Package 'SmartPhos'

Description

addZeroTime adds a zero timepoint to a specific treatment's data subset.

Usage

addZeroTime(data, condition, treat, zeroTreat, timeRange)

Arguments

Details

The function performs the following steps:

1. Subsets the data for the specified treatment and time range.

2. Subsets the data for the zero timepoint of the specified zero treatment.

3. Combines the assays from the treatment and zero timepoint subsets.

4. Updates the column data to reflect the combined treatment.

5. Returns a SummarizedExperiment object with the combined data.

Value

A SummarizedExperiment object with the zero timepoint added to the specified treatment's data.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Call the function
addZeroTime(se, condition = "treatment", treat = "EGF",
zeroTreat = "1stCrtl", timeRange = c("20min","40min", "6h"))

Description

calcKinaseScore calculates kinase activity scores based on input data and a specified network of regulatory relationships (decoupler network).

Usage

calcKinaseScore(
  resTab,
  decoupler_network,
  corrThreshold = 0.9,
  statType = c("stat", "log2FC"),
  nPerm = 100
)

Arguments

Details

The function performs the following steps:

1. Removes duplicate rows based on the site column.

2. Filters the data to include only those sites present in the target column of the decoupler network.

3. Prepares the input table based on the specified statType.

4. Intersects the input table with the decoupler network to find common regulons.

5. Checks for correlated regulons and filters out those exceeding the correlation threshold.

6. Calculates kinase activity using a weighted mean approach.

7. Processes the results to handle NA values and formats the output.

Value

A data frame with kinase activity scores, including columns for 'source', 'score', and 'p_value'.

Examples

resTab <- data.frame(
site = c("EGFR_Y1172", "EGFR_Y1197", "EGFR_S1166", "ROCK2_S1374",
"WASL_Y256", "GAB1_Y259", "ADD1_S586", "EPHA2_Y772", "PRKDC_T2638",
"PRKDC_T2609", "PRKDC_S2612"),
stat = c(-10.038770, -5.945562, 5.773384, -7.303834, 5.585326, 5.971104,
5.199119, -5.169500, 5.130228, 5.407387, 4.493933),
log2FC = c(-2.6113343, -2.4858615, 1.0056629, -1.1561780, 1.6421145,
2.0296634, 1.3766283, -0.8531656, 1.0742881, 1.0042942, 1.0608129)
)
decoupler_network <- data.frame(
source = c(rep("ABL1", 5), rep("CDK2", 6)),
mor = c(rep(1, 11)),
target = c("EGFR_Y1172", "EGFR_Y1197", "EGFR_S1166", "ROCK2_S1374",
"WASL_Y256", "GAB1_Y259", "ADD1_S586", "EPHA2_Y772", "PRKDC_T2638",
"PRKDC_T2609", "PRKDC_S2612"),
likelihood = c(rep(1, 11))
)
# Call the function
calcKinaseScore(resTab, decoupler_network)

Description

checkRatioMat checks the ratio matrix for samples that do not have sufficient overlap of phospho-peptides between enriched (PP) and unenriched (FP) samples.

Usage

checkRatioMat(ratioMat, minOverlap = 3)

Arguments

Value

A character vector of sample names that do not meet the overlap criteria.

Description

clusterEnrich performs enrichment analysis on gene clusters, using Fisher's Exact Test to determine the significance of enrichment for each cluster.

Usage

clusterEnrich(
  clusterTab,
  se,
  inputSet,
  reference = NULL,
  ptm = FALSE,
  adj = "BH",
  filterP = 0.05,
  ifFDR = FALSE
)

Arguments

Details

The function first retrieves or computes the reference set of genes or PTM sites. It then performs enrichment analysis for each cluster using the runFisher function. The results are filtered based on the p-value threshold and adjusted for multiple testing if ifFDR is TRUE. The function generates a dot plot where the size and color of the points represent the significance of enrichment.

Value

A list containing two elements:

• 'table': A data frame with enrichment results for each cluster and pathway.

• 'plot': A ggplot2 object showing the significance of enrichment for each pathway across clusters.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
seProcess <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
result <- addZeroTime(seProcess, condition = "treatment", treat = "EGF",
zeroTreat = "1stCrtl", timeRange = c("20min","40min", "6h"))
# Get the numeric matrix
exprMat <- SummarizedExperiment::assay(result)
# Call the clustering function
clust <- clusterTS(x = exprMat, k = 3)
genesetPath <- appDir <- system.file("shiny-app/geneset",
package = "SmartPhos")
inGMT <- piano::loadGSC(paste0(genesetPath,
"/Cancer_Hallmark.gmt"),type="gmt")
# Call the function
clusterEnrich(clust$cluster, seProcess, inGMT)

Description

clusterTS performs clustering on time-series data and generates plots for visualization.

Usage

clusterTS(x, k = 5, pCut = NULL, twoCondition = FALSE)

Arguments

Details

The function performs the following steps:

1. Sets a seed for reproducibility.

2. Removes rows with missing values.

3. Performs clustering using fuzzy C-means.

4. Filters clusters based on the probability cutoff if provided.

5. Generates plots for visualizing clustering results.

Value

A list containing:

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
seProcess <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
result <- addZeroTime(seProcess, condition = "treatment", treat = "EGF",
zeroTreat = "1stCrtl", timeRange = c("20min","40min", "6h"))
# Get the numeric matrix
exprMat <- assay(result)
# Call the function
clusterTS(x = exprMat, k = 3)

Description

A sample of Data Dependent Acquisition (DDA) mass spectrometry data from Max Quant.

