| Title: | Differential Enrichment analysis of Proteomics data |
|---|---|
| Description: | This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. |
| Authors: | Arne Smits [cre, aut], Wolfgang Huber [aut] |
| Maintainer: | Arne Smits <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.29.0 |
| Built: | 2024-11-18 03:41:47 UTC |
| Source: | https://github.com/bioc/DEP |
add_rejections marks significant proteins based on defined cutoffs.
add_rejections(diff, alpha = 0.05, lfc = 1)add_rejections(diff, alpha = 0.05, lfc = 1)
diff |
SummarizedExperiment,
Proteomics dataset on which differential enrichment analysis
has been performed (output from |
alpha |
Numeric(1), Sets the threshold for the adjusted P value. |
lfc |
Numeric(1), Sets the threshold for the log2 fold change. |
A SummarizedExperiment object annotated with logical columns indicating significant proteins.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1)
analyze_dep tests for differential expression of proteins
based on protein-wise linear models and empirical Bayes
statistics using limma.
analyze_dep(se, type = c("all", "control", "manual"), control = NULL, alpha = 0.05, lfc = 1, test = NULL, design_formula = formula(~0 + condition))analyze_dep(se, type = c("all", "control", "manual"), control = NULL, alpha = 0.05, lfc = 1, test = NULL, design_formula = formula(~0 + condition))
se |
SummarizedExperiment,
Proteomics data with unique names and identifiers
annotated in 'name' and 'ID' columns.
Additionally, the colData should contain sample annotation including
'label', 'condition' and 'replicate' columns.
The appropriate columns and objects can be generated
using |
type |
"all", "control" or "manual", The type of contrasts that will be tested. This can be all possible pairwise comparisons ("all"), limited to the comparisons versus the control ("control"), or manually defined contrasts ("manual"). |
control |
Character(1), The condition to which contrasts are generated (a control condition would be most appropriate). |
alpha |
Numeric(1), Sets the threshold for the adjusted P value. |
lfc |
Numeric(1), Sets the threshold for the log2 fold change. |
test |
Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC"). |
design_formula |
Formula, Used to create the design matrix. |
A SummarizedExperiment object containing FDR estimates of differential expression and logical columns indicating significant proteins.
# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import and process data se <- import_MaxQuant(data, exp_design) processed <- process(se) # Differential protein expression analysis dep <- analyze_dep(processed, "control", "Ctrl") dep <- analyze_dep(processed, "control", "Ctrl", alpha = 0.01, lfc = log2(1.5)) dep <- analyze_dep(processed, "manual", test = c("Ubi6_vs_Ubi4"))# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import and process data se <- import_MaxQuant(data, exp_design) processed <- process(se) # Differential protein expression analysis dep <- analyze_dep(processed, "control", "Ctrl") dep <- analyze_dep(processed, "control", "Ctrl", alpha = 0.01, lfc = log2(1.5)) dep <- analyze_dep(processed, "manual", test = c("Ubi6_vs_Ubi4"))
This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also entails wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.
run_app: Shiny apps for interactive analysis.
LFQ:
Label-free quantification (LFQ) workflow wrapper.
TMT:
Tandem-mass-tags (TMT) workflow wrapper.
report:
Create a rmarkdown report wrapper.
import_MaxQuant:
Import data from MaxQuant into a SummarizedExperiment object.
import_IsobarQuant:
Import data from IsobarQuant into a SummarizedExperiment object.
process:
Perform filtering, normalization and imputation on protein data.
analyze_dep:
Differential protein expression analysis.
plot_all:
Visualize the results in different types of plots.
make_unique:
Generate unique names.
make_se_parse:
Turn data.frame into SummarizedExperiment by parsing column names.
make_se:
Turn data.frame into SummarizedExperiment using an experimental design.
filter_proteins:
Filter proteins based on missing values.
normalize_vsn:
Normalize data using vsn.
impute:
Impute missing values.
test_diff:
Differential enrichment analysis.
add_rejections:
Mark significant proteins.
get_results:
Generate a results table.
plot_single:
Barplot for a protein of interest.
plot_volcano:
Volcano plot for a specified contrast.
plot_heatmap:
Heatmap of all significant proteins.
plot_normalization:
Boxplots to inspect normalization.
plot_detect:
Density and CumSum plots of proteins
with and without missing values.
plot_imputation:
Density plots to inspect imputation.
plot_missval:
Heatmap to inspect missing values.
plot_numbers:
Barplot of proteins identified.
plot_frequency:
Barplot of protein identification overlap between conditions.
plot_coverage:
Barplot of the protein coverage in conditions.
plot_pca:
PCA plot of top variable proteins.
plot_cor:
Plot correlation matrix.
plot_cor:
Plot Gower's distance matrix.
plot_p_hist:
P value histogram.
plot_cond_freq:
Barplot of the number of significant conditions per protein.
plot_cond_overlap:
Barplot of the number of proteins for overlapping conditions.
plot_cond:
Barplot of the frequency of significant conditions per protein
and the overlap in proteins between conditions.
get_df_wide:
Generate a wide data.frame from a SummarizedExperiment.
get_df_long:
Generate a long data.frame from a SummarizedExperiment.
se2msn:
SummarizedExperiment object to MSnSet object conversion.
filter_missval:
Filter on missing values.
manual_impute:
Imputation by random draws from a manually defined distribution.
get_prefix:
Obtain the longest common prefix.
get_suffix:
Obtain the longest common suffix.
UbiLength:
Ubiquitin interactors of different linear ubiquitin lengths
(UbIA-MS dataset) (Zhang, Smits, van Tilburg et al. Mol. Cell 2017).
UbiLength_ExpDesign:
Experimental design of the UbiLength dataset.
DiUbi:
Ubiquitin interactors for different diubiquitin-linkages
(UbIA-MS dataset) (Zhang, Smits, van Tilburg et al. Mol. Cell 2017).
DiUbi_ExpDesign:
Experimental design of the DiUbi dataset.
