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compute group mean by LOD

Source: R/tidyMS_missigness_V2.R
MissingHelpers.Rd

compute group mean by LOD

compute group mean by LOD

Details

weight lod by nr of NA's $(LOD * nrNas + meanAbundance *nrObs)/(nrMeasured)$

Public fields

data

data

config

config

prob

quantile of groups with one observed value to estimate LOD

stats

data.frame with group statistics

weighted

should we weight the LOD

Methods

Public methods

Method new()

initialize

Usage

MissingHelpers$new(data, config, prob = 0.5, weighted = TRUE)

Arguments

data

data

config

config

prob

default 0.5, median of groups with one observed value

weighted

should group average be computed used weighting, default TRUE.

Method get_stats()

get data.frame with statistics

Usage

MissingHelpers$get_stats()

Returns

data.frame

Method get_LOD()

determine limit of detection computes quantile of abundances in groups with a single observation

Usage

MissingHelpers$get_LOD()

Returns

integer LOD

Method impute_weighted_lod()

compute group averages using weighted lod

Usage

MissingHelpers$impute_weighted_lod()

Method impute_lod()

if group average absent substitute with LOD

Usage

MissingHelpers$impute_lod()

Method get_poolvar()

compute pooled var per protein

Usage

MissingHelpers$get_poolvar(prob = 0.75)

Arguments

prob

prob of sd from proteins where it can be computed

Method get_contrast_estimates()

get contrast estimates

Usage

MissingHelpers$get_contrast_estimates(Contrasts)

Arguments

Contrasts

named array with contrasts

Method get_contrasts()

compute contrasts

Usage

MissingHelpers$get_contrasts(Contrasts, confint = 0.95, all = FALSE)

Arguments

Contrasts

vector with contrasts

confint

compute confint

all

return all columns, default FALSE

Method clone()

The objects of this class are cloneable with this method.

Usage

MissingHelpers$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Contrasts <- c("group.b-a" = "group_A - group_B", "group.a-ctrl" = "group_A - group_Ctrl")
dd <- prolfqua::sim_lfq_data_protein_config(Nprot = 100,weight_missing = 2)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
mh <- prolfqua::MissingHelpers$new(dd$data, dd$config, prob = 0.8,weighted = TRUE)
xx <- mh$get_stats()
#> completing cases
xx <- mh$get_LOD()
xx <- mh$impute_weighted_lod()
xx <- mh$impute_lod()
xx <- mh$get_poolvar()
bb <- mh$get_contrast_estimates(Contrasts)
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
mh$get_contrasts(Contrasts)
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
#> group.b-a=group_A - group_B
#> group.a-ctrl=group_A - group_Ctrl
#> # A tibble: 200 × 18
#>    protein_Id  meanAbundanceImp_group_1 meanAbundanceImp_group_2 estimate
#>    <chr>                          <dbl>                    <dbl>    <dbl>
#>  1 0EfVhX~3967                     22.0                     19.2    2.84 
#>  2 0YSKpy~2865                     19.5                     20.0    0    
#>  3 0m5WN4~6025                     19.5                     23.1   -3.59 
#>  4 3QLHfm~8938                     22.2                     21.8    0.365
#>  5 3QYop0~7543                     22.5                     22.1    0.433
#>  6 76k03k~7094                     25.3                     24.3    1.02 
#>  7 7QuTub~1867                     20.4                     24.6   -4.12 
#>  8 7cbcrd~7351                     26.7                     23.6    3.17 
#>  9 7soopj~5352                     20.1                     20.0    0.113
#> 10 7zeekV~7127                     20.6                     20.8   -0.196
#> # ℹ 190 more rows
#> # ℹ 14 more variables: group_1_name <chr>, group_2_name <chr>, contrast <chr>,
#> #   avgAbd <dbl>, indic <dbl>, nrMeasured_group_1 <int>,
#> #   nrMeasured_group_2 <int>, df <dbl>, sd <dbl>, sdT <dbl>, statistic <dbl>,
#> #   p.value <dbl>, conf.low <dbl>, conf.high <dbl>

dd <- prolfqua::sim_lfq_data_2Factor_config(Nprot = 100,weight_missing = 0.1)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done

Contrasts <- c("c1" = "TreatmentA - TreatmentB",
               "C2" = "BackgroundX- BackgroundZ",
               "c3" = "`TreatmentA:BackgroundX` - `TreatmentA:BackgroundZ`",
               "c4" = "`TreatmentB:BackgroundX` - `TreatmentB:BackgroundZ`"
               )
mh <- prolfqua::MissingHelpers$new(dd$data, dd$config, prob = 0.8,weighted = TRUE)
mh$get_stats()$interaction |> table()
#> completing cases
#> completing cases
#> completing cases
#> 
#> TreatmentA:BackgroundX TreatmentB:BackgroundX TreatmentA:BackgroundZ 
#>                    100                    100                    100 
#> TreatmentB:BackgroundZ             TreatmentA             TreatmentB 
#>                    100                    100                    100 
#>            BackgroundX            BackgroundZ 
#>                    100                    100 
mh$get_contrast_estimates(Contrasts)
#> c1=TreatmentA - TreatmentB
#> C2=BackgroundX- BackgroundZ
#> c3=`TreatmentA:BackgroundX` - `TreatmentA:BackgroundZ`
#> c4=`TreatmentB:BackgroundX` - `TreatmentB:BackgroundZ`
#> c1=TreatmentA - TreatmentB
#> C2=BackgroundX- BackgroundZ
#> c3=`TreatmentA:BackgroundX` - `TreatmentA:BackgroundZ`
#> c4=`TreatmentB:BackgroundX` - `TreatmentB:BackgroundZ`
#> c1=TreatmentA - TreatmentB
#> C2=BackgroundX- BackgroundZ
#> c3=`TreatmentA:BackgroundX` - `TreatmentA:BackgroundZ`
#> c4=`TreatmentB:BackgroundX` - `TreatmentB:BackgroundZ`
#> # A tibble: 400 × 11
#>    protein_Id  meanAbundanceImp_group_1 meanAbundanceImp_group_2 estimate
#>    <chr>                          <dbl>                    <dbl>    <dbl>
#>  1 0EfVhX~3967                     21.1                     20.5    0.608
#>  2 0YSKpy~2865                     18.3                     17.1    1.15 
#>  3 0m5WN4~6025                     18.1                     22.2   -4.09 
#>  4 3QLHfm~8938                     23.2                     22.5    0.712
#>  5 3QYop0~7543                     23.3                     23.7   -0.310
#>  6 76k03k~7094                     24.3                     24.6   -0.343
#>  7 7QuTub~1867                     21.6                     25.7   -4.14 
#>  8 7cbcrd~7351                     25.6                     24.8    0.804
#>  9 7soopj~5352                     19.7                     21.6   -1.90 
#> 10 7zeekV~7127                     21.1                     20.3    0.720
#> # ℹ 390 more rows
#> # ℹ 7 more variables: group_1_name <chr>, group_2_name <chr>, contrast <chr>,
#> #   avgAbd <dbl>, indic <dbl>, nrMeasured_group_1 <int>,
#> #   nrMeasured_group_2 <int>