Package 'MetaProViz'

Description

Manually curated table for the amino acid alanine toshowcase pathways (wiki, reactome, etc.) and alanine IDs (chebi, hmdb, etc.) included in those pathways

Usage

alanine_pathways

Format

An object of class tbl_df (inherits from tbl, data.frame) with 204 rows and 5 columns.

Examples

data(alanine_pathways)
head(alanine_pathways)

Description

Biocrates kit feature information of the "MxP Quant 500 XL kit" that covers more than 1,000 metabolites with biochemical class information and the exported different metabolite IDs (HMDB, KEGG, etc.). information (INCHI, Key, etc.).

Usage

biocrates_features

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 1019 rows and 14 columns.

Examples

data(biocrates_features)
head(biocrates_features)

Description

Metabolomics workbench project PR001418, study ST002226 and ST002224 measured metabolites were assigned HMDB and KEGG IDs as well as one main metabolic pathway. metabolite trivial names. nitrogen supports renal cancer progression , Nature Communications 2022, doi:10.1038/s41467-022-35036-4

Usage

cellular_meta

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 200 rows and 5 columns.

Examples

data(cellular_meta)
head(cellular_meta)

Description

features

Usage

checkmatch_pk_to_data(
  data,
  input_pk,
  metadata_info = c(InputID = "HMDB", PriorID = "HMDB", grouping_variable = "term"),
  save_table = "csv",
  path = NULL,
  delimiter = ", "
)

Arguments

Value

A list with three elements:

• data_summary — a data frame summarising matching results per input ID, including counts, conflicts, and recommended actions.

• GroupingVariable_summary — a detailed data frame showing matches grouped by the specified variable, with conflict annotations.

• data_long — a merged data frame of prior knowledge IDs and detected IDs in long format.

Examples

## Not run: 
data(cellular_meta)
DetectedIDs <- cellular_meta %>%
    dplyr::select("Metabolite", "HMDB") %>%
    tidyr::drop_na()
input_pathway <- translate_id(
    data = metsigdb_kegg(),
    metadata_info = c(
        InputID = "MetaboliteID",
        grouping_variable = "term"
    ),
    from = c("kegg"),
    to = c("hmdb")
)[["TranslatedDF"]] %>% tidyr::drop_na()
Res <- checkmatch_pk_to_data(
    data = DetectedIDs,
    input_pk = input_pathway,
    metadata_info = c(
        InputID = "HMDB",
        PriorID = "hmdb",
        grouping_variable = "term"
    )
)

## End(Not run)

Description

Uses enricher to run ORA on each of the metabolite cluster from any of the MCA functions using a pathway list

Usage

cluster_ora(
  data,
  metadata_info = c(ClusterColumn = "RG2_Significant", BackgroundColumn = "BG_method",
    PathwayTerm = "term", PathwayFeature = "Metabolite"),
  remove_background = TRUE,
  input_pathway,
  pathway_name = "",
  min_gssize = 10,
  max_gssize = 1000,
  save_table = "csv",
  path = NULL
)

Arguments

Value

Saves results as individual .csv files.

Examples

KEGG_Pathways <- metsigdb_kegg()
data(intracell_dma) # loads the object into your environment
DMAres <- intracell_dma %>%
    dplyr::filter(!is.na(KEGGCompound)) %>%
    tibble::column_to_rownames("KEGGCompound") %>%
    dplyr::select(-"Metabolite")
RES <- cluster_ora(
    data = DMAres,
    metadata_info = c(
        ClusterColumn = "Pathway",
        PathwayTerm = "term",
        PathwayFeature = "Metabolite"
    ),
    input_pathway = KEGG_Pathways,
    remove_background = FALSE
)

Description

Cluster terms in prior knowledge by set overlap

Usage

cluster_pk(
  data,
  metadata_info = c(metabolite_column = "MetaboliteID", pathway_column = "term"),
  similarity = c("jaccard", "overlap_coefficient", "correlation"),
  correlation_method = "pearson",
  input_format = c("long", "enrichment"),
  delimiter = "/",
  threshold = 0.5,
  plot_threshold = 0,
  clust = c("components", "community", "hierarchical"),
  hclust_method = "average",
  min = 2,
  plot_name = "ClusterGraph",
  max_nodes = 10000,
  min_degree = 1,
  node_size_column = NULL,
  show_density = FALSE,
  save_plot = "png",
  print_plot = FALSE,
  path = NULL
)

Arguments

Value

A list with:

Examples

# Create toy pathway data in long format
toy_pw <- data.frame(
    MetaboliteID = c("C1", "C2", "C3", "C1", "C2", "C4", "C3", "C4", "C5"),
    term = c("pA", "pA", "pA", "pB", "pB", "pB", "pC", "pC", "pC")
)

r <- cluster_pk(
    toy_pw,
    metadata_info = c(
        metabolite_column = "MetaboliteID",
        pathway_column = "term"
    ),
    input_format = "long",
    similarity = "jaccard",
    threshold = 0.1,
    clust = "community",
    min = 1,
    save_plot = NULL,
    print_plot = FALSE
)