The DiUbi dataset contains label free quantification (LFQ) and intensity-based absolute quantification (iBAQ) data for ubiquitin interactors of different diubiquitin-linkages, generated by Zhang et al 2017. The dataset contains the proteingroups output file from MaxQuant.
DiUbiDiUbi
A data.frame with 4071 observations and 102 variables:
Uniprot IDs
Uniprot IDs of major protein(s) in the protein group
Full protein names
Gene name
Header as present in the Uniprot fasta file
Number of peptides identified for this protein group
Number of peptides used for the quantification of this protein group
Number of peptides identified which are unique for this protein group
Raw mass spectrometry intensity, A.U.
iBAQ normalized mass spectrometry intensity, A.U.
LFQ normalized mass spectrometry intensity, A.U.
The protein is only identified by a modification site if marked ('+')
The protein is identified in the decoy database if marked ('+')
The protein is a known contaminant if marked ('+')
The protein group ID
A data.frame.
Zhang, Smits, van Tilburg, et al (2017). An interaction landscape of ubiquitin signaling. Molecular Cell 65(5): 941-955. doi: 10.1016/j.molcel.2017.01.004.
The DiUbi_ExpDesign object annotates 30 different samples of the DiUbi dataset in 10 conditions and 3 replicates.
DiUbi_ExpDesignDiUbi_ExpDesign
A data.frame with 30 observations and 3 variables:
Label names
Experimental conditions
Replicate number
A data.frame.
Zhang, Smits, van Tilburg, et al (2017). An interaction landscape of ubiquitin signaling. Molecular Cell 65(5): 941-955. doi: 10.1016/j.molcel.2017.01.004.
filter_missval filters a proteomics dataset based on missing values.
The dataset is filtered for proteins that have a maximum of
'thr' missing values in at least one condition.
filter_missval(se, thr = 0)filter_missval(se, thr = 0)
se |
SummarizedExperiment,
Proteomics data (output from |
thr |
Integer(1), Sets the threshold for the allowed number of missing values in at least one condition. |
A filtered SummarizedExperiment object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter stringent_filter <- filter_missval(se, thr = 0) less_stringent_filter <- filter_missval(se, thr = 1)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter stringent_filter <- filter_missval(se, thr = 0) less_stringent_filter <- filter_missval(se, thr = 1)
filter_proteins filters a proteomic dataset based on missing values.
Different types of filtering can be applied, which range from only keeping
proteins without missing values to keeping proteins with a certain percent
valid values in all samples or keeping proteins that are complete
in at least one condition.
filter_proteins(se, type = c("complete", "condition", "fraction"), thr = NULL, min = NULL)filter_proteins(se, type = c("complete", "condition", "fraction"), thr = NULL, min = NULL)
se |
SummarizedExperiment,
Proteomics data (output from |
type |
"complete", "condition" or "fraction", Sets the type of filtering applied. "complete" will only keep proteins with valid values in all samples. "condition" will keep proteins that have a maximum of 'thr' missing values in at least one condition. "fraction" will keep proteins that have a certain fraction of valid values in all samples. |
thr |
Integer(1), Sets the threshold for the allowed number of missing values in at least one condition if type = "condition". |
min |
Numeric(1), Sets the threshold for the minimum fraction of valid values allowed for any protein if type = "fraction". |
A filtered SummarizedExperiment object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter stringent_filter <- filter_proteins(se, type = "complete") less_stringent_filter <- filter_proteins(se, type = "condition", thr = 0)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter stringent_filter <- filter_proteins(se, type = "complete") less_stringent_filter <- filter_proteins(se, type = "condition", thr = 0)
get_df_long generate a wide data.frame from a SummarizedExperiment.
get_df_long(se)get_df_long(se)
se |
SummarizedExperiment,
Proteomics data (output from |
A data.frame object containing all data in a wide format, where each row represents a single measurement.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get a long data.frame long <- get_df_long(dep) colnames(long)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get a long data.frame long <- get_df_long(dep) colnames(long)
get_df_wide generate a wide data.frame from a SummarizedExperiment.
get_df_wide(se)get_df_wide(se)
se |
SummarizedExperiment,
Proteomics data (output from |
A data.frame object containing all data in a wide format, where each row represents a protein.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get a wide data.frame wide <- get_df_wide(dep) colnames(wide)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get a wide data.frame wide <- get_df_wide(dep) colnames(wide)
get_prefix returns the longest common prefix
of the supplied words.
get_prefix(words)get_prefix(words)
words |
Character vector, A list of words. |
A character vector containing the prefix.
# Load example data <- UbiLength columns <- grep("LFQ.", colnames(data)) # Get prefix names <- colnames(data[, columns]) get_prefix(names)# Load example data <- UbiLength columns <- grep("LFQ.", colnames(data)) # Get prefix names <- colnames(data[, columns]) get_prefix(names)
get_results generates a results table from a proteomics dataset
on which differential enrichment analysis was performed.
get_results(dep)get_results(dep)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
A data.frame object containing all results variables from the performed analysis.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get results results <- get_results(dep) colnames(results) significant_proteins <- results[results$significant,] nrow(significant_proteins) head(significant_proteins)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Get results results <- get_results(dep) colnames(results) significant_proteins <- results[results$significant,] nrow(significant_proteins) head(significant_proteins)
get_suffix returns the longest common suffix
of the supplied words.
get_suffix(words)get_suffix(words)
words |
Character vector, A list of words. |
A character vector containing the suffix
# Get suffix names <- c("xyz_rep", "abc_rep") get_suffix(names)# Get suffix names <- c("xyz_rep", "abc_rep") get_suffix(names)
import_IsobarQuant imports a protein table from IsobarQuant
and converts it into a SummarizedExperiment object.
import_IsobarQuant(proteins, expdesign, intensities = "signal_sum", names = "gene_name", ids = "protein_id", delim = "[|]")import_IsobarQuant(proteins, expdesign, intensities = "signal_sum", names = "gene_name", ids = "protein_id", delim = "[|]")
proteins |
Data.frame, Protein table for which unique names will be created. |
expdesign |
Data.frame,
Experimental design with 'label', 'condition'
and 'replicate' information.
