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Plot panel with volcano plot and according to the data type - scatter plots or box plots

Source: R/plot-prism.R
plot_volcano_assoc_panel.Rd

Plot panel with volcano plot and according to the data type - scatter plots or box plots

Usage

plot_volcano_assoc_panel(
  dt_response,
  dt_depmap,
  selected_metric,
  selected_feat_meta_col
)

Arguments

dt_response

data.table with the experimental response data (rows are samples) for one metric outputted by one of functions: prep_dt_response_metric_sa, prep_dt_response_dose_sa, prep_dt_response_scores or prep_dt_response_metric_diff, must have at least a column with CellLineName and a numeric column with metric values.

dt_depmap

data.table with dependent variables data loaded from DepMap where rows are samples, columns are features/metadata levels; one of: data for one feature outputted by prep_dt_depmap_feat or data or data for one metadata outputted by prep_dt_depmap_meta

selected_metric

string name of the metric in dt_response

selected_feat_meta_col

string with name of selected feature from dt_depmap or the name of the selected metadata from dt_depmap - respectively

Value

A named list with elements:

  • assoc_data table with association data

  • panel ggplot object containing a panel with volcano plot and depending on data type: a scatter plots with correlation for top 4 variables or boxplots for variable levels

Author

Janina Smoła janina.smola@external.roche.com

Examples

mae_prism <- gDRutils::get_synthetic_data("prism")
se_prism <- mae_prism[[gDRutils::get_supported_experiments("sa")]]
dt_metrics <- gDRutils::convert_se_assay_to_dt(se_prism, "Metrics")
dt_response <- prep_dt_response_metric_sa(
  dt_metrics = dt_metrics,
  d_name = unique(dt_metrics[["DrugName"]])[1],
  normalization_type = "RV", metric = "x_mean")
feat_data_path <- system.file("depmap_data", package = "gDRtestData")
meta_data_path <- system.file("depmap_data/Model.csv.gz", package = "gDRtestData")
obj_depmap_feat <- prep_dt_depmap_feat(feat_data_path, meta_data_path,
                                       feature_set = "OmicsCNGene")
plot_volcano_assoc_panel(
  dt_response = dt_response,
  dt_depmap = obj_depmap_feat[["dt_depmap"]],
  selected_metric = "RV_gDR_x_mean",
  selected_feat_meta_col = obj_depmap_feat[["selected_feat_meta_col"]])
#> $assoc_data
#>              feature      response         rho     q_value neglog_q_value
#>               <char>        <char>       <num>       <num>          <num>
#>   1: ZNF534 (147658) RV_gDR_x_mean -0.14824996 0.002867780      2.5424541
#>   2: ZNF615 (284370) RV_gDR_x_mean -0.14248001 0.003925022      2.4061579
#>   3:    TGFB1 (7040) RV_gDR_x_mean -0.13649677 0.005332100      2.2731017
#>   4:     CIC (23152) RV_gDR_x_mean -0.13206367 0.007002094      2.1547721
#>   5:    CNOT3 (4849) RV_gDR_x_mean -0.12565526 0.009563663      2.0193757
#>  ---                                                                     
#> 146: UGT3A2 (167127) RV_gDR_x_mean  0.01442878 0.725568543      0.1393216
#> 147:      MET (4233) RV_gDR_x_mean -0.01005047 0.727138752      0.1383827
#> 148:     CDK4 (1019) RV_gDR_x_mean -0.04991113 0.728755050      0.1374184
#> 149:       ALK (238) RV_gDR_x_mean  0.01879964 0.730468704      0.1363984
#> 150:     MYCN (4613) RV_gDR_x_mean  0.05212956 0.732223291      0.1353565
#> 
#> $panel

#>