Description

and Generate an UpSet Plot This function compares gene and/or metabolite features across multiple prior knowledge (PK) resources or, if a single resource is provided with a vector of column names in metadata_info, compares columns within that resource. In the multi-resource mode, each element in data represents a PK resource (either as a data frame or a recognized resource name) from which a set of features is extracted. A binary summary table is then constructed and used to create an UpSet plot. In the within-resource mode, a single data frame is provided (with data containing one element) and its metadata_info entry is a vector of column names to compare (e.g., binary indicators for different annotations). In this case, the function expects the data frame to have a grouping column named "Class" (or, alternatively, a column specified via the class_col attribute in metadata_info) that is used for grouping in the UpSet plot.

Usage

compare_pk(
  data,
  metadata_info = NULL,
  filter_by = c("both", "gene", "metabolite"),
  plot_name = "Overlap of Prior Knowledge Resources",
  name_col = "TrivialName",
  palette_type = "polychrome",
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

A list containing two elements:

• summary_table: A data frame representing either:

  • the binary summary matrix of feature presence/absence across multiple resources, or

  • the original data frame (augmented with binary columns and a None column) in within-resource mode.

• upset_plot: The UpSet plot object generated by the function.

Examples

## Within-resource comparison of the ID columns of the Biocrates kit
data(biocrates_features)
res_single <- compare_pk(
    data = list(Biocft = biocrates_features),
    metadata_info = list(Biocft = c("CHEBI", "HMDB", "LIMID")),
    plot_name = "Overlap of BioCrates Columns",
    save_plot = NULL,
    save_table = NULL
)

Description

Normalize consumption release data

Usage

core_norm(data, metadata_sample, metadata_info)

Arguments

Value

List with two elements: DF (including output table) and Plot (including all plots generated)

This function is intended for standalone CoRe-style experiments with control-media metadata available via metadata_info.

Examples

data(medium_raw)
Media <-
    medium_raw %>%
    tibble::column_to_rownames("Code") %>%
    subset(!Conditions == "Pool") %>%
    dplyr::mutate_all(~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .))
Res <- core_norm(
    data = Media[, -c(1:3)],
    metadata_sample = Media[, c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        core_norm_factor = "GrowthFactor",
        core_media = "blank"
    )
)

Description

This function processes a data frame column by counting the number of entries within each cell. It considers both NA values and empty strings as zero entries, and categorizes each cell as "No ID", "Single ID", or "Multiple IDs" based on the count. A histogram is then generated to visualize the distribution of entry counts. scale_x_continuous

Usage

count_id(
  data,
  column,
  delimiter = ",\n    ",
  fill_colors = c(`No ID` = "#FB8072", `Single ID` = "#B3DE69", `Multiple IDs` =
    "#80B1D3"),
  binwidth = 1,
  title_prefix = NULL,
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

A list with two elements:

Examples

data(biocrates_features)
count_id(biocrates_features, "HMDB")

Description

Performs differential metabolite analysis to obtain Log2FC, p-value, adjusted p-value, and t-value when comparing two or multiple conditions.

Usage

dma(
  data,
  metadata_sample = NULL,
  metadata_info = c(Conditions = "Conditions", Numerator = NULL, Denominator = NULL),
  pval = "lmFit",
  padj = "fdr",
  metadata_feature = NULL,
  core = FALSE,
  vst = FALSE,
  shapiro = TRUE,
  bartlett = TRUE,
  transform = TRUE,
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List of lists. Depending on parameter settings, returns dma (data frame of each comparison), shapiro (includes data frame and plot), bartlett (includes data frame and histogram), vst (includes data frame and plot), and VolcanoPlot (plots of each comparison).

Examples

data(intracell_raw_se)
ResI <- dma(
    data = intracell_raw_se,
    metadata_info = c(
        Conditions = "Conditions", Numerator = NULL, Denominator = "HK2"
    )
)

data(intracell_raw)
Intra <- intracell_raw[-c(49:58), ] %>% tibble::column_to_rownames("Code")
ResI <- dma(
    data = Intra[, -c(1:3)],
    metadata_sample = Intra[, c(1:3)],
    metadata_info = c(
        Conditions = "Conditions", Numerator = NULL, Denominator = "HK2"
    )
)

Description

Manually curated list of aminoacids and aminoacid-related metabolites with corresponding metabolite identifiers (HMDB, KEGG, etc.) irrespective of chirality.

Usage

equivalent_features

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 34 rows and 7 columns.