See |
intensities |
Character(1), Prefix of the columns containing sample intensities. |
names |
Character(1), Name of the column containing feature names. |
ids |
Character(1), Name of the column containing feature IDs. |
delim |
Character(1), Sets the delimiter separating the feature names within on protein group. |
A SummarizedExperiment object with log2-transformed values and "name" and "ID" columns containing unique names and identifiers.
## Not run: # Load data isobarquant_table <- read.csv("testfile.txt", header = TRUE, stringsAsFactors = FALSE, sep = "\t") exp_design <- read.csv("test_experimental_design.txt", header = TRUE, stringsAsFactors = FALSE, sep = "\t") # Import data se <- import_IsobarQuant(isabarquant_table, exp_design) ## End(Not run)## Not run: # Load data isobarquant_table <- read.csv("testfile.txt", header = TRUE, stringsAsFactors = FALSE, sep = "\t") exp_design <- read.csv("test_experimental_design.txt", header = TRUE, stringsAsFactors = FALSE, sep = "\t") # Import data se <- import_IsobarQuant(isabarquant_table, exp_design) ## End(Not run)
import_MaxQuant imports a protein table from MaxQuant
and converts it into a SummarizedExperiment object.
import_MaxQuant(proteins, expdesign, filter = c("Reverse", "Potential.contaminant"), intensities = "LFQ", names = "Gene.names", ids = "Protein.IDs", delim = ";")import_MaxQuant(proteins, expdesign, filter = c("Reverse", "Potential.contaminant"), intensities = "LFQ", names = "Gene.names", ids = "Protein.IDs", delim = ";")
proteins |
Data.frame, Protein table originating from MaxQuant. |
expdesign |
Data.frame,
Experimental design with 'label', 'condition'
and 'replicate' information.
See |
filter |
Character, Name of the column(s) containing features to be filtered on. |
intensities |
Character(1), Prefix of the columns containing sample intensities. |
names |
Character(1), Name of the column containing feature names. |
ids |
Character(1), Name of the column containing feature IDs. |
delim |
Character(1), Sets the delimiter separating the feature names within on protein group. |
A SummarizedExperiment object with log2-transformed values and "name" and "ID" columns containing unique names and identifiers.
# Load example data and experimental design data <- UbiLength exp_design <- UbiLength_ExpDesign # Import data se <- import_MaxQuant(data, exp_design)# Load example data and experimental design data <- UbiLength exp_design <- UbiLength_ExpDesign # Import data se <- import_MaxQuant(data, exp_design)
impute imputes missing values in a proteomics dataset.
impute(se, fun = c("bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "man", "min", "zero", "mixed", "nbavg"), ...)impute(se, fun = c("bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "man", "min", "zero", "mixed", "nbavg"), ...)
se |
SummarizedExperiment,
Proteomics data (output from |
fun |
"bpca", "knn", "QRILC", "MLE", "MinDet",
"MinProb", "man", "min", "zero", "mixed" or "nbavg",
Function used for data imputation based on |
... |
Additional arguments for imputation functions as depicted in
|
An imputed SummarizedExperiment object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Impute missing values using different functions imputed_MinProb <- impute(norm, fun = "MinProb", q = 0.05) imputed_QRILC <- impute(norm, fun = "QRILC") imputed_knn <- impute(norm, fun = "knn", k = 10, rowmax = 0.9) imputed_MLE <- impute(norm, fun = "MLE") imputed_manual <- impute(norm, fun = "man", shift = 1.8, scale = 0.3)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Impute missing values using different functions imputed_MinProb <- impute(norm, fun = "MinProb", q = 0.05) imputed_QRILC <- impute(norm, fun = "QRILC") imputed_knn <- impute(norm, fun = "knn", k = 10, rowmax = 0.9) imputed_MLE <- impute(norm, fun = "MLE") imputed_manual <- impute(norm, fun = "man", shift = 1.8, scale = 0.3)
LFQ is a wrapper function running the entire
differential enrichment/expression analysis workflow
for label free quantification (LFQ)-based proteomics data.
The protein table from
MaxQuant
is used as direct input.
LFQ(proteins, expdesign, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), type = c("all", "control", "manual"), control = NULL, test = NULL, filter = c("Reverse", "Potential.contaminant"), name = "Gene.names", ids = "Protein.IDs", alpha = 0.05, lfc = 1)LFQ(proteins, expdesign, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), type = c("all", "control", "manual"), control = NULL, test = NULL, filter = c("Reverse", "Potential.contaminant"), name = "Gene.names", ids = "Protein.IDs", alpha = 0.05, lfc = 1)
proteins |
Data.frame, The data object. |
expdesign |
Data.frame, The experimental design object. |
fun |
"man", "bpca", "knn", "QRILC", "MLE", "MinDet",
"MinProb", "min", "zero", "mixed" or "nbavg",
Function used for data imputation based on |
type |
'all', 'control' or 'manual', The type of contrasts that will be generated. |
control |
Character(1), The sample name to which the contrasts are generated (the control sample would be most appropriate). |
test |
Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC"). |
filter |
Character, Name(s) of the column(s) to be filtered on. |
name |
Character(1), Name of the column representing gene names. |
ids |
'Character(1), Name of the column representing protein IDs. |
alpha |
Numeric(1), sets the false discovery rate threshold. |
lfc |
Numeric(1), sets the log fold change threshold. |
A list of 9 objects:
data |
data.frame containing the original data |
se |
SummarizedExperiment object containing the original data |
filt |
SummarizedExperiment object containing the filtered data |
norm |
SummarizedExperiment object containing the normalized data |
imputed |
SummarizedExperiment object containing the imputed data |
diff |
SummarizedExperiment object containing FDR estimates of differential expression |
dep |
SummarizedExperiment object annotated with logical columns indicating significant proteins |
results |
data.frame containing containing all results variables from the performed analysis |
param |
data.frame containing the test parameters |
data <- UbiLength expdesign <- UbiLength_ExpDesign results <- LFQ(data, expdesign, 'MinProb', 'control', 'Ctrl')data <- UbiLength expdesign <- UbiLength_ExpDesign results <- LFQ(data, expdesign, 'MinProb', 'control', 'Ctrl')
make_se creates a SummarizedExperiment object
based on two data.frames: the protein table and experimental design.
make_se(proteins_unique, columns, expdesign)make_se(proteins_unique, columns, expdesign)
proteins_unique |
Data.frame,
Protein table with unique names annotated in the 'name' column
(output from |
columns |
Integer vector, Column numbers indicating the columns containing the assay data. |
expdesign |
Data.frame,
Experimental design with 'label', 'condition'
and 'replicate' information.
