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Decorates LFQData with methods to compute statistics of interactions

Source: R/LFQDataStats.R
LFQDataStats.Rd

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

Value

An R6 class generator.

See also

Other LFQData: AggregateLimpa, AggregateMedpolish, AggregateRlm, AggregateTopN, AggregatorBase, LFQData, LFQDataPlotter, LFQDataSummariser, LFQDataToSummarizedExperiment()

Public fields

lfq

LFQData

stat

either CV or sd (if is_transformed)

statsdf

frame with statistics.

Methods

Public methods

LFQDataStats$new()

create analyse variances and CV

Usage

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

Arguments

lfqdata

LFQData object

stats

if `interaction`, compute within-group stats; if `all`, compute overall CV; if `pooled`, use pooled variance with grouping information

LFQDataStats$stats()

access data.frame with statistics

Usage

LFQDataStats$stats()

Returns

data.frame with computed statistics

LFQDataStats$stats_wide()

access data.frame with statistics in wide format

Usage

LFQDataStats$stats_wide()

Returns

data.frame with computed statistics in wide format

LFQDataStats$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

LFQDataStats$density()

plots density or ecdf

Usage

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

Arguments

ggstat

either density or ecdf

Returns

ggplot

LFQDataStats$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

LFQDataStats$violin()

plot violinplot of CV or sd

Usage

LFQDataStats$violin()

Returns

ggplot

LFQDataStats$violin_median()

plot violinplot of CV or sd for the 50

Usage

LFQDataStats$violin_median()

Returns

ggplot

LFQDataStats$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

LFQDataStats$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.

LFQDataStats$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.

LFQDataStats$clone()

The objects of this class are cloneable with this method.

Usage

LFQDataStats$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done



lfqdata <- LFQData$new(bb$data, bb$config)
lfqstats <- lfqdata$get_Stats()
stopifnot(ncol(lfqstats$stats_wide()) == 30)
lfqstats$violin()
#> Warning: Removed 2 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 2 rows containing non-finite outside the scale range
#> (`stat_summary()`).

runallfuncs(lfqstats)
#> Warning: data is not transformed - aborting
#> Warning: data is not transformed - aborting
x <- lfqstats

#study variance of normalized data


lfqdata <- LFQData$new(bb$data, bb$config)
lfqdata$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)

# Group size 1
bb <- prolfqua::sim_lfq_data_peptide_config(N=1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfq1 <- LFQData$new(bb$data, bb$config)
table_factors_size(lfq1$data_long(), lfq1$file_name(),
  lfq1$sample_name(), lfq1$factor_keys(), lfq1$relevant_factor_keys())
#> # A tibble: 3 × 2
#>   group_     n
#>   <chr>  <int>
#> 1 A          1
#> 2 B          1
#> 3 Ctrl       1
lfqdata <- LFQData$new(bb$data, bb$config)
lfqstats <- lfqdata$get_Stats()

# stopifnot(ncol(lfqstats$stats_wide()) == 30)
lfqstats$violin()
#> Warning: Removed 2 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: Removed 2 rows containing non-finite outside the scale range
#> (`stat_summary()`).

runallfuncs(lfqstats)
#> Warning: data is not transformed - aborting
#> Warning: data is not transformed - aborting