Usage

data(dda_example)

Format

a class S4 object of MultiAssayExperiment

Value

A MultiAssayExperiment object containing a sample of DDA mass spectrometry data from Max Quant.

Examples

data(dda_example)

Description

A sample of Data Independent Acquisition (DIA) mass spectrometry data from Spectronaut.

Usage

data(dia_example)

Format

a class S4 object of MultiAssayExperiment

Value

A MultiAssayExperiment object containing a sample of DIA mass spectrometry data from Spectronaut.

Examples

data(dia_example)

Description

enrichDifferential performs enrichment analysis on differentially expressed genes and phospho-sites for either pathway or phospho-specific enrichment, depending on the input parameters. It supports multiple statistical methods such as PAGE and GSEA for pathway enrichment and a Kolmogorov-Smirnov approach for phospho-enrichment.

Usage

enrichDifferential(
  dea,
  type = c("Pathway enrichment", "Phospho-signature enrichment"),
  gsaMethod = c("PAGE", "GSEA"),
  geneSet,
  ptmSet,
  statType = c("stat", "log2FC"),
  nPerm = 100,
  sigLevel = 0.05,
  ifFDR = FALSE
)

Arguments

Details

The 'enrichDifferential' function performs either pathway enrichment or phospho-enrichment analysis based on the 'type' parameter. For pathway enrichment, it uses either the PAGE or GSEA method with a provided gene set collection. For phospho-enrichment, it uses a Kolmogorov-Smirnov test with a PTM set database. Results can be filtered by significance level and optionally adjusted for FDR.

Value

A data frame containing the results of the enrichment analysis, including columns such as the gene set name, statistical significance, and adjusted p-values.

Examples

library(SummarizedExperiment)
library(piano)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Preprocess the proteome assay
result <- preprocessPhos(se, normalize = TRUE)
# Call the function to perform differential expression analyis
dea <- performDifferentialExp(se = result, assay = "Intensity",
method = "limma", reference = "1stCrtl", target = "EGF",
condition = "treatment")
# Load the gene set
genesetPath <- appDir <- system.file("shiny-app/geneset",
package = "SmartPhos")
inGMT <- loadGSC(paste0(genesetPath,"/Cancer_Hallmark.gmt"),type="gmt")
# Call the function
resTab <- enrichDifferential(dea = dea$resDE, type = "Pathway enrichment",
gsaMethod = "PAGE", geneSet = inGMT, statType = "stat", nPerm = 200,
sigLevel = 0.05, ifFDR = FALSE)

Description

generateInputTable generates an input table for proteomic and phosphoproteomic analysis by reading files from a specified folder.

Usage

generateInputTable(rawFolder, batchAsFolder = FALSE)

Arguments

Details

The function performs the following steps:

• Optionally treats subdirectories as separate batches.

• Reads the summary file containing experimental information.

• Generates unique experimental IDs based on batch and sample names.

• Processes file paths for full proteome and phosphoproteome data.

• Creates a combined input table with file names, sample names, search types, batches, and IDs.

Value

A data.frame with columns fileName, sample, searchType, batch, and id that can be used as input for further analysis.

Description

generateInputTable_DIA generates an input table for DIA analysis by reading files from a specified folder.

Usage

generateInputTable_DIA(rawFolder)

Arguments

Details

The function performs the following steps:

• Reads the summary file containing experimental information.

• Generates unique experimental IDs based on sample type, treatment, timepoint, and replicate.

• Processes file paths for full proteome and phosphoproteome data.

• Creates a combined input table with file names, search types, and IDs.

Value

A data.frame with columns fileName, searchType, and id that can be used as input for further analysis.

Description

getDecouplerNetwork loads the kinase-substrate interaction network for a specified species from pre-defined files.

Usage

getDecouplerNetwork(speciesRef = c("Homo sapiens", "Mus musculus"))

Arguments

Value

A data frame containing the kinase-substrate interaction network for the specified species.

Examples

# Load the human kinase-substrate interaction network
getDecouplerNetwork("Homo sapiens")

# Load the mouse kinase-substrate interaction network
getDecouplerNetwork("Mus musculus")

Description

getOneSymbol extracts the last gene symbol from a semicolon-separated list of gene symbols.

Usage

getOneSymbol(Gene)

Arguments

Details

This function processes a character vector where each element consists of gene symbols separated by semicolons. It splits each element by semicolons and extracts the last gene symbol from the resulting list. The output is a character vector of these last gene symbols.

Value

A character vector containing the last gene symbol from each element of the input vector.

Description

getRatioMatrix calculates the ratio matrix of phosphoproteome data from a MultiAssayExperiment object.

Usage

getRatioMatrix(maeData, normalization = FALSE, getAdjustedPP = FALSE)

Arguments

Value

A numeric matrix representing the ratio of intensity of PP (phosphoproteome) data to FP (full proteome) data.

Examples

# Load multiAssayExperiment object
data("dia_example")
# Call the function
getRatioMatrix(dia_example, normalization = TRUE)

Description

A prior knowledge database about the known kinase-phosphosite interactions for Homo sapiens

Usage

data(Homo_sapien_kinase_substrate_network)

Format

a data.frame object

Value

A data frame containing the information about the known kinase-phosphosite interactions for Homo sapiens.

