This use-case-specific function allows users to filter down their plots to a specified p-value IF statistics data has been included. This function is mostly relevant to the MODE application.
trelli_pvalue_filter(
trelliData,
p_value_test = "anova",
p_value_thresh = 0.05,
comparison = NULL
)A trelliData object with statistics results (statRes). Required.
A string to indicate which p_values to plot. Acceptable entries are "anova" or "gtest". Default is "anova". Unlike the plotting functions, here p_value_test cannot be null. Required unless the data is seqData, when this parameter will be ignored.
A value between 0 and 1 to indicate the p-value threshold at which to keep plots. Default is 0.05. Required.
The specific comparison to filter significant values to. Can be null. See attr(statRes, "comparisons") for the available options. Optional.
A paneled trelliData object with only plots corresponding to significant p-values from a statistical test.
# \donttest{
library(pmartRdata)
# Transform the data
omicsData <- edata_transform(omicsData = pep_object, data_scale = "log2")
# Group the data by condition
omicsData <- group_designation(omicsData = omicsData, main_effects = c("Phenotype"))
# Apply the IMD ANOVA filter
imdanova_Filt <- imdanova_filter(omicsData = omicsData)
omicsData <- applyFilt(filter_object = imdanova_Filt, omicsData = omicsData,
min_nonmiss_anova = 2)
# Normalize my pepData
omicsData <- normalize_global(omicsData, "subset_fn" = "all", "norm_fn" = "median",
"apply_norm" = TRUE, "backtransform" = TRUE)
# Implement the IMD ANOVA method and compute all pairwise comparisons
# (i.e. leave the `comparisons` argument NULL)
statRes <- imd_anova(omicsData = omicsData, test_method = 'combined')
# Generate the trelliData object
trelliData3 <- as.trelliData(statRes = statRes)
trelliData4 <- as.trelliData(omicsData = omicsData, statRes = statRes)
###########################
## MS/NMR OMICS EXAMPLES ##
###########################
# Filter a trelliData object with only statistics results, while not caring about a comparison
trelli_pvalue_filter(trelliData3, p_value_test = "anova", p_value_thresh = 0.1)
# Filter a trelliData object with only statistics results, while caring about a specific comparison
trelli_pvalue_filter(
trelliData3, p_value_test = "anova", p_value_thresh = 0.1, comparison = "Phenotype3_vs_Phenotype2")
# Filter both a omicsData and statRes object, while not caring about a specific comparison
trelli_pvalue_filter(trelliData4, p_value_test = "anova", p_value_thresh = 0.001)
# Filter both a omicsData and statRes object, while caring about a specific comparison
trelli_pvalue_filter(
trelliData4, p_value_test = "gtest", p_value_thresh = 0.25,
comparison = "Phenotype3_vs_Phenotype2"
)
######################
## RNA-SEQ EXAMPLES ##
######################
#' # Group data by condition
omicsData_seq <- group_designation(omicsData = rnaseq_object, main_effects = c("Virus"))
# Filter low transcript counts
omicsData_seq <- applyFilt(
filter_object = total_count_filter(omicsData = omicsData_seq),
omicsData = omicsData_seq, min_count = 15
)
# Select a normalization and statistics method (options are 'edgeR', 'DESeq2', and 'voom').
# See ?difexp_seq for more details
statRes_seq <- diffexp_seq(omicsData = omicsData_seq, method = "voom")
# Generate the trelliData object
trelliData_seq3 <- as.trelliData(statRes = statRes_seq)
trelliData_seq4 <- as.trelliData(omicsData = omicsData_seq, statRes = statRes_seq)
# Filter a trelliData seqData object with only statistics results, while not
# caring about a comparison
trelliData_seq3_filt <- trelli_pvalue_filter(trelliData_seq3, p_value_thresh = 0.05)
# Filter both a omicsData and statRes object, while caring about a specific comparison
trelliData_seq4_filt <- trelli_pvalue_filter(trelliData_seq4, p_value_thresh = 0.05,
comparison = "StrainA_vs_StrainB")
# }