AggregateRlm

AggregateRlm

AggregateRlm

Details

Aggregates peptide intensities to protein level using robust regression (rlm). Works best with variance-stabilized (log-transformed) intensities.

See also

Other LFQData: AggregateMedpolish, AggregateTopN, LFQData, LFQDataImp, LFQDataPlotter, LFQDataStats, LFQDataSummariser, LFQDataToSummarizedExperiment()

Public fields

lfq

LFQData

lfq_agg

aggregation result

prefix

to use for aggregation results e.g. protein

Methods

Public methods

• AggregateRlm$new()

• AggregateRlm$aggregate()

• AggregateRlm$plot()

• AggregateRlm$write_plots()

• AggregateRlm$clone()

---

Method new()

initialize

Usage

AggregateRlm$new(lfq, prefix = "protein")

Arguments

lfq

LFQData

prefix

default protein

---

Method aggregate()

run robust regression aggregation

Usage

AggregateRlm$aggregate()

Returns

LFQData

---

Method plot()

creates aggregation plots

Usage

AggregateRlm$plot(subset = NULL, show.legend = FALSE)

Arguments

subset

create plots for a subset of the data only

show.legend

default FALSE

Returns

data.frame

---

Method write_plots()

writes plots to folder

Usage

AggregateRlm$write_plots(
  qcpath,
  subset = NULL,
  show.legend = FALSE,
  width = 6,
  height = 6
)

Arguments

qcpath

qcpath

subset

write plots only for some

show.legend

legend

width

figure width

height

figure height

Returns

file path

---

Method clone()

The objects of this class are cloneable with this method.

Usage

AggregateRlm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
data <- istar$data |> dplyr::filter(protein_Id %in% sample(protein_Id, 100))
lfqdata <- LFQData$new(data, istar$config)
lfqTrans <- lfqdata$clone()$get_Transformer()$log2()$robscale()$lfq
#> Column added : log2_abundance
#> data is : TRUE
#> Joining with `by = join_by(sampleName, isotopeLabel, protein_Id, peptide_Id)`

agg <- AggregateRlm$new(lfqTrans, "protein")
agg$aggregate()
#> starting aggregation
p <- agg$plot()
p$plots[[1]]
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_line()`).