hypothesisTest: Parameter estimates, standard errors and statistical...

In statOmics/msqrob2: Robust statistical inference for quantitative LC-MS proteomics

Parameter estimates, standard errors and statistical inference on differential expression analysis

Description

Summary table of the estimates for differential expression of features

Usage

## S4 method for signature 'SummarizedExperiment'
hypothesisTest(
  object,
  contrast,
  adjust.method = "BH",
  modelColumn = "msqrobModels",
  resultsColumnNamePrefix = "",
  overwrite = FALSE
)

## S4 method for signature 'SummarizedExperiment'
hypothesisTestHurdle(
  object,
  contrast,
  adjust.method = "BH",
  modelColumn = "msqrobHurdle",
  resultsColumnNamePrefix = "hurdle_",
  overwrite = FALSE
)

## S4 method for signature 'QFeatures'
hypothesisTest(
  object,
  i,
  contrast,
  adjust.method = "BH",
  modelColumn = "msqrobModels",
  resultsColumnNamePrefix = "",
  overwrite = FALSE
)

## S4 method for signature 'QFeatures'
hypothesisTestHurdle(
  object,
  i,
  contrast,
  adjust.method = "BH",
  modelColumn = "msqrobHurdle",
  resultsColumnNamePrefix = "hurdle_",
  overwrite = FALSE
)

Arguments

object	SummarizedExperiment or QFeatures instance
contrast	numeric matrix specifying one or more contrasts of the linear model coefficients to be tested equal to zero. If multiple contrasts are given (multiple columns) then results will be returned for each contrast. The rownames of the matrix should be equal to the names of parameters of the model that are involved in the contrast. The column names of the matrix will be used to construct names to store the results in the rowData of the SummarizedExperiment or of the assay of the QFeatures object. The contrast matrix can be made using the makeContrast function.
adjust.method	character specifying the method to adjust the p-values for multiple testing. Options, in increasing conservatism, include ‘"none"’, ‘"BH"’, ‘"BY"’ and ‘"holm"’. See ‘p.adjust’ for the complete list of options. Default is "BH" the Benjamini-Hochberg method to controle the False Discovery Rate (FDR).
modelColumn	character to indicate the variable name that was used to store the msqrob models in the rowData of the SummarizedExperiment instance or of the assay of the QFeatures instance. Default is "msqrobModels" when the hypothesisTest function is used and "msqrobHurdle" for hypothesisTestHurdle.
resultsColumnNamePrefix	character to indicate the the prefix for the variable name that will be used to store test results in the rowData of the SummarizedExperiment instance or of the assay of the QFeatures instance. Default is "" so that the variable name with the results will be the column name of the column in the contrast matrix L. If L is a matrix with multiple columns, multiple results columns will be made, one for each contrast. If L is a matrix with a single column which has no column names and if resultsColumnNamePrefix="" the results will be stored in the column with name msqrobResults. For hypothesisTestHurdle the default prefix is "hurdle_". If L is a matrix with one column and has no column names and if resultsColumnNamePrefix="hurdle_" the results will be stored in the column with name hurdleResults.
overwrite	boolean(1) to indicate if the column in the rowData has to be overwritten if the modelColumnName already exists. Default is FALSE.
i	character or integer to specify the element of the QFeatures that contains the log expression intensities that will be modelled.

Value

A SummarizedExperiment or a QFeatures instance augmented with the test results.

Author(s)

Lieven Clement

Examples

# Load example data
# The data are a Feature object containing
# a SummarizedExperiment named "peptide" with MaxQuant peptide intensities
# The data are a subset of spike-in the human-ecoli study
# The variable condition in the colData of the Feature object
# contains information on the spike in condition a-e (from low to high)
data(pe)

# Aggregate peptide intensities in protein expression values
pe <- aggregateFeatures(pe, i = "peptide", fcol = "Proteins", name = "protein")

# Fit msqrob model
pe <- msqrob(pe, i = "protein", formula = ~condition)

# Define contrast
getCoef(rowData(pe[["protein"]])$msqrobModels[[1]])
# Assess log2 fold change between condition c and condition b
L <- makeContrast(
    "conditionc - conditionb=0",
    c("conditionb", "conditionc")
)

# example SummarizedExperiment instance
se <- pe[["protein"]]
se <- hypothesisTest(se, L)
head(rowData(se)$"conditionc - conditionb", 10)
# Volcano plot
plot(-log10(pval) ~ logFC,
    rowData(se)$"conditionc - conditionb",
    col = (adjPval < 0.05) + 1
)

# Example for QFeatures instance
# Assess log2 fold change between condition b and condition a (reference class),
# condition c and condition a, and, condition c and condition b.
L <- makeContrast(
    c(
        "conditionb=0",
        "conditionc=0",
        "conditionc - conditionb=0"
    ),
    c("conditionb", "conditionc")
)
pe <- hypothesisTest(pe, i = "protein", L)
head(rowData(pe[["protein"]])$"conditionb", 10)
# Volcano plots
par(mfrow = c(1, 3))
plot(-log10(pval) ~ logFC,
    rowData(pe[["protein"]])$"conditionb",
    col = (adjPval < 0.05) + 1,
    main = "log2 FC b-a"
)
plot(-log10(pval) ~ logFC,
    rowData(pe[["protein"]])$"conditionc",
    col = (adjPval < 0.05) + 1,
    main = "log2 FC c-a"
)
plot(-log10(pval) ~ logFC,
    rowData(pe[["protein"]])$"conditionc - conditionb",
    col = (adjPval < 0.05) + 1,
    main = "log2 FC c-b"
)

# Hurdle method
pe <- msqrobHurdle(pe, i = "protein", formula = ~condition)
pe <- hypothesisTestHurdle(pe, i = "protein", L)
head(rowData(pe[["protein"]])$"hurdle_conditionb", 10)

statOmics/msqrob2 documentation built on May 8, 2024, 4:38 p.m.