Package index • prolfqua

All functions

AnalysisConfiguration: Analysis Configuration
Contrasts: Estimate contrasts using Wald Test
ContrastsDEqMSFacade: DEqMS contrast analysis facade
ContrastsFirth: Estimate contrasts using Wald Test
ContrastsFirthFacade: Firth logistic missingness contrast analysis facade
ContrastsInterface: Base class for all Contrasts classes
ContrastsLMFacade: LM contrast analysis facade
ContrastsLMMissingFacade: LM + missing-value imputation contrast analysis facade
ContrastsLimma: Limma-based contrasts (direct limma pipeline)
ContrastsLimmaFacade: Limma contrast analysis facade
ContrastsLmerFacade: Lmer contrast analysis facade
ContrastsMissing: Compute contrasts with group mean imputation
ContrastsModerated: Limma moderated contrasts
ContrastsModeratedDEqMS: DEqMS count-dependent moderated contrasts
ContrastsPlotter: plot contrasts
ContrastsRLMFacade: RLM contrast analysis facade
ContrastsROPECA: ROPECA reproducibility-optimization method
ContrastsROPECAFacade: ROPECA contrast analysis facade
ContrastsTable: holds results when contrasts are added.
INTERNAL_FUNCTIONS_BY_FAMILY: Internal Functions by category
LFQData: LFQData R6 class
LFQDataAggregator: Decorates LFQData with methods to aggregate protein intensities aggregates intensities
LFQDataImp: Decorates LFQData with methods to impute missing values
LFQDataPlotter: LFQDataPlotter —- Create various visualization of the LFQdata
LFQDataStats: Decorates LFQData with methods to compute statistics of interactions
LFQDataSummariser: Summarize LFQData
LFQDataToSummarizedExperiment(): converts LFQData object to SummarizedExperiment
LFQDataTransformer: Decorate LFQData with Methods for transforming Intensities
LR_test(): Likelihood ratio test
MissingHelpers: compute group mean by LOD
Model: R6 class representing modelling result
ModelFirth: R6 class representing modelling result
ModelInterface: R6 interface class representing modelling result
ModelLimma: R6 class representing a limma modelling result
PACKAGE_DATA: Package Data
R6_extract_values(): Extract all value slots in an R6 object
UpSet_interaction_missing_stats(): UpSetR plot from interaction_missing_stats
adjust_p_values(): adjust columns
annotation_add_contrasts(): DRY function: process and export annotated contrasts
build_contrast_analysis(): Build a contrast analysis using one of several statistical methods
build_model(): Build protein models from data
build_model_glm_peptide(): Build Firth logistic model for nested LFQData
build_model_glm_protein(): Build Firth logistic model for aggregated LFQData
build_model_limma(): Build limma model from LFQData
center_to_reference_cfg(): center to reference
create_config_Skyline() create_config_Spectronaut_Peptide() create_config_MQ_peptide() create_config_MSFragger_MSstats(): Generate instances of AnalysisConfiguration
estimate_lod_global(): esitmate lod
find_package_file(): find file stored in package
function_lod_quantile(): get smallest values per sample
generate_contrasts(): Combined generate_contrasts
generate_contrasts_for_factor(): Single-factor contrasts (pairwise comparisons)
get_UniprotID_from_fasta_header(): Extracts uniprot ID
group_label(): group label function
impute_with_zcomp(): Impute missing values using zCompositions
interaction_contrasts(): Interaction contrasts (difference of differences)
level_specific_contrasts(): Level-specific contrasts (per secondary level)
list_to_AnalysisConfiguration(): read minimal yaml to reconstruct configuration
main_effect_contrasts(): main effects contrasts
matrix_to_tibble(): matrix or data.frame to tibble (taken from tidyquant)
merge_contrasts_results(): Merge contrast results coming from two different model.
nr_obs_experiment(): Aggregates e.g. protein abundances from peptide abundances
nr_obs_sample(): Aggregates e.g. protein abundances from peptide abundances
old2new(): Convert old proflqua configurations (prolfqua 0.4) to new Analysis configurations prolfqua 0.5
pivot_model_contrasts_2_Wide(): pivot model contrasts matrix to wide format produced by `contrasts_linfct` and ...
print(<pheatmap>): Print method for pheatmap objects
prolfqua_data(): load data from prolfqua
scatter_plotly(): scatter plotly
setup_analysis(): Setup a tidy table compatible with a AnalysisConfiguration
sim_lfq_data(): simulate protein level data with two groups
sim_lfq_data_2Factor_config(): Simulate data, protein, with config with 2 factors Treatment and Background
sim_lfq_data_peptide_config(): Simulate data, protein and peptide, with config
sim_lfq_data_protein_config(): Simulate data, protein, with config
squeezeVarRob(): Robustly Squeeze Sample Variances
strategy_logistf() strategy_lmer() strategy_lm() strategy_rlm() strategy_glm(): Firth's Bias-Reduced Logistic Regression (logistf)
strategy_limma(): Create limma modelling strategy
summarize_stats_factors(): compute var sd etc for all factor levels
table_facade(): table facade to easily switch implementations
volcano_plotly(): volcano plotly
which_missing(): add missing values to x vector based on the values of x