plot_stdv_vs_mean: plot stddev vs mean to asses stability of variance

In wolski/prolfqua: Proteomics Label Free Quantification

plot stddev vs mean to asses stability of variance

Description

plot stddev vs mean to asses stability of variance

Usage

plot_stdv_vs_mean(pdata, config, size = 2000)

Arguments

pdata	data.frame
config	AnalysisConfiguration
size	how many points to sample (since scatter plot to slow for all)

See Also

Other stats: INTERNAL_FUNCTIONS_BY_FAMILY, lfq_power_t_test_proteins(), lfq_power_t_test_quantiles(), lfq_power_t_test_quantiles_V2(), plot_stat_density(), plot_stat_density_median(), plot_stat_violin(), plot_stat_violin_median(), pooled_V2(), summarize_stats()

Examples

bb1 <- prolfqua::sim_lfq_data_peptide_config()
config <- bb1$config
data <- bb1$data
res <- summarize_stats(data, config)

plot_stdv_vs_mean(res, config)
datalog2 <- transform_work_intensity(data, config, log2)
statlog2 <- summarize_stats(datalog2, config)
plot_stdv_vs_mean(statlog2, config)
config$table$get_response()
config$table$pop_response()
datasqrt <- transform_work_intensity(data, config, sqrt)
ressqrt <- summarize_stats(datasqrt, config)
plot_stdv_vs_mean(ressqrt, config)

wolski/prolfqua documentation built on May 12, 2024, 10:16 p.m.