Examples

data(Homo_sapien_kinase_substrate_network)

Description

intensityBoxPlot creates a boxplot for the Intensity data of a given gene or feature, with optional subject-specific lines.

Usage

intensityBoxPlot(se, id, symbol)

Arguments

Details

This function generates a boxplot for the intensity data of a specified gene or feature from a SummarizedExperiment (SE) object. The plot shows the distribution of normalized intensities across different groups specified in the comparison column of the SE object.

The function can handle both grouped data and repeated measures: - If the SE object does not contain a subjectID column, the function plots a standard boxplot grouped by the comparison column. - If the SE object contains a subjectID column, the function adds lines connecting the points for each subject across the groups, providing a visual indication of subject-specific changes.

The boxplot is customized with various aesthetic elements, such as box width, transparency, point size, axis labels, and title formatting.

Value

A ggplot2 object representing the boxplot of the intensity data.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Proteome"]]
colData(se) <- colData(dda_example)
# Preprocess the proteome assay
result <- preprocessProteome(se, normalize = TRUE)
# Call the function to perform differential expression analyis
de <- performDifferentialExp(se = result, assay = "Intensity",
method = "limma", reference = "1stCrtl", target = "EGF",
condition = "treatment")
# Plot the box plot for the given id and symbol
intensityBoxPlot(de$seSub, "p99", "PPP6C")

Description

makeSmartPhosDirectory creates a directory for the SmartPhos shiny app, and copies the necessary Shiny app files into the newly created directory.

Usage

makeSmartPhosDirectory(path)

Arguments

Details

The function first creates the main directory at the specified path and a subdirectory named '"save"' for storing MultiAssayExperiment object. It then locates the Shiny application files from the SmartPhos package and copies them into the new directory.

Value

None (invisible NULL). The function creates the necessary directories and copies files.

Examples

makeSmartPhosDirectory("shinyApp")

Description

medianNorm normalizes the columns of a matrix by either the median or the mean.

Usage

medianNorm(x, method = "median")

Arguments

Details

The function performs the following steps:

1. If the method is "median", it calculates the median of each column and adjusts by the overall median of these medians.

2. If the method is "mean", it calculates the mean of each column and adjusts by the overall mean of these means.

3. It constructs a matrix of these adjusted values and subtracts it from the original matrix to normalize the columns.

Value

A numeric matrix with normalized columns.

Examples

# Example usage:
x <- matrix(rnorm(20), nrow=5, ncol=4)
medianNorm(x, method = "median")

Description

mscale scales and centers each row of a matrix, with options for using mean or median, standard deviation or mean absolute deviation, and censoring extreme values.

Usage

mscale(x, center = TRUE, scale = TRUE, censor = NULL, useMad = FALSE)

Arguments

Details

The function allows for flexible scaling and centering of the rows of a matrix:

• If both center and scale are TRUE, rows are centered and scaled.

• If only center is TRUE, rows are centered but not scaled.

• If only scale is TRUE, rows are scaled but not centered.

• If neither center nor scale is TRUE, the original matrix is returned.

The function can also censor extreme values, either symmetrically or asymmetrically, based on the censor parameter.

Value

A scaled and centered numeric matrix with the same dimensions as the input matrix 'x'.

Examples

# Create a sample matrix (3 rows by 5 columns)
sample_matrix <- matrix(c(1:15), nrow = 3, byrow = TRUE)

# Scale and center the matrix using the default settings
mscale(sample_matrix, center = TRUE, scale = TRUE)

# Only center the matrix without scaling
mscale(sample_matrix, center = TRUE, scale = FALSE)

# Only scale the matrix without centering
mscale(sample_matrix, center = FALSE, scale = TRUE)

Description

A prior knowledge database about the known kinase-phosphosite interactions for Mus musculus

Usage

data(Mus_musculus_kinase_substrate_network)

Format

a data.frame object

Value

A data frame containing the information about the known kinase-phosphosite interactions for Mus musculus.

Examples

data(Mus_musculus_kinase_substrate_network)

Description

normByFullProteome normalizes the phosphoproteome data by the corresponding full proteome data in a MultiAssayExperiment object. The "Phosphoproteome" assay in the MultiAssayExperiment will be replaced by the ratio.

Usage

normByFullProteome(mae, replace = TRUE)

Arguments

Details

The function performs the following steps:

• Checks if both phosphoproteome and proteome assays are present in the MultiAssayExperiment object.

• Extracts the phosphoproteome and proteome assays along with the sample annotations.

• Matches the samples between the phosphoproteome and proteome assays.

• Normalizes the phosphoproteome data by dividing it by the corresponding proteome data.

• Replaces the phosphoproteome assay in the MultiAssayExperiment object or adds the normalized data as a new assay, depending on the replace parameter.

Value

A MultiAssayExperiment object with the normalized phosphoproteome data.

Examples

# load mae object
data("dia_example")
# call the function
normByFullProteome(dia_example)

Description

performCombinedNormalization performs combined normalization on proteome and phosphoproteome data from a MultiAssayExperiment object.

Usage

performCombinedNormalization(maeData)

Arguments

Details

The function performs the following steps:

1. Extracts the count matrices for Full Proteome (FP) samples.

2. Combines the proteome and phosphoproteome data into a single matrix.

3. Removes rows with all NA values.

4. Performs median normalization and log2 transformation on the combined matrix.

Value

A numeric matrix with normalized and log2-transformed data.

