AggregateLimpa

Aggregates peptide/precursor intensities to protein level using limpa's probabilistic DPC-based quantification (limpa::dpcQuant). With impute_only = TRUE, performs same-level imputation without aggregation using limpa::dpcQuantByRow.

The output LFQData contains three value columns:

• intensity ("limpa") — protein-level log-expression (complete, no NAs)

• standard error (in config$opt_se) — per-protein, per-sample posterior SEs

• observation count (in config$nr_children) — number of observed precursors

Value

An R6 class generator.

See also

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

Super class

AggregatorBase -> AggregateLimpa

Public fields

dpc_result

estimated DPC object from limpa::dpc

dpc_slope

DPC slope parameter (default 0.8)

impute_only

if TRUE use dpcQuantByRow (no aggregation)

Methods

Public methods

• AggregateLimpa$new()

• AggregateLimpa$aggregate()

• AggregateLimpa$plot()

• AggregateLimpa$write_plots()

• AggregateLimpa$clone()

---

AggregateLimpa$new()

initialize

Usage

AggregateLimpa$new(
  lfq,
  prefix = "protein",
  dpc_slope = 0.8,
  impute_only = FALSE
)

Arguments

lfq

LFQData with log2-transformed intensities

prefix

default "protein"

dpc_slope

DPC slope parameter passed to limpa (default 0.8)

impute_only

if TRUE, use dpcQuantByRow instead of dpcQuant

---

AggregateLimpa$aggregate()

run limpa DPC-based aggregation (or imputation)

Usage

AggregateLimpa$aggregate()

Returns

LFQData

---

AggregateLimpa$plot()

creates aggregation plots (only for aggregation mode, not impute_only)

Usage

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

Arguments

subset

create plots for a subset of the data only

show.legend

default FALSE

Returns

data.frame

---

AggregateLimpa$write_plots()

writes plots to folder

Usage

AggregateLimpa$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

---

AggregateLimpa$clone()

The objects of this class are cloneable with this method.

Usage

AggregateLimpa$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata <- lfqdata$get_Transformer()$log2()$lfq
#> Column added : log2_abundance

agg <- AggregateLimpa$new(lfqdata, "protein")
agg$aggregate()
#> completing cases
agg$lfq_agg$data_wide()
#> $data
#> # A tibble: 10 × 14
#>    protein_Id  isotopeLabel  A_V1  A_V2  A_V3  A_V4  B_V1  B_V2  B_V3  B_V4
#>    <chr>       <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 0EfVhX~0087 light         4.37  4.28  4.26  4.31  4.74  4.68  4.72  4.78
#>  2 7cbcrd~5725 light         4.90  4.90  4.86  4.77  3.55  4.43  4.38  4.39
#>  3 9VUkAq~4703 light         4.10  4.12  4.25  4.24  4.05  3.53  4.13  4.14
#>  4 BEJI92~5282 light         4.31  4.37  4.30  4.30  4.54  4.47  4.47  4.40
#>  5 CGzoYe~2147 light         4.60  4.60  4.65  4.58  3.66  4.96  4.95  4.95
#>  6 DoWup2~5896 light         4.59  4.60  4.54  4.54  4.21  4.22  4.21  4.16
#>  7 Fl4JiV~8625 light         4.32  4.37  4.29  4.27  4.54  4.56  4.60  4.51
#>  8 HvIpHG~9079 light         4.09  4.29  4.04  4.24  4.74  4.78  4.77  4.74
#>  9 JcKVfU~9653 light         4.97  5.00  5.01  5.02  5.22  5.23  5.22  5.27
#> 10 SGIVBl~5782 light         4.60  4.61  4.73  4.70  4.73  4.67  4.71  4.74
#> # ℹ 4 more variables: Ctrl_V1 <dbl>, Ctrl_V2 <dbl>, Ctrl_V3 <dbl>,
#> #   Ctrl_V4 <dbl>
#> 
#> $annotation
#> # A tibble: 12 × 4
#>    sampleName sample  group_ isotopeLabel
#>    <chr>      <chr>   <chr>  <chr>       
#>  1 A_V1       A_V1    A      light       
#>  2 A_V2       A_V2    A      light       
#>  3 A_V3       A_V3    A      light       
#>  4 A_V4       A_V4    A      light       
#>  5 B_V1       B_V1    B      light       
#>  6 B_V2       B_V2    B      light       
#>  7 B_V3       B_V3    B      light       
#>  8 B_V4       B_V4    B      light       
#>  9 Ctrl_V1    Ctrl_V1 Ctrl   light       
#> 10 Ctrl_V2    Ctrl_V2 Ctrl   light       
#> 11 Ctrl_V3    Ctrl_V3 Ctrl   light       
#> 12 Ctrl_V4    Ctrl_V4 Ctrl   light       
#> 
#> $rowdata
#> # A tibble: 10 × 2
#>    protein_Id  isotopeLabel
#>    <chr>       <chr>       
#>  1 0EfVhX~0087 light       
#>  2 7cbcrd~5725 light       
#>  3 9VUkAq~4703 light       
#>  4 BEJI92~5282 light       
#>  5 CGzoYe~2147 light       
#>  6 DoWup2~5896 light       
#>  7 Fl4JiV~8625 light       
#>  8 HvIpHG~9079 light       
#>  9 JcKVfU~9653 light       
#> 10 SGIVBl~5782 light       
#> 
#> $config
#> <AnalysisConfiguration>
#>   Public:
#>     annotation_vars: function () 
#>     bin_resp: 
#>     clone: function (deep = FALSE) 
#>     factor_depth: 1
#>     factor_keys: function () 
#>     factor_keys_depth: function () 
#>     factors: list
#>     file_name: sample
#>     get_response: function () 
#>     hierarchy: list
#>     hierarchy_depth: 1
#>     hierarchy_keys: function () 
#>     hierarchy_keys_depth: function (names = TRUE) 
#>     id_vars: function () 
#>     ident_q_value: qValue
#>     ident_score: 
#>     is_response_transformed: TRUE
#>     isotope_label: isotopeLabel
#>     min_peptides_protein: 2
#>     norm_value: NULL
#>     nr_children: nr_children_protein_Id
#>     opt_mz: 
#>     opt_rt: 
#>     opt_se: limpa_se
#>     pattern_contaminants: NULL
#>     pattern_decoys: NULL
#>     pop_response: function () 
#>     sample_name: sampleName
#>     sep: ~
#>     set_response: function (col_name) 
#>     value_vars: function () 
#>     work_intensity: limpa
#>