See |
A SummarizedExperiment object with log2-transformed values.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design)
make_se_parse creates a SummarizedExperiment object
based on a single data.frame.
make_se_parse(proteins_unique, columns, mode = c("char", "delim"), chars = 1, sep = "_")make_se_parse(proteins_unique, columns, mode = c("char", "delim"), chars = 1, sep = "_")
proteins_unique |
Data.frame,
Protein table with unique names annotated in the 'name' column
(output from |
columns |
Integer vector, Column numbers indicating the columns containing the assay data. |
mode |
"char" or "delim", The mode of parsing the column headers. "char" will parse the last number of characters as replicate number and requires the 'chars' parameter. "delim" will parse on the separator and requires the 'sep' parameter. |
chars |
Numeric(1), The number of characters to take at the end of the column headers as replicate number (only for mode == "char"). |
sep |
Character(1), The separator used to parse the column header (only for mode == "delim"). |
A SummarizedExperiment object with log2-transformed values.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) se <- make_se_parse(data_unique, columns, mode = "char", chars = 1) se <- make_se_parse(data_unique, columns, mode = "delim", sep = "_")# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) se <- make_se_parse(data_unique, columns, mode = "char", chars = 1) se <- make_se_parse(data_unique, columns, mode = "delim", sep = "_")
make_unique generates unique identifiers
for a proteomics dataset based on "name" and "id" columns.
make_unique(proteins, names, ids, delim = ";")make_unique(proteins, names, ids, delim = ";")
proteins |
Data.frame, Protein table for which unique names will be created. |
names |
Character(1), Name of the column containing feature names. |
ids |
Character(1), Name of the column containing feature IDs. |
delim |
Character(1), Sets the delimiter separating the feature names within one protein group. |
A data.frame with the additional variables "name" and "ID" containing unique names and identifiers, respectively.
# Load example data <- UbiLength # Check colnames and pick the appropriate columns colnames(data) data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")# Load example data <- UbiLength # Check colnames and pick the appropriate columns colnames(data) data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")
manual_impute imputes missing values in a proteomics dataset
by random draws from a manually defined distribution.
manual_impute(se, scale = 0.3, shift = 1.8)manual_impute(se, scale = 0.3, shift = 1.8)
se |
SummarizedExperiment,
Proteomics data (output from |
scale |
Numeric(1), Sets the width of the distribution relative to the standard deviation of the original distribution. |
shift |
Numeric(1), Sets the left-shift of the distribution (in standard deviations) from the median of the original distribution. |
An imputed SummarizedExperiment object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Impute missing values manually imputed_manual <- impute(norm, fun = "man", shift = 1.8, scale = 0.3)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Impute missing values manually imputed_manual <- impute(norm, fun = "man", shift = 1.8, scale = 0.3)
meanSdPlot generates a hexagonal heatmap
of the row standard deviations versus row means
from SummarizedExperiment objects.
See meanSdPlot.
meanSdPlot(x, ranks = TRUE, xlab = ifelse(ranks, "rank(mean)", "mean"), ylab = "sd", pch, plot = TRUE, bins = 50, ...)meanSdPlot(x, ranks = TRUE, xlab = ifelse(ranks, "rank(mean)", "mean"), ylab = "sd", pch, plot = TRUE, bins = 50, ...)
x |
SummarizedExperiment, Data object. |
ranks |
Logical, Whether or not to plot the row means on the rank scale. |
xlab |
Character, x-axis label. |
ylab |
Character, y-axis label. |
pch |
Ignored - exists for backward compatibility. |
plot |
Logical, Whether or not to produce the plot. |
bins |
Numeric vector, Data object before normalization. |
... |
Other arguments,
Passed to |
A scatter plot of row standard deviations
versus row means(generated by stat_binhex)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Plot meanSdPlot meanSdPlot(norm)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Plot meanSdPlot meanSdPlot(norm)
normalize_vsn performs variance stabilizing transformation
using the vsn-package.
normalize_vsn(se)normalize_vsn(se)
se |
SummarizedExperiment,
Proteomics data (output from |
A normalized SummarizedExperiment object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt)
plot_all visualizes the results of the differential protein
expression analysis in different types of plots. These are (1)
volcano plots, (2) heatmaps, (3) single protein plots, (4) frequency plots
and/or (5) comparison plots.
plot_all(dep, plots = c("volcano", "heatmap", "single", "freq", "comparison"))plot_all(dep, plots = c("volcano", "heatmap", "single", "freq", "comparison"))
dep |
SummarizedExperiment,
Data object which has been generated by |
plots |
"volcano", "heatmap", "single", "freq" and/or "comparison", |
Pdfs containg the desired plots.
# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import and process data se <- import_MaxQuant(data, exp_design) processed <- process(se) # Differential protein expression analysis dep <- analyze_dep(processed, "control", "Ctrl") ## Not run: # Plot all plots plot_all(dep) ## End(Not run)# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import and process data se <- import_MaxQuant(data, exp_design) processed <- process(se) # Differential protein expression analysis dep <- analyze_dep(processed, "control", "Ctrl") ## Not run: # Plot all plots plot_all(dep) ## End(Not run)
plot_cond generates a histogram of
the number of proteins per condition and stacks for overlapping conditions.
plot_cond(dep, plot = TRUE)plot_cond(dep, plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
plot |
Logical(1),
If |
A histogram (generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot histogram with overlaps plot_cond(dep)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot histogram with overlaps plot_cond(dep)
plot_cond_freq generates a histogram of the number of significant conditions per protein.
plot_cond_freq(dep, plot = TRUE)plot_cond_freq(dep, plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
plot |
Logical(1),
If |
A histogram (generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot frequency of significant conditions plot_cond_freq(dep)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot frequency of significant conditions plot_cond_freq(dep)
plot_cond_overlap generates a histogram of
the number of proteins per condition or overlapping conditions.
plot_cond_overlap(dep, plot = TRUE)plot_cond_overlap(dep, plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
plot |
Logical(1),
If |
A histogram (generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot condition overlap plot_cond_overlap(dep)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot condition overlap plot_cond_overlap(dep)
plot_cor generates a Pearson correlation matrix.
plot_cor(dep, significant = TRUE, lower = -1, upper = 1, pal = "PRGn", pal_rev = FALSE, indicate = NULL, font_size = 12, plot = TRUE, ...)plot_cor(dep, significant = TRUE, lower = -1, upper = 1, pal = "PRGn", pal_rev = FALSE, indicate = NULL, font_size = 12, plot = TRUE, ...)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
significant |
Logical(1), Whether or not to filter for significant proteins. |
lower |
Integer(1), Sets the lower limit of the color scale. |
upper |
Integer(1), Sets the upper limit of the color scale. |
pal |
Character(1), Sets the color panel (from RColorBrewer). |
pal_rev |
Logical(1), Whether or not to invert the color palette. |
indicate |
Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData). |
font_size |
Integer(1), Sets the size of the labels. |
plot |
Logical(1),
If |
... |
Additional arguments for Heatmap function as depicted in
|
A heatmap plot (generated by Heatmap)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot correlation matrix plot_cor(dep)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot correlation matrix plot_cor(dep)
plot_coverage generates a barplot
of the protein coverage in all samples.
plot_coverage(se, plot = TRUE)plot_coverage(se, plot = TRUE)
se |
SummarizedExperiment, Data object for which to plot observation frequency. |
plot |
Logical(1),
If |
Barplot of protein coverage in samples
(generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot coverage filt <- filter_missval(se, thr = 0) plot_coverage(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot coverage filt <- filter_missval(se, thr = 0) plot_coverage(filt)
plot_detect generates density and CumSum plots
of protein intensities with and without missing values
plot_detect(se)plot_detect(se)
se |
SummarizedExperiment, Data object with missing values. |
Density and CumSum plots of intensities of
proteins with and without missing values
(generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter filt <- filter_missval(se, thr = 0) # Plot intensities of proteins with missing values plot_detect(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter filt <- filter_missval(se, thr = 0) # Plot intensities of proteins with missing values plot_detect(filt)
plot_dist generates a distance matrix heatmap using the Gower's distance.
plot_dist(dep, significant = TRUE, pal = "YlOrRd", pal_rev = TRUE, indicate = NULL, font_size = 12, plot = TRUE, ...)plot_dist(dep, significant = TRUE, pal = "YlOrRd", pal_rev = TRUE, indicate = NULL, font_size = 12, plot = TRUE, ...)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
significant |
Logical(1), Whether or not to filter for significant proteins. |
pal |
Character(1), Sets the color panel (from RColorBrewer). |
pal_rev |
Logical(1), Whether or not to invert the color palette. |
indicate |
Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData). |
font_size |
Integer(1), Sets the size of the labels. |
plot |
Logical(1),
If |
... |
Additional arguments for Heatmap function as depicted in
|
A heatmap plot (generated by Heatmap)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot correlation matrix plot_dist(dep)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot correlation matrix plot_dist(dep)
plot_frequency generates a barplot
of the protein overlap between samples
plot_frequency(se, plot = TRUE)plot_frequency(se, plot = TRUE)
se |
SummarizedExperiment, Data object for which to plot observation frequency. |
plot |
Logical(1),
If |
Barplot of overlap of protein identifications
between samples (generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot frequency filt <- filter_missval(se, thr = 0) plot_frequency(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot frequency filt <- filter_missval(se, thr = 0) plot_frequency(filt)
plot_gsea plots enriched gene sets
from Gene Set Enrichment Analysis.
plot_gsea(gsea_results, number = 10, alpha = 0.05, contrasts = NULL, databases = NULL, nrow = 1, term_size = 8)plot_gsea(gsea_results, number = 10, alpha = 0.05, contrasts = NULL, databases = NULL, nrow = 1, term_size = 8)
gsea_results |
Data.frame,
Gene Set Enrichment Analysis results object.
(output from |
number |
Numeric(1), Sets the number of enriched terms per contrast to be plotted. |
alpha |
Numeric(1), Sets the threshold for the adjusted P value. |
contrasts |
Character, Specifies the contrast(s) to plot. If 'NULL' all contrasts will be plotted. |
databases |
Character, Specifies the database(s) to plot. If 'NULL' all databases will be plotted. |
nrow |
Numeric(1), Sets the number of rows for the plot. |
term_size |
Numeric(1), Sets the text size of the terms. |
A barplot of the enriched terms
(generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) ## Not run: # Test enrichments gsea_results <- test_gsea(dep) plot_gsea(gsea_results) ## End(Not run)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) ## Not run: # Test enrichments gsea_results <- test_gsea(dep) plot_gsea(gsea_results) ## End(Not run)
plot_heatmap generates a heatmap of all significant proteins.
plot_heatmap(dep, type = c("contrast", "centered"), kmeans = FALSE, k = 6, col_limit = 6, indicate = NULL, clustering_distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall", "gower"), row_font_size = 6, col_font_size = 10, plot = TRUE, ...)plot_heatmap(dep, type = c("contrast", "centered"), kmeans = FALSE, k = 6, col_limit = 6, indicate = NULL, clustering_distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall", "gower"), row_font_size = 6, col_font_size = 10, plot = TRUE, ...)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
type |
'contrast' or 'centered', The type of data scaling used for plotting. Either the fold change ('contrast') or the centered log2-intensity ('centered'). |
kmeans |
Logical(1), Whether or not to perform k-means clustering. |
k |
Integer(1), Sets the number of k-means clusters. |
col_limit |
Integer(1), Sets the outer limits of the color scale. |
indicate |
Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData). Only applicable to type = 'centered'. |
clustering_distance |
"euclidean", "maximum", "manhattan", "canberra",
"binary", "minkowski", "pearson", "spearman", "kendall" or "gower",
Function used to calculate clustering distance (for proteins and samples).