Examples

# Load multiAssayExperiment object
data("dia_example")
# Call the function
performCombinedNormalization(dia_example)

Description

performDifferentialExp performs differential expression analysis on a given SummarizedExperiment object using either the limma or ProDA method.

Usage

performDifferentialExp(
  se,
  assay,
  method = c("limma", "ProDA"),
  condition = NULL,
  reference,
  target,
  refTime = NULL,
  targetTime = NULL,
  pairedTtest = FALSE
)

Arguments

Details

This function is designed to facilitate differential expression analysis on a SummarizedExperiment (SE) object. The function allows users to specify various parameters to tailor the analysis to their specific experimental setup.

The main steps of the function are as follows:

1. Sample Selection: Based on the provided condition, reference, and target arguments, the function identifies the relevant samples for the analysis. If time points (refTime and targetTime) are provided, it further refines the sample selection.

2. Subsetting the SE Object: The SE object is subsetted to include only the selected samples. A new column comparison is added to the colData, indicating whether each sample belongs to the reference or target group.

3. Design Matrix Construction: The function constructs a design matrix for the differential expression analysis. If the SE object contains a subjectID column, this is included in the design to account for repeated measures or paired samples.

4. Differential Expression Analysis: Depending on the specified method, the function performs the differential expression analysis using either the limma or ProDA package: - Limma: The function fits a linear model to the expression data and applies empirical Bayes moderation to the standard errors. The results are then extracted and formatted. - ProDA: The function fits a probabilistic dropout model to the expression data and tests for differential expression. The results are then extracted and formatted.

5. Result Formatting: The differential expression results are merged with the metadata from the SE object, and the resulting table is formatted into a tibble. The table includes columns for log2 fold change (log2FC), test statistic (stat), p-value (pvalue), adjusted p-value (padj), and gene/feature ID (ID).

The function returns a list containing the formatted differential expression results and the subsetted SE object. This allows users to further explore or visualize the results as needed.

Value

A list containing:

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Proteome"]]
colData(se) <- colData(dda_example)
# Preprocess the proteome assay
result <- preprocessProteome(se, normalize = TRUE)
# Call the function to perform differential expression analyis
performDifferentialExp(se = result, assay = "Intensity", method = "limma",
reference = "1stCrtl", target = "EGF", condition = "treatment")

Description

plotAdjustmentResults generates plots to visualize the results of phosphoproteome adjustment.

Usage

plotAdjustmentResults(maeData, normalization = FALSE)

Arguments

Details

The function performs the following steps:

1. Checks if the adjustment factor is present in the sample annotation.

2. Calculates the ratio matrix before and after adjustment.

3. Creates a trend line plot for features present in all samples.

4. Creates box plots of the PP/FP ratios and phosphorylation intensities before and after adjustment.

Value

A list containing:

Description

plotHeatmap generates a heatmap for intensity assay for different conditions, including top variants, differentially expressed genes, and selected time series clusters.

Usage

plotHeatmap(
  type = c("Top variant", "Differentially expressed", "Selected time series cluster"),
  se,
  data = NULL,
  top = 100,
  cutCol = 1,
  cutRow = 1,
  clustCol = TRUE,
  clustRow = TRUE,
  annotationCol = NULL,
  title = NULL
)

Arguments

Details

This function creates a heatmap using the Intensity assay from a SummarizedExperiment object. The heatmap can show the top variants based on standard deviation, differentially expressed genes, or selected time series clusters. Row normalization is performed, and the heatmap can include annotations based on specified metadata columns.

Value

A pheatmap object showing the heatmap of Intensity data.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Generate the imputed assay
result <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
# Plot heatmap for top variant
plotHeatmap(type = "Top variant", top = 10, se = result, cutCol = 2)

Description

plotIntensity generates boxplots of assay intensities for each sample in a SummarizedExperiment object. Optionally, the boxplots can be colored based on a specified metadata column. The function handles missing values by filtering them out before plotting.

Usage

plotIntensity(se, colorByCol = "none")

Arguments

Value

A ggplot2 object showing boxplots of intensities for each sample.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Preprocess the phosphoproteome assay
result <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
# Call the plotting function
plotIntensity(result, colorByCol = "replicate")

Description

'plotKinaseDE' generates a bar plot of the top kinases associated with the differentially expressed genes based on their scores.

Usage

plotKinaseDE(scoreTab, nTop = 10, pCut = 0.05)

Arguments

Details

The function performs the following steps:

1. Adds a column for significance based on the p-value cutoff.

2. Adds a column for the sign of the score.

3. Filters out kinases with a score of 0.

4. Selects the top nTop kinases by absolute score for each sign of the score.

5. Creates a bar plot with the selected kinases.

Value

A ggplot2 object representing the bar plot of kinase score.

Examples

# Example usage:
scoreTab <- data.frame(
 source = c("Kinase1", "Kinase2", "Kinase3", "Kinase4"),
 score = c(2.3, -1.5, 0, 3.1),
 p_value = c(0.01, 0.2, 0.05, 0.03)
)
plotKinaseDE(scoreTab, nTop = 3, pCut = 0.05)

Description

plotKinaseTimeSeries creates a heatmap to visualize the result of kinase activity inference for time-series clustering, with significant activity changes marked.

