data.frame with computed statistics in wide format

---

Method stats_quantiles()

Determine CV or sd for the quantiles

Usage

LFQDataStats$stats_quantiles(probs = c(0.1, 0.25, 0.5, 0.75, 0.9))

Arguments

probs

for which quantile to determine CV or sd

---

Method density()

plots density or ecdf

Usage

LFQDataStats$density(ggstat = c("density", "ecdf"))

Arguments

ggstat

either density or ecdf

Returns

ggplot

---

Method density_median()

plot density or ecdf of CV or sd for the 50

Usage

LFQDataStats$density_median(ggstat = c("density", "ecdf"))

Arguments

ggstat

either density of ecdf

Returns

ggplot

---

Method violin()

plot violinplot of CV or sd

Usage

LFQDataStats$violin()

Arguments

ggstat

either density of ecdf

Returns

ggplot

---

Method violin_median()

plot violinplot of CV or sd for the 50

Usage

LFQDataStats$violin_median()

Returns

ggplot

---

Method stdv_vs_mean()

plot sd vs mean

Usage

LFQDataStats$stdv_vs_mean(size = 200)

Arguments

size

number of points to sample (default 200)

Returns

ggplot

---

Method power_t_test_quantiles()

compute sample size for entire dataset

Usage

LFQDataStats$power_t_test_quantiles(
  probs = c(0.1, 0.25, 0.5, 0.75, 0.9),
  delta = c(0.59, 1, 2),
  power = 0.8,
  sig.level = 0.05
)

Arguments

probs

quantiles of sd for which sample size should be computed

delta

effect size

power

power of test

sig.level

significance level.

---

Method power_t_test()

compute sample for each protein

Usage

LFQDataStats$power_t_test(delta = c(0.59, 1, 2), power = 0.8, sig.level = 0.05)

Arguments

delta

effect size

power

power of test

sig.level

significance level.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LFQDataStats$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other LFQData: LFQData, LFQDataAggregator, LFQDataPlotter, LFQDataSummariser, LFQDataToSummarizedExperiment(), LFQDataWriter, ProteinAnnotation

Examples

# study variance of not normalized data
#source("c:/Users/wewol/prog/prolfqua/R/LFQData.R")
runallfuncs <- function(x){

  stopifnot("data.frame" %in% class(x$stats()))
  stopifnot("data.frame" %in% class(x$stats_wide()))
  stopifnot(c("long", "wide") %in% names(x$stats_quantiles()))
  stopifnot("ggplot" %in% class(x$density()))
  stopifnot("ggplot" %in% class(x$density_median()))
  stopifnot("ggplot" %in% class(x$density("ecdf")))
  stopifnot("ggplot" %in% class(x$density_median("ecdf")))
  stopifnot("ggplot" %in% class(x$violin()))
  stopifnot("ggplot" %in% class(x$violin_median()))
  stopifnot("ggplot" %in% class(x$stdv_vs_mean(size = 400)))
  if(!x$lfq$is_transformed()){
    stopifnot(is.null(x$power_t_test()))
    stopifnot(is.null(x$power_t_test_quantiles()))
  }
}
bb <- prolfqua::sim_lfq_data_peptide_config()
istar$config <- bb$config
data <-bb$data
lfqdata <- LFQData$new(data, istar$config)
lfqstats <- lfqdata$get_Stats()
stopifnot(ncol(lfqstats$stats_wide()) == 30)
lfqstats$violin()
runallfuncs(lfqstats)
x <- lfqstats

#study variance of normalized data


lfqdata <- LFQData$new(data, istar$config)
lfqdata$is_transformed(TRUE)
lfqstats <- lfqdata$get_Stats()
stopifnot(ncol(lfqstats$stats_wide()) == 26)
runallfuncs(lfqstats)

#Slightly different dataset


# estimates statistics for all samples
lfqstats <- lfqdata$get_Stats(stats = "all")
stopifnot(ncol(lfqstats$stats_wide()) == 8)
runallfuncs(lfqstats)
lfqstats <- lfqdata$get_Stats(stats = "interaction")
stopifnot(ncol(lfqstats$stats_wide()) == 20)
runallfuncs(lfqstats)

wolski/prolfqua documentation built on May 12, 2024, 10:16 p.m.