Based on |
row_font_size |
Integer(1), Sets the size of row labels. |
col_font_size |
Integer(1), Sets the size of column labels. |
plot |
Logical(1),
If |
... |
Additional arguments for Heatmap function as depicted in
|
A heatmap (generated by Heatmap)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot heatmap plot_heatmap(dep) plot_heatmap(dep, 'centered', kmeans = TRUE, k = 6, row_font_size = 3) plot_heatmap(dep, 'contrast', col_limit = 10, row_font_size = 3)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot heatmap plot_heatmap(dep) plot_heatmap(dep, 'centered', kmeans = TRUE, k = 6, row_font_size = 3) plot_heatmap(dep, 'contrast', col_limit = 10, row_font_size = 3)
plot_imputation generates density plots
of all conditions for input objects, e.g. before and after imputation.
plot_imputation(se, ...)plot_imputation(se, ...)
se |
SummarizedExperiment,
Data object, e.g. before imputation
(output from |
... |
Other SummarizedExperiment object(s),
E.g. data object after imputation
(output from |
Density plots of all conditions
of all conditions for input objects, e.g. before and
after imputation (generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Plot imputation plot_imputation(filt, norm, imputed)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Plot imputation plot_imputation(filt, norm, imputed)
plot_missval generates a heatmap of proteins
with missing values to discover whether values are missing by random or not.
plot_missval(se)plot_missval(se)
se |
SummarizedExperiment, Data object with missing values. |
A heatmap indicating whether values are missing (0) or not (1)
(generated by Heatmap).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) # Plot missing values heatmap plot_missval(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) # Plot missing values heatmap plot_missval(filt)
plot_normalization generates boxplots
of all conditions for input objects, e.g. before and after normalization.
plot_normalization(se, ...)plot_normalization(se, ...)
se |
SummarizedExperiment,
Data object, e.g. before normalization (output from |
... |
Additional SummarizedExperiment object(s),
E.g. data object after normalization
(output from |
Boxplots of all conditions
for input objects, e.g. before and after normalization
(generated by ggplot).
Adding components and other plot adjustments can be easily done
using the ggplot2 syntax (i.e. using '+')
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Plot normalization plot_normalization(se, filt, norm)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and normalize filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) # Plot normalization plot_normalization(se, filt, norm)
plot_numbers generates a barplot
of the number of identified proteins per sample.
plot_numbers(se, plot = TRUE)plot_numbers(se, plot = TRUE)
se |
SummarizedExperiment,
Data object for which to plot protein numbers
(output from |
plot |
Logical(1),
If |
Barplot of the number of identified proteins per sample
(generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot numbers filt <- filter_missval(se, thr = 0) plot_numbers(filt)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter and plot numbers filt <- filter_missval(se, thr = 0) plot_numbers(filt)
plot_p_hist generates a p value histogram.
plot_p_hist(dep, adjusted = FALSE, wrap = FALSE)plot_p_hist(dep, adjusted = FALSE, wrap = FALSE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
adjusted |
Logical(1), Whether or not to use adjusted p values. |
wrap |
Logical(1), Whether or not to display different histograms for the different contrasts. |
A histogram (generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot p value histogram plot_p_hist(dep) plot_p_hist(dep, wrap = TRUE)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot p value histogram plot_p_hist(dep) plot_p_hist(dep, wrap = TRUE)
plot_pca generates a PCA plot using the top variable proteins.
plot_pca(dep, x = 1, y = 2, indicate = c("condition", "replicate"), label = FALSE, n = 500, point_size = 4, label_size = 3, plot = TRUE)plot_pca(dep, x = 1, y = 2, indicate = c("condition", "replicate"), label = FALSE, n = 500, point_size = 4, label_size = 3, plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
x |
Integer(1), Sets the principle component to plot on the x-axis. |
y |
Integer(1), Sets the principle component to plot on the y-axis. |
indicate |
Character, Sets the color, shape and facet_wrap of the plot based on columns from the experimental design (colData). |
label |
Logical, Whether or not to add sample labels. |
n |
Integer(1), Sets the number of top variable proteins to consider. |
point_size |
Integer(1), Sets the size of the points. |
label_size |
Integer(1), Sets the size of the labels. |
plot |
Logical(1),
If |
A scatter plot (generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot PCA plot_pca(dep) plot_pca(dep, indicate = "condition")# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot PCA plot_pca(dep) plot_pca(dep, indicate = "condition")
plot_single generates a barplot of a protein of interest.
plot_single(dep, proteins, type = c("contrast", "centered"), plot = TRUE)plot_single(dep, proteins, type = c("contrast", "centered"), plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
proteins |
Character, The name(s) of the protein(s) to plot. |
type |
'contrast' or 'centered', The type of data scaling used for plotting. Either the fold change ('contrast') or the centered log2-intensity ('centered'). |
plot |
Logical(1),
If |
A barplot (generated by ggplot).