Usage

plotKinaseTimeSeries(scoreTab, pCut = 0.05, clusterName = "cluster1")

Arguments

Details

The heatmap shows kinase activity scores over different time points. Significant activities (based on the specified p-value threshold) are marked with an asterisk (*). The color gradient represents the activity score, with blue indicating low activity, red indicating high activity, and white as the midpoint.

Value

A ggplot2 object representing the heatmap of kinase activity score.

Examples

# Example usage:
scoreTab <- data.frame(
 timepoint = rep(c("0h", "1h", "2h"), each = 3),
 source = rep(c("KinaseA", "KinaseB", "KinaseC"), times = 3),
 score = runif(9, -2, 2),
 p_value = runif(9, 0, 0.1)
)
plotKinaseTimeSeries(scoreTab)

Description

plotLogRatio generates a boxplot of the log2 ratio of intensities of phosphoproteome to full proteome data from a MultiAssayExperiment object.

Usage

plotLogRatio(maeData, normalization = FALSE)

Arguments

Value

A ggplot2 object representing the boxplot of the log2 ratios.

Examples

# Load multiAssayExperiment object
data("dia_example")
# Call the function
plotLogRatio(dia_example, normalization = TRUE)

Description

plotMissing generates a bar plot showing the completeness (percentage of non-missing values) for each sample in a SummarizedExperiment object.

Usage

plotMissing(se)

Arguments

Details

This function calculates the percentage of non-missing values for each sample in the provided SummarizedExperiment object. It then generates a bar plot where each bar represents a sample, and the height of the bar corresponds to the completeness (percentage of non-missing values) of that sample.

Value

A ggplot2 object showing the percentage of completeness for each sample.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Phosphoproteome"]]
colData(se) <- colData(dda_example)
# Call the function
plotMissing(se)

Description

plotPCA generates a PCA plot using the results from a PCA analysis and a SummarizedExperiment object. The points on the plot can be colored and shaped based on metadata.

Usage

plotPCA(pca, se, xaxis = "PC1", yaxis = "PC2", color = "none", shape = "none")

Arguments

Details

This function creates a PCA plot using the scores from a PCA result object and metadata from a SummarizedExperiment object. The x-axis and y-axis can be customized to display different principal components, and the points can be optionally colored and shaped based on specified metadata columns.

Value

A ggplot2 object showing the PCA plot.

Examples

# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
SummarizedExperiment::colData(se) <- SummarizedExperiment::colData(
dia_example)
# Generate the imputed assay
result <- preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")
# Perform PCA
pcaResult <- stats::prcomp(t(
SummarizedExperiment::assays(result)[["imputed"]]),
center = TRUE, scale. = TRUE)
# Plot PCA results
plotPCA(pca = pcaResult, se = result, color = "treatment")

Description

plotTimeSeries plots time series data for a given gene or phospho site from a given SummarizedExperiment object, allowing different types of plots such as expression, log fold change, or two-condition expression.

Usage

plotTimeSeries(
  se,
  type = c("expression", "logFC", "two-condition expression"),
  geneID,
  symbol,
  condition,
  treatment,
  refTreat,
  addZero = FALSE,
  zeroTreat = NULL,
  timerange
)

Arguments

Details

This function generates time series plots for a specified gene or feature from a SummarizedExperiment (SE) object. The type of plot can be one of the following: - "expression": Plots normalized expression levels over time. - "logFC": Plots log fold change (logFC) over time, comparing a treatment to a reference treatment. - "two-condition expression": Plots normalized expression levels over time for two conditions.

The function can add a zero time point if specified and handles data with and without subject-specific information. The plot includes points for each time point and a summary line representing the mean value.

The x-axis represents time, and the y-axis represents the selected metric (normalized expression or logFC). The plot is customized with various aesthetic elements, such as point size, line type, axis labels, and title formatting.

Value

A ggplot2 object representing the time series plot.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Proteome"]]
colData(se) <- colData(dda_example)
# Preprocess the proteome assay
result <- preprocessProteome(se, normalize = TRUE)
# Plot a specific gene experssion over time
timerange <- unique(se$timepoint)
plotTimeSeries(result, type = "expression", geneID = "p18",
symbol = "TMEM238", condition = "treatment", treatment = "EGF",
timerange = timerange)

Description

plotVolcano generates a volcano plot to visualize differential expression results.

Usage

plotVolcano(tableDE, pFilter = 0.05, fcFilter = 0.5)

Arguments

Details

This function creates a volcano plot where differentially expressed genes are categorized as 'Up', 'Down', or 'Not Sig' based on the provided p-value and log2 fold-change thresholds. Points on the plot are color-coded to indicate their expression status.

Value

A ggplot2 object representing the volcano plot.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Proteome"]]
colData(se) <- colData(dda_example)
# Preprocess the proteome assay
result <- preprocessProteome(se, normalize = TRUE)
# Call the function to perform differential expression analyis
de <- performDifferentialExp(se = result, assay = "Intensity",
method = "limma", reference = "1stCrtl", target = "EGF",
condition = "treatment")
# Plot the volcano plot from the result
plotVolcano(de$resDE)

Description

preprocessPhos preprocesses phosphoproteome data stored in a SummarizedExperiment object by performing filtering, transformation, normalization, imputation, and batch effect removal.

