imd_anova: Test for a qualitative and quantitative difference between...

In pmartR/pmartRqc: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

Test for a qualitative and quantitative difference between groups using IMD and ANOVA, respectively

Description

This is the IMD-ANOVA test defined in Webb-Robertson et al. (2010).

Usage

imd_anova(
  omicsData,
  comparisons = NULL,
  test_method,
  pval_adjust_a_multcomp = "none",
  pval_adjust_g_multcomp = "none",
  pval_adjust_a_fdr = "none",
  pval_adjust_g_fdr = "none",
  pval_thresh = 0.05,
  equal_var = TRUE,
  parallel = TRUE
)

Arguments

omicsData	pmartR data object of any class, which has a 'group_df' attribute created by the 'group_designation()' function
comparisons	data frame with columns for "Control" and "Test" containing the different comparisons of interest. Comparisons will be made between the Test and the corresponding Control. If left NULL, then all pairwise comparisons are executed.
test_method	character string specifying the filter method to use: "combined", "gtest", or "anova". Specifying "combined" implements both the gtest and anova filters.
pval_adjust_a_multcomp	character string specifying the type of multiple comparison adjustment to implement for ANOVA tests. Valid options include: "bonferroni", "holm", "tukey", and "dunnett". The default is "none" which corresponds to no p-value adjustment.
pval_adjust_g_multcomp	character string specifying the type of multiple comparison adjustment to implement for G-test tests. Valid options include: "bonferroni" and "holm". The default is "none" which corresponds to no p-value adjustment.
pval_adjust_a_fdr	character string specifying the type of FDR adjustment to implement for ANOVA tests. Valid options include: "bonferroni", "BH", "BY", and "fdr". The default is "none" which corresponds to no p-value adjustment.
pval_adjust_g_fdr	character string specifying the type of FDR adjustment to implement for G-test tests. Valid options include: "bonferroni", "BH", "BY", and "fdr". The default is "none" which corresponds to no p-value adjustment.
pval_thresh	numeric p-value threshold, below or equal to which biomolecules are considered differentially expressed. Defaults to 0.05
equal_var	logical; should the variance across groups be assumed equal?
parallel	logical value indicating whether or not to use a "doParallel" loop when running the G-Test with covariates. Defaults to TRUE.

Value

An object of class 'statRes', which is a data frame containing columns (when relevant based on the test(s) performed) for: e_data cname, group counts, group means, ANOVA p-values, IMD p-values, fold change estimates on the same scale as the data (e.g. log2, log10, etc.), and fold change significance flags (0 = not significant; +1 = significant and positive fold change (ANOVA) or more observations in test group relative to reference group (IMD); -1 = significant and negative fold change (ANOVA) or fewer observations in test group relative to reference group (IMD))

Author(s)

Bryan Stanfill, Kelly Stratton

References

Webb-Robertson, Bobbie-Jo M., et al. "Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data." Journal of proteome research 9.11 (2010): 5748-5756.

Examples

library(pmartRdata)
# Transform the data
mymetab <- edata_transform(omicsData = metab_object, data_scale = "log2")

# Group the data by condition
mymetab <- group_designation(omicsData = mymetab, main_effects = c("Phenotype"))

# Apply the IMD ANOVA filter
imdanova_Filt <- imdanova_filter(omicsData = mymetab)
mymetab <- applyFilt(filter_object = imdanova_Filt, omicsData = mymetab, min_nonmiss_anova = 2)

# Implement IMD ANOVA and compute all pairwise comparisons 
# (i.e. leave the comparisons argument NULL), with FDR adjustment
anova_res <- imd_anova(omicsData = mymetab, test_method = "anova",
                       pval_adjust_a_multcomp = "Holm", pval_adjust_a_fdr = "BY")
imd_res <- imd_anova(omicsData = mymetab, test_method = "gtest",
                     pval_adjust_g_multcomp = "bon", pval_adjust_g_fdr = "BY")
imd_anova_res <- imd_anova(omicsData = mymetab, test_method = "combined",
                           pval_adjust_a_fdr = "BY", pval_adjust_g_fdr = "BY")
imd_anova_res <- imd_anova(omicsData = mymetab, test_method = "combined",
                           pval_adjust_a_multcomp = "bon", pval_adjust_g_multcomp = "bon",
                           pval_adjust_a_fdr = "BY", pval_adjust_g_fdr = "BY")

# Two main effects and a covariate
mymetab <- group_designation(omicsData = mymetab, main_effects = c("Phenotype", "SecondPhenotype"),
                             covariates = "Characteristic")
imd_anova_res <- imd_anova(omicsData = mymetab, test_method = 'comb')

# Same but with custom comparisons
comp_df <- data.frame(Control = c("Phenotype1", "A"), Test = c("Phenotype2", "B"))
custom_comps_res <- imd_anova(omicsData = mymetab, comparisons = comp_df, test_method = "combined")

pmartR/pmartRqc documentation built on April 25, 2024, 6:18 a.m.