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot single proteins plot_single(dep, 'USP15') plot_single(dep, 'USP15', 'centered') plot_single(dep, c('USP15', 'CUL1')) plot_single(dep, c('USP15', 'CUL1'), plot = FALSE)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot single proteins plot_single(dep, 'USP15') plot_single(dep, 'USP15', 'centered') plot_single(dep, c('USP15', 'CUL1')) plot_single(dep, c('USP15', 'CUL1'), plot = FALSE)
plot_volcano generates a volcano plot for a specified contrast.
plot_volcano(dep, contrast, label_size = 3, add_names = TRUE, adjusted = FALSE, plot = TRUE)plot_volcano(dep, contrast, label_size = 3, add_names = TRUE, adjusted = FALSE, plot = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
contrast |
Character(1), Specifies the contrast to plot. |
label_size |
Integer(1), Sets the size of name labels. |
add_names |
Logical(1), Whether or not to plot names. |
adjusted |
Logical(1), Whether or not to use adjusted p values. |
plot |
Logical(1),
If |
A volcano plot (generated by ggplot)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot volcano plot_volcano(dep, 'Ubi6_vs_Ctrl', label_size = 5, add_names = TRUE) plot_volcano(dep, 'Ubi6_vs_Ctrl', label_size = 5, add_names = TRUE, adjusted = TRUE) plot_volcano(dep, 'Ubi6_vs_Ctrl', add_names = FALSE) plot_volcano(dep, 'Ubi4_vs_Ctrl', label_size = 5, add_names = TRUE)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) # Plot volcano plot_volcano(dep, 'Ubi6_vs_Ctrl', label_size = 5, add_names = TRUE) plot_volcano(dep, 'Ubi6_vs_Ctrl', label_size = 5, add_names = TRUE, adjusted = TRUE) plot_volcano(dep, 'Ubi6_vs_Ctrl', add_names = FALSE) plot_volcano(dep, 'Ubi4_vs_Ctrl', label_size = 5, add_names = TRUE)
process performs data processing on a SummarizedExperiment object.
It (1) filters a proteomics dataset based on missing values,
(2) applies variance stabilizing normalization and
(3) imputes eventual remaining missing values.
process(se, thr = 0, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), ...)process(se, thr = 0, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), ...)
se |
SummarizedExperiment,
Proteomics data with unique names and identifiers
annotated in 'name' and 'ID' columns.
The appropriate columns and objects can be generated
using the wrapper import functions |
thr |
Integer(1), Sets the threshold for the allowed number of missing values per condition. |
fun |
"man", "bpca", "knn", "QRILC", "MLE", "MinDet",
"MinProb", "min", "zero", "mixed" or "nbavg",
Function used for data imputation based on |
... |
Additional arguments for imputation functions as depicted in
|
A filtered, normalized and imputed SummarizedExperiment object.
# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import data se <- import_MaxQuant(data, exp_design) # Process data processed <- process(se)# Load datasets data <- UbiLength exp_design <- UbiLength_ExpDesign # Import data se <- import_MaxQuant(data, exp_design) # Process data processed <- process(se)
report generates a report of the analysis performed
by TMT and LFQ wrapper functions.
Additionally, the results table is saved as a tab-delimited file.
report(results)report(results)
results |
List of SummarizedExperiment objects obtained
from the |
A rmarkdown report is generated and saved.
Additionally, the results table is saved as a tab-delimited txt file.
## Not run: data <- UbiLength expdesign <- UbiLength_ExpDesign results <- LFQ(data, expdesign, 'MinProb', 'control', 'Ctrl') report(results) ## End(Not run)## Not run: data <- UbiLength expdesign <- UbiLength_ExpDesign results <- LFQ(data, expdesign, 'MinProb', 'control', 'Ctrl') report(results) ## End(Not run)
run_app launches an interactive shiny app
for interactive differential enrichment/expression analysis
of proteomics data.
run_app(app)run_app(app)
app |
'LFQ' or 'TMT', The name of the app. |
Launches a browser with the shiny app
## Not run: # Run the app run_app('LFQ') run_app('TMT') ## End(Not run)## Not run: # Run the app run_app('LFQ') run_app('TMT') ## End(Not run)
Use as instead.
se2msn(se)se2msn(se)
se |
SummarizedExperiment, Object which will be turned into a MSnSet object. |
A MSnSet object.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Convert to MSnSet data_msn <- as(se, "MSnSet") # Convert back to SE se_back <- as(data_msn, "SummarizedExperiment")# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Convert to MSnSet data_msn <- as(se, "MSnSet") # Convert back to SE se_back <- as(data_msn, "SummarizedExperiment")
test_diff performs a differential enrichment test based on
protein-wise linear models and empirical Bayes
statistics using limma. False Discovery Rates are estimated
using fdrtool.
test_diff(se, type = c("control", "all", "manual"), control = NULL, test = NULL, design_formula = formula(~0 + condition))test_diff(se, type = c("control", "all", "manual"), control = NULL, test = NULL, design_formula = formula(~0 + condition))
se |
SummarizedExperiment,
Proteomics data (output from |
type |
"control", "all" or "manual", The type of contrasts that will be tested. This can be all possible pairwise comparisons ("all"), limited to the comparisons versus the control ("control"), or manually defined contrasts ("manual"). |
control |
Character(1), The condition to which contrasts are generated if type = "control" (a control condition would be most appropriate). |
test |
Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC"). |
design_formula |
Formula, Used to create the design matrix. |
A SummarizedExperiment object containing fdr estimates of differential expression.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") diff <- test_diff(imputed, "manual", test = c("Ubi4_vs_Ctrl", "Ubi6_vs_Ctrl")) # Test for differentially expressed proteins with a custom design formula diff <- test_diff(imputed, "control", "Ctrl", design_formula = formula(~ 0 + condition + replicate))# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") diff <- test_diff(imputed, "manual", test = c("Ubi4_vs_Ctrl", "Ubi6_vs_Ctrl")) # Test for differentially expressed proteins with a custom design formula diff <- test_diff(imputed, "control", "Ctrl", design_formula = formula(~ 0 + condition + replicate))
test_gsea tests for enriched gene sets
in the differentially enriched proteins.
This can be done independently for the different contrasts.
test_gsea(dep, databases = c("GO_Molecular_Function_2017b", "GO_Cellular_Component_2017b", "GO_Biological_Process_2017b"), contrasts = TRUE)test_gsea(dep, databases = c("GO_Molecular_Function_2017b", "GO_Cellular_Component_2017b", "GO_Biological_Process_2017b"), contrasts = TRUE)
dep |
SummarizedExperiment,
Data object for which differentially enriched proteins are annotated
(output from |
databases |
Character, Databases to search for gene set enrichment. See http://amp.pharm.mssm.edu/Enrichr/ for available databases. |
contrasts |
Logical(1), Whether or not to perform the gene set enrichment analysis independently for the different contrasts. |
A data.frame with enrichment terms (generated by enrichr)
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) ## Not run: # Test enrichments gsea_results_per_contrast <- test_gsea(dep) gsea_results <- test_gsea(dep, contrasts = FALSE) gsea_kegg <- test_gsea(dep, databases = "KEGG_2016") ## End(Not run)# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- diff <- test_diff(imputed, "control", "Ctrl") dep <- add_rejections(diff, alpha = 0.05, lfc = 1) ## Not run: # Test enrichments gsea_results_per_contrast <- test_gsea(dep) gsea_results <- test_gsea(dep, contrasts = FALSE) gsea_kegg <- test_gsea(dep, databases = "KEGG_2016") ## End(Not run)
theme_DEP1 is the default ggplot theme used for plotting
in DEP with horizontal x-axis labels.
theme_DEP1()theme_DEP1()
ggplot theme
data <- UbiLength data <- data[data$Reverse != '+' & data$Potential.contaminant != '+',] data_unique <- make_unique(data, 'Gene.names', 'Protein.IDs', delim = ';') columns <- grep('LFQ.', colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) filt <- filter_missval(se, thr = 0) plot_frequency(filt) # uses theme_DEP1() styledata <- UbiLength data <- data[data$Reverse != '+' & data$Potential.contaminant != '+',] data_unique <- make_unique(data, 'Gene.names', 'Protein.IDs', delim = ';') columns <- grep('LFQ.', colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) filt <- filter_missval(se, thr = 0) plot_frequency(filt) # uses theme_DEP1() style
theme_DEP2 is the ggplot theme used for plotting
in DEP with vertical x-axis labels.
theme_DEP2()theme_DEP2()
ggplot theme
data <- UbiLength data <- data[data$Reverse != '+' & data$Potential.contaminant != '+',] data_unique <- make_unique(data, 'Gene.names', 'Protein.IDs', delim = ';') columns <- grep('LFQ.', colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) filt <- filter_missval(se, thr = 0) plot_numbers(filt) # uses theme_DEP2() styledata <- UbiLength data <- data[data$Reverse != '+' & data$Potential.contaminant != '+',] data_unique <- make_unique(data, 'Gene.names', 'Protein.IDs', delim = ';') columns <- grep('LFQ.', colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) filt <- filter_missval(se, thr = 0) plot_numbers(filt) # uses theme_DEP2() style
TMT is a wrapper function running the
entire differential enrichment/expression analysis workflow
for TMT-based proteomics data.
The protein table from
IsobarQuant
is used as direct input.
TMT(proteins, expdesign, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), type = c("all", "control", "manual"), control = NULL, test = NULL, name = "gene_name", ids = "protein_id", alpha = 0.05, lfc = 1)TMT(proteins, expdesign, fun = c("man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), type = c("all", "control", "manual"), control = NULL, test = NULL, name = "gene_name", ids = "protein_id", alpha = 0.05, lfc = 1)
proteins |
Data.frame, The data object. |
expdesign |
Data.frame, The experimental design object. |
fun |
"man", "bpca", "knn", "QRILC", "MLE", "MinDet",
"MinProb", "min", "zero", "mixed" or "nbavg",
Function used for data imputation based on |
type |
'all', 'control' or 'manual', The type of contrasts that will be generated. |
control |
Character(1), The sample name to which the contrasts are generated (the control sample would be most appropriate). |
test |
Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC"). |
name |
Character(1), Name of the column representing gene names. |
ids |
'Character(1), Name of the column representing protein IDs. |
alpha |
Numeric(1), sets the false discovery rate threshold. |
lfc |
Numeric(1), sets the log fold change threshold. |
A list of 8 objects:
se |
SummarizedExperiment object containing the original data |
filt |
SummarizedExperiment object containing the filtered data |
norm |
SummarizedExperiment object containing the normalized data |
imputed |
SummarizedExperiment object containing the imputed data |
diff |
SummarizedExperiment object containing FDR estimates of differential expression |
dep |
SummarizedExperiment object annotated with logical columns indicating significant proteins |
results |
data.frame containing containing all results variables from the performed analysis |
param |
data.frame containing the test parameters |
## Not run: TMT_res <- TMT() ## End(Not run)## Not run: TMT_res <- TMT() ## End(Not run)
The UbiLength dataset contains label free quantification (LFQ) data for ubiquitin interactors of different linear ubiquitin lengths, generated by Zhang et al 2017. The dataset contains the proteingroups output file from MaxQuant.
UbiLengthUbiLength
A data.frame with 3006 observations and 35 variables:
Uniprot IDs
Uniprot IDs of major protein(s) in the protein group
Full protein names
Gene name
Header as present in the Uniprot fasta file
Number of peptides identified for this protein group
Number of peptides used for the quantification of this protein group
Number of peptides identified which are unique for this protein group
Raw mass spectrometry intensity, A.U.
LFQ normalized mass spectrometry intensity, A.U.
The protein is only identified by a modification site if marked ('+')
The protein is identified in the decoy database if marked ('+')
The protein is a known contaminant if marked ('+')
A data.frame.
Zhang, Smits, van Tilburg, et al (2017). An interaction landscape of ubiquitin signaling. Molecular Cell 65(5): 941-955. doi: 10.1016/j.molcel.2017.01.004.
The UbiLength_ExpDesign object annotates 12 different samples of the UbiLength dataset in 4 conditions and 3 replicates.
UbiLength_ExpDesignUbiLength_ExpDesign
A data.frame with 12 observations and 3 variables:
Label names
Experimental conditions
Replicate number
A data.frame.
Zhang, Smits, van Tilburg, et al (2017). An interaction landscape of ubiquitin signaling. Molecular Cell 65(5): 941-955. doi: 10.1016/j.molcel.2017.01.004.