Usage

preprocessPhos(
  seData,
  filterList = NULL,
  missCut = 50,
  transform = c("log2", "vst", "none"),
  normalize = FALSE,
  getFP = FALSE,
  removeOutlier = NULL,
  assayName = NULL,
  batch = NULL,
  scaleFactorTab = NULL,
  impute = c("none", "QRILC", "MLE", "bpca", "missForest", "MinDet"),
  verbose = FALSE
)

Arguments

Value

A SummarizedExperiment object with preprocessed phosphoproteome data.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Phosphoproteome"]]
colData(se) <- colData(dia_example)
# Call the function
preprocessPhos(seData = se, normalize = TRUE, impute = "QRILC")

Description

preprocessProteome preprocesses proteome data stored in a SummarizedExperiment object by performing filtering, transformation, normalization, imputation, and batch effect removal.

Usage

preprocessProteome(
  seData,
  filterList = NULL,
  missCut = 50,
  transform = c("log2", "vst", "none"),
  normalize = FALSE,
  getPP = FALSE,
  removeOutlier = NULL,
  impute = c("none", "QRILC", "MLE", "bpca", "missForest", "MinDet"),
  batch = NULL,
  verbose = FALSE,
  scaleFactorTab = NULL
)

Arguments

Value

A SummarizedExperiment object with preprocessed proteome data.

Examples

library(SummarizedExperiment)
# Load multiAssayExperiment object
data("dia_example")
# Get SummarizedExperiment object
se <- dia_example[["Proteome"]]
colData(se) <- colData(dia_example)
# Call the function
preprocessProteome(seData = se, normalize = TRUE, impute = "QRILC")

Description

readExperiment reads and processes DDA (Data-Dependent Acquisition) phosphoproteomic and proteomic data from a given file table, and returns a MultiAssayExperiment object.

Usage

readExperiment(
  fileTable,
  localProbCut = 0.75,
  scoreDiffCut = 5,
  fdrCut = 0.1,
  scoreCut = 10,
  pepNumCut = 1,
  ifLFQ = TRUE,
  annotation_col = c(),
  verbose = FALSE
)

Arguments

Details

The function performs the following steps:

• Reads and processes the phosphoproteomic data using the readPhosphoExperiment function.

• Reads and processes the proteomic data using the readProteomeExperiment function.

• Prepares the sample annotation table.

• Constructs and returns a MultiAssayExperiment object containing the processed data.

Value

A MultiAssayExperiment object containing the processed phosphoproteomic and proteomic data from a DDA experiment.

Examples

# Example usage:
file1 <- system.file("extdata", "phosDDA_1.xls", package = "SmartPhos")
file2 <- system.file("extdata", "proteomeDDA_1.xls", package = "SmartPhos")
# Create fileTable
fileTable <- data.frame(
  searchType = c("phosphoproteome", "proteome"),
  fileName = c(file1, file2),
  sample = c("Sample1", "sample1"),
  id = c("s1", "s2")
)
# Call the function
readExperiment(fileTable, localProbCut = 0.75, scoreDiffCut = 5,
fdrCut = 0.1, scoreCut = 10, pepNumCut = 1, ifLFQ = TRUE,
annotation_col = c("id"))

Description

readExperimentDIA reads and processes DIA (Data-Independent Acquisition) data for both phosphoproteome and proteome experiments, and constructs a MultiAssayExperiment object.

Usage

readExperimentDIA(
  fileTable,
  localProbCut = 0.75,
  annotation_col = c(),
  onlyReviewed = TRUE,
  normalizeByProtein = FALSE,
  verbose = FALSE
)

Arguments

Details

The function performs the following steps:

• Reads and processes phosphoproteomic data using readPhosphoExperimentDIA.

• Reads and processes proteomic data using readProteomeExperimentDIA.

• Prepares sample annotations based on the provided fileTable and annotation_col.

• Constructs a MultiAssayExperiment object with the processed data and sample annotations.

The readPhosphoExperimentDIA and readProteomeExperimentDIA functions are used to read and filter the data for phosphoproteome and proteome experiments, respectively, and they must be available in the environment.

Value

A MultiAssayExperiment object containing the processed phosphoproteome and proteome data.

Examples

# Example usage:
file1 <- system.file("extdata", "phosDIA_1.xls", package = "SmartPhos")
file2 <- system.file("extdata", "proteomeDIA_1.xls", package = "SmartPhos")
# Create fileTable
fileTable <- data.frame(
  searchType = c("phosphoproteome", "proteome", "proteome"),
  fileName = c(file1, file2, file2),
  id = c("Sample_1", "sample1", "sample2"),
  outputID = c("s1", "s2", "s3")
)
# Call the function
readExperimentDIA(fileTable, localProbCut = 0.75, annotation_col = c("id"),
onlyReviewed = FALSE, normalizeByProtein = FALSE)

Description

readOnePhos reads phosphorylation data from an input table, filters it based on localization probability, score difference, and intensity, and returns the filtered data for a specific sample.

Usage

readOnePhos(
  inputTab,
  sampleName,
  localProbCut = 0.75,
  scoreDiffCut = 5,
  multiMap
)

Arguments

Details

The function filters the input phosphorylation data based on three criteria: localization probability, score difference, and intensity. Only rows that meet or exceed the specified cutoffs for these criteria and have non-zero intensity are retained. The filtered data is then returned with a unique identifier for each row.

Value

A data.frame containing the filtered phosphorylation data for the specified sample, with columns for intensity, Uniprot ID, gene name, position within proteins, amino acid residue, and sequence window.

Description

readOnePhosDIA reads and processes phosphorylation data for a single sample from a DIA experiment, applying filters for localization probability and removing duplicates if specified.

Usage

readOnePhosDIA(inputTab, sampleName, localProbCut = 0.75, removeDup = FALSE)

Arguments

Details

This function processes phosphorylation data for a single sample by filtering based on localization probability and non-zero intensity. It handles multiplicity by summarizing intensities and optionally removes duplicates. The resulting data is returned as a data.table with unique identifiers.

Value

A data.table containing the processed phosphorylation data for the specified sample.

Description

readOneProteom reads and processes proteomics data for a single sample, applying filters for peptide count and optionally using LFQ quantification. It returns a data.table with useful columns and unique identifiers.

Usage

readOneProteom(inputTab, sampleName, pepNumCut = 1, ifLFQ = TRUE)

Arguments

Details

This function processes proteomics data for a single sample by filtering based on the number of peptides and optionally using LFQ quantification. It ensures that unique identifiers are created for each protein, and removes rows with missing or zero quantification values.

Value

A data.table with the processed proteomics data, including columns for intensity, Uniprot ID, peptide counts, and gene names.

Description

readOneProteomDIA reads and processes data from a single DIA proteomics sample, applying filtering and data transformation steps.

Usage

readOneProteomDIA(inputTab, sampleName)

Arguments

Details

This function processes DIA proteomics data for a single sample by filtering out rows with non-quantitative data, converting character values to numeric, and renaming columns for consistency. It also ensures that each protein group has a unique identifier.

Value

A data.table containing the processed data with columns for intensity, UniProt ID, and gene name.

Description

readPhosphoExperiment reads and processes phosphorylation experiment data from multiple files, filtering based on localization probability and score difference, and constructs a SummarizedExperiment object.

Usage

readPhosphoExperiment(fileTable, localProbCut = 0.75, scoreDiffCut = 5)

Arguments

Details

This function reads phosphorylation data from multiple files as specified in fileTable, filters the data based on localization probability and score difference, and removes reverse and potential contaminant entries. It constructs an intensity matrix and annotation data, which are then used to create a SummarizedExperiment object.

Value

A SummarizedExperiment object containing the processed phosphorylation data.

Examples

file1 <- system.file("extdata", "phosDDA_1.xls", package = "SmartPhos")
file2 <- system.file("extdata", "proteomeDDA_1.xls", package = "SmartPhos")
# Create fileTable
fileTable <- data.frame(
   searchType = c("phosphoproteome", "proteome"),
   fileName = c(file1, file2),
   sample = c("Sample1", "sample1"),
   id = c("s1", "s2")
)
# Call the function
readPhosphoExperiment(fileTable, localProbCut = 0.75, scoreDiffCut = 5)

Description

readPhosphoExperimentDIA reads and processes phosphorylation data from DIA experiments, applying filters for localization probability, and optionally including only reviewed proteins. It constructs a SummarizedExperiment object.

Usage

readPhosphoExperimentDIA(
  fileTable,
  localProbCut = 0.75,
  onlyReviewed = TRUE,
  showProgressBar = FALSE
)

Arguments

Details

This function processes phosphorylation data from DIA experiments by filtering based on localization probability and non-zero intensity, handling multiplicity, and optionally including only reviewed proteins. The resulting data is returned as a SummarizedExperiment object with annotations and an intensity matrix.

Value

A SummarizedExperiment object containing the processed phosphorylation data.

Examples

file <- system.file("extdata", "phosDIA_1.xls", package = "SmartPhos")
fileTable <- data.frame(searchType = "phosphoproteome", fileName = file,
id = c("Sample_1"))
readPhosphoExperimentDIA(fileTable, localProbCut = 0.75,
onlyReviewed = FALSE, showProgressBar = FALSE)

Description

readProteomeExperiment reads and processes proteomics data from multiple samples, applying various quality filters, and returns a SummarizedExperiment object.

Usage

readProteomeExperiment(
  fileTable,
  fdrCut = 0.1,
  scoreCut = 10,
  pepNumCut = 1,
  ifLFQ = TRUE
)

Arguments

Details

This function processes proteomics data by filtering based on FDR, score, and peptide count, and optionally using LFQ quantification. It aggregates the data from multiple samples and constructs a SummarizedExperiment object.

Value

A SummarizedExperiment object containing the processed proteomics data.

Examples

file1 <- system.file("extdata", "phosDDA_1.xls", package = "SmartPhos")
file2 <- system.file("extdata", "proteomeDDA_1.xls", package = "SmartPhos")
# Create fileTable
fileTable <- data.frame(
   searchType = c("phosphoproteome", "proteome"),
   fileName = c(file1, file2),
   sample = c("Sample1", "sample1"),
   id = c("s1", "s2")
)
# Call the function
readProteomeExperiment(fileTable, fdrCut = 0.1, scoreCut = 10,
pepNumCut = 1, ifLFQ = TRUE)

Description

readProteomeExperimentDIA reads and processes DIA (Data-Independent Acquisition) proteome data from multiple files and constructs a SummarizedExperiment object.

Usage

readProteomeExperimentDIA(fileTable, showProgressBar = FALSE)

Arguments

Value

A SummarizedExperiment object containing the processed proteome data. #' @details The function performs the following steps:

• Filters the 'fileTable' to include only rows where 'searchType' is "proteome".

• For each file specified in 'fileTable', reads the data using 'data.table::fread'.

• Removes rows where the 'PG.ProteinGroups' column is NA or empty.

• Processes each sample in parallel using 'BiocParallel::bplapply', applying the 'readOneProteomDIA' function to filter and clean the data for each sample.

• Combines the processed data from all files.

• Constructs a matrix of intensities with rows corresponding to proteins and columns corresponding to samples.

• Constructs a 'SummarizedExperiment' object with the intensity matrix and protein annotations.

The readOneProteomDIA function is used to read and filter the data for each individual sample, and it must be available in the environment.

Examples

file <- system.file("extdata", "proteomeDIA_1.xls", package = "SmartPhos")
fileTable <- data.frame(searchType = "proteome", fileName = file,
id = c("sample1", "sample2"))
readProteomeExperimentDIA(fileTable)

Description

runFisher performs Fisher's Exact Test to determine the enrichment of a set of genes within reference gene sets.

Usage

runFisher(genes, reference, inputSet, ptm = FALSE)

Arguments

Details

The function can operate in two modes: standard gene sets and PTM-specific gene sets. For PTM-specific gene sets, additional filtering and processing are performed.

Value

A data frame with the results of the Fisher's Exact Test, including the gene set name, the number of genes in the set, set size, p-value, adjusted p-value, and the genes in the set.

Examples

library(SummarizedExperiment)
library(piano)
# Load multiAssayExperiment object
data("dda_example")
# Get SummarizedExperiment object
se <- dda_example[["Proteome"]]
colData(se) <- colData(dda_example)
# Preprocess the proteome assay
result <- preprocessProteome(se, normalize = TRUE)
# Call the function to perform differential expression analyis
de <- performDifferentialExp(se = result, assay = "Intensity",
method = "limma", reference = "1stCrtl", target = "EGF",
condition = "treatment")
genesList <- unique(de$resDE$Gene)
referenceList <- unique(SummarizedExperiment::rowData(result)$Gene)
genesetPath <- appDir <- system.file("shiny-app/geneset",
package = "SmartPhos")
inGMT <- loadGSC(paste0(genesetPath,"/Cancer_Hallmark.gmt"),type="gmt")
# Run the function
runFisher(genes = genesList, reference = referenceList, inputSet = inGMT)

Description

runGSEAforPhospho performs Gene Set Enrichment Analysis (GSEA) for phosphorylation data.

Usage

runGSEAforPhospho(
  geneStat,
  ptmSetDb,
  nPerm,
  weight = 1,
  correl.type = c("rank", "symm.rank", "z.score"),
  statistic = c("Kolmogorov-Smirnov", "area.under.RES"),
  min.overlap = 5
)

Arguments

Details

This function runs GSEA on phosphorylation data to identify enriched PTM sets. It calculates enrichment scores and p-values for each set, normalizes the scores, and adjusts p-values for multiple testing.

Value

A tibble with enrichment scores and associated statistics for each PTM set.

Description

runPhosphoAdjustment performs phospho adjustment on a MultiAssayExperiment object to normalize the phosphoproteome data.

Usage

runPhosphoAdjustment(
  maeData,
  normalization = FALSE,
  minOverlap = 3,
  completeness = 0,
  ncore = 1
)

Arguments

Details

The function performs the following steps:

1. Defines an optimization function to minimize the sum of squared differences between pairs of samples.

2. Calculates the ratio matrix of phosphoproteome to full proteome data.

3. Subsets features based on completeness criteria.

4. Performs a sanity check to identify and exclude problematic samples.

5. Sets initial values for the adjustment factor based on column medians.

6. Estimates the adjustment factor using parallel optimization.

7. Adjusts the phosphoproteome measurements using the estimated adjustment factor.

Value

A MultiAssayExperiment object with adjusted phosphoproteome data.

Description

runSmartPhos launches the SmartPhos Shiny application, which provides an interactive interface for analyzing phosphoproteomic data.

Usage

runSmartPhos()

Details

The runSmartPhos function locates the Shiny app directory within the SmartPhos package and launches the application. If the app directory cannot be found, the function will stop and prompt the user to re-install the SmartPhos package.

Value

The function does not return a value; it starts the Shiny application for SmartPhos.

Examples

# To run the SmartPhos Shiny application, simply call:
# runSmartPhos()

Description

splineFilter filters an expression matrix based on spline models fitted to time-series data, optionally considering treatment and subject ID.

Usage

splineFilter(
  exprMat,
  subjectID = NULL,
  time,
  df,
  pCut = 0.5,
  ifFDR = FALSE,
  treatment = NULL,
  refTreatment = NULL
)

Arguments

Details

The function performs the following steps:

1. Converts time points from minutes to hours if both units are present.

2. Removes rows with missing values from the expression matrix.

3. Constructs a design matrix for the spline model, optionally including subject IDs and treatments.

4. Fits a linear model using the design matrix and performs empirical Bayes moderation.

5. Extracts significant features based on the specified p-value or FDR cutoff.

Value

A filtered expression matrix containing only the features that meet the significance criteria.

Description

This is a high-quality, manually curated protein sequence database which provides a high level of annotations (such as the description of the function of a protein, structure of its domains, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases.

Usage

data(swissProt)

Format

an object of "tbl_df" (tidy table)

Value

A data frame or tibble containing high-level annotations for manually curated proteins.

Examples

data(swissProt)