Tests the null hypothesis that the number of missing observations is independent of the groups. A g-test is used to test this null hypothese against the alternative that the groups and missing data are related. This is usually performed in conjuction with an ANOVA which tests if the mean response (which varies with data type) is the same across groups; this combination is called IMD_ANOVA. It's probably a good idea to first filter the data with `imd_anova_filter` to see if there is enough infomration to even do this test. See Webb-Robertson et al. (2010) for more.

imd_test(
  omicsData,
  groupData,
  comparisons,
  pval_adjust_multcomp,
  pval_adjust_fdr,
  pval_thresh,
  covariates,
  paired,
  parallel = TRUE
)

Arguments

omicsData

A pmartR data object of any class

groupData

`data.frame` that assigns sample names to groups

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.

pval_adjust_multcomp

A character string specifying the type of multiple comparisons adjustment to implement. The default setting, "none", is to not apply an adjustment. Valid options include: "bonferroni" and "holm".

pval_adjust_fdr

A character string specifying the type of FDR adjustment to implement. The default setting, "none", is to not apply an adjustment. Valid options include: "bonferroni", "BH", "BY", and "fdr".

pval_thresh

numeric p-value threshold, below or equal to which peptides are considered differentially expressed. Defaults to 0.05

covariates

A character vector with no more than two variable names that will be used as covariates in the IMD-ANOVA analysis.

paired

A logical value that determines whether paired data should be accounted for

parallel

A logical value indicating whether or not to use a "doParallel" loop when running the G-Test with covariates. The default is TRUE.

Value

a list of `data.frame`s

Resultse_data cname, Count of non-missing data for each group, Global G-test statistic and p-value
GstatsValue of the g statistics for each of the pairwise comparisons specified by the `comparisons` argument
Pvaluesp-values for each of the pairwise comparisons specified by `comparisons` argument
FlagsIndicator of statistical significance where the sign of the flag reflects the difference in the ratio of non-missing observations (0/+-2 to if adjusted p-value>=pval_thresh or p-value<pval_thresh)

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.

Author

Bryan Stanfill