AggregateLimpa

AggregateLimpa

AggregateLimpa

Value

An R6 class generator.

Details

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

See also

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

Public fields

lfq

LFQData (deep cloned input)

lfq_agg

aggregation result

prefix

to use for aggregation results e.g. protein

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()

---

Method 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

---

Method aggregate()

run limpa DPC-based aggregation (or imputation)

Usage

AggregateLimpa$aggregate()

Returns

LFQData

---

Method 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

---

Method 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

---

Method 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()
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         4.40  4.32  4.26  4.34  4.74  4.68  4.73  4.85
#>  2 7cbcrd~5725 light         4.90  4.90  4.87  4.78  3.64  4.43  4.38  4.39
#>  3 9VUkAq~4703 light         4.10  4.12  4.25  4.24  4.04  3.59  4.13  4.14
#>  4 BEJI92~5282 light         4.31  4.37  4.36  4.37  4.54  4.47  4.47  4.40
#>  5 CGzoYe~2147 light         4.60  4.60  4.65  4.58  3.93  4.96  4.95  4.95
#>  6 DoWup2~5896 light         4.59  4.60  4.54  4.54  4.21  4.22  4.21  4.15
#>  7 Fl4JiV~8625 light         4.35  4.41  4.29  4.35  4.54  4.56  4.65  4.54
#>  8 HvIpHG~9079 light         4.15  4.29  4.10  4.24  4.74  4.78  4.78  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         4.67  4.61  4.76  4.73  4.73  4.69  4.71  4.74
#> # ℹ 4 more variables: Ctrl_V1 <dbl>, Ctrl_V2 <dbl>, Ctrl_V3 <dbl>,
#> #   Ctrl_V4 <dbl>
#> 
#> $annotation
#>    sampleName  sample group_ isotopeLabel
#> 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 (rev = FALSE) 
#>     hierarchy_keys_depth: function (names = TRUE) 
#>     id_required: function () 
#>     id_vars: function () 
#>     ident_q_value: qValue
#>     ident_score: 
#>     initialize: function () 
#>     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
#>     pop_response: function () 
#>     sample_name: sampleName
#>     sep: ~
#>     set_response: function (col_name) 
#>     value_vars: function () 
#>     work_intensity: limpa
#>