Examples

data(equivalent_features)
head(equivalent_features)

Description

Find additional potential IDs for "kegg", "pubchem", "chebi", "hmdb"

Usage

equivalent_id(
  data,
  metadata_info = c(InputID = "MetaboliteID"),
  from = "hmdb",
  save_table = "csv",
  path = NULL
)

Arguments

Value

Input DF with additional column including potential additional IDs.

Examples

data(cellular_meta)
DetectedIDs <- cellular_meta %>% tidyr::drop_na()
Res <- equivalent_id(
    data = DetectedIDs,
    metadata_info = c(InputID = "HMDB"),
    from = "hmdb"
)

Description

Filter features

Usage

feature_filtering(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  featurefilt = "Modified",
  cutoff_featurefilt = 0.8,
  metadata_feature = NULL
)

Arguments

Value

List with elements: DF (filtered matrix); FilteredFeatures (a data frame with one row per input feature, columns FeatureID and FilteredFeatures containing TRUE or FALSE; this column is always named "FilteredFeatures" regardless of what it was called in metadata_feature); featurefilt_used (the filtering method actually applied, which differs from featurefilt if a fallback was triggered); and, for SummarizedExperiment input, SE (the filtered data as a SummarizedExperiment).

This function can be used as a standalone preprocessing step on raw input data with matching sample metadata.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- feature_filtering(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    )
)

Description

gaude_pathways

Usage

gaude_pathways

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 1932 rows and 3 columns.

Examples

data(gaude_pathways)
head(gaude_pathways)

Description

Builds an exclusion table from hard-coded seed identifiers and translates them across ID systems (HMDB, KEGG, CHEBI, PUBCHEM).

Usage

get_exclusion_metabolites()

Value

A data frame with columns metabolite_id, class, and id_type.

Examples

## Not run: 
get_exclusion_metabolites()

## End(Not run)

Description

hallmarks

Usage

hallmarks

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 7322 rows and 2 columns.

Examples

data(hallmarks)
head(hallmarks)

Description

Runs an ID-space QC and expansion workflow on feature metadata using the exported MetaProViz ID helper functions. The workflow always starts with ID exploration using compare_pk() and count_id(), can optionally perform automatic seed-ID compatibility handling, and can then run either translate_id() or traverse_ids().

Usage

id_processing(
  data,
  id_types = c("HMDB", "KEGG", "CHEBI", "PUBCHEM"),
  delimiter = c(";", ","),
  run_compatibility_check = FALSE,
  handle_partially_compatible = TRUE,
  handle_completely_incompatible = TRUE,
  completely_incompatible_priority = c("HMDB", "CHEBI", "PUBCHEM", "KEGG"),
  run_translation = FALSE,
  translation_from = NULL,
  translation_to = NULL,
  translation_summary = FALSE,
  run_traversal = FALSE,
  edge_table = NULL,
  compare_name_col = "Metabolite",
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  verbose = FALSE,
  path = NULL
)

Arguments

Value

Named list with three top-level entries:

Examples

data(tissue_meta)

qc_only <- id_processing(
    data = tissue_meta[seq_len(min(40, nrow(tissue_meta))), , drop = FALSE],
    save_plot = NULL,
    save_table = NULL,
    print_plot = FALSE,
    verbose = FALSE
)

Description

Metabolomics workbench project PR001418, study ST002224 where we performed differential metabolite analysis comparing intracellular metabolomics of 786-M1A versus HK2 cells. metabolite values used as input with row names being metabolitetrivial names. nitrogen supports renal cancer progression , Nature Communications 2022, doi:10.1038/s41467-022-35036-4

Usage

intracell_dma

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 179 rows and 14 columns.

Examples

data(intracell_dma)
head(intracell_dma)

Description

Metabolomics workbench project PR001418, study ST002224 where we exported integrated raw peak values of intracellular metabolomics of HK2 and ccRCC cell lines 786-O, 786-M1A and 786-M2A. -Conditions: Character vector indicating cell line identity - Analytical_Replicate: Integer replicate number for analytical replicates -Biological_Replicate: Integer replicate number for biological replicates - Additional numeric columns (183 in total) containing raw metabolite intensities nitrogen supports renal cancer progression, Nature Communications 2022, DOI:10.1038/s41467-022-35036-4.

Usage

intracell_raw

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 58 rows and 186 columns.

Examples

data(intracell_raw)
head(intracell_raw)

Description

Metabolomics workbench project PR001418, study ST002224 where we exported integrated raw peak values of intracellular metabolomics of HK2 and ccRCC cell lines 786-O, 786-M1A and 786-M2A converted into an se object. -Conditions: Character vector indicating cell line identity - Analytical_Replicate: Integer replicate number for analytical replicates -Biological_Replicate: Integer replicate number for biological replicates nitrogen supports renal cancer progression, Nature Communications 2022, DOI:10.1038/s41467-022-35036-4.

Usage

intracell_raw_se

Format

An object of class SummarizedExperiment with 182 rows and 58 columns.

Examples

data(intracell_raw_se)
head(intracell_raw_se)

Description

Gene to metabolite translation is based on mappings in Recon-3D (cosmosR).

Usage

make_gene_metab_set(
  input_pk,
  metadata_info = c(Target = "gene"),
  pk_name = NULL,
  save_table = "csv",
  path = NULL,
  exclude_metabolites = "all"
)

Arguments

Value

List of two data frames: "GeneMetabSet" and "MetabSet".

Examples

data(hallmarks)
make_gene_metab_set(hallmarks)

Description

Create Mapping Ambiguities between two ID types

Usage

mapping_ambiguity(
  data,
  from,
  to,
  grouping_variable = NULL,
  summary = FALSE,
  save_table = "csv",
  path = NULL
)

Arguments

Value

List with at least 4 DFs: 1-3) from-to-to: 1. MappingIssues, 2. MappingIssues summary, 3. Long summary (If summary=TRUE) & 4-6) to-to-from: 4. MappingIssues, 5. MappingIssues summary, 6. Long summary (If summary=TRUE) & 7) Combined summary table (If summary=TRUE)

Examples

## Not run: 
KEGG_Pathways <- metsigdb_kegg()
InputDF <- translate_id(
    data = KEGG_Pathways,
    metadata_info = c(
        InputID = "MetaboliteID",
        grouping_variable = "term"
    ),
    from = c("kegg"),
    to = c("pubchem")
)[["TranslatedDF"]]
Res <- mapping_ambiguity(
    data = InputDF,
    from = "MetaboliteID",
    to = "pubchem",
    grouping_variable = "term",
    summary = TRUE
)

## End(Not run)

Description

Performs metabolite clustering analysis and computes clusters based on regulatory rules between conditions.

Usage

mca_2cond(
  data_c1,
  data_c2,
  metadata_info_c1 = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
    ValueCutoff = 1),
  metadata_info_c2 = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
    ValueCutoff = 1),
  feature = "Metabolite",
  save_table = "csv",
  method_background = "C1&C2",
  path = NULL
)

Arguments

Value

List of two DFs: 1. summary of the cluster count and 2. the detailed information of each metabolites in the clusters.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Input <- dma(
    data = Intra[-c(49:58), -c(1:3)],
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Numerator = NULL,
        Denominator = "HK2"
    )
)

Res <- mca_2cond(
    data_c1 = Input[["dma"]][["786-O_vs_HK2"]],
    data_c2 = Input[["dma"]][["786-M1A_vs_HK2"]]
)

Description

Performs metabolite clustering analysis and computes clusters based on regulatory rules between intracellular and culture media metabolomics in core experiments.

Usage

mca_core(
  data_intra,
  data_core,
  metadata_info_intra = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
    ValueCutoff = 1),
  metadata_info_core = c(DirectionCol = "core", ValueCol = "Log2(Distance)", StatCol =
    "p.adj", cutoff_stat = 0.05, ValueCutoff = 1),
  feature = "Metabolite",
  save_table = "csv",
  method_background = "Intra&core",
  path = NULL
)

Arguments

Value

List of two DFs: 1. summary of the cluster count and 2. the detailed information of each metabolites in the clusters.

Examples

data(intracell_dma)

# Create mock CoRe DMA results with required columns
core_dma <- data.frame(
    Metabolite = intracell_dma$Metabolite[1:50],
    `Log2(Distance)` = runif(50, -2, 2),
    p.adj = runif(50, 0, 0.1),
    core = sample(c("Consumption", "Release"), 50, replace = TRUE),
    check.names = FALSE
)

Res <- mca_core(
    data_intra = as.data.frame(intracell_dma),
    data_core = core_dma,
    save_table = NULL
)

Description

Manually curated table defining the flow of information of the Conusuption-Release and Intracellular metabolomics biological regulatory clusters Regulatory labels from the different grouping methods. columns (RG1-RG3)

Usage

mca_core_rules

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 108 rows and 7 columns.

Examples

data(mca_core_rules)
head(mca_core_rules)

Description

Manually curated table defining the flow of information of the two condition biological regulatory clusters Regulatory labels from the different grouping methods. columns (RG1-RG3)

Usage

mca_twocond_rules

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 36 rows and 5 columns.

Examples

data(mca_twocond_rules)
head(mca_twocond_rules)

Description

Metabolomics workbench project PR001418, study ST002226 where we exported integrated raw peak values of intracellular metabolomics of HK2 and cccRCC cell lines 786-O, 786-M1A, 786-M2A, OS-RC-2, OS-LM1 and RFX-631. a numeric column for each measured metabolite (raw data) nitrogen supports renal cancer progression , Nature Communications 2022