trelli_pvalue_filter: Filter a paneled trelliData object by a p-value

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

Filter a paneled trelliData object by a p-value

Description

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.

Usage

trelli_pvalue_filter(
  trelliData,
  p_value_test = "anova",
  p_value_thresh = 0.05,
  comparison = NULL
)

Arguments

trelliData	A trelliData object with statistics results (statRes). Required.
p_value_test	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.
p_value_thresh	A value between 0 and 1 to indicate the p-value threshold at which to keep plots. Default is 0.05. Required.
comparison	The specific comparison to filter significant values to. Can be null. See attr(statRes, "comparisons") for the available options. Optional.

Value

A paneled trelliData object with only plots corresponding to significant p-values from a statistical test.

Author(s)

David Degnan, Lisa Bramer

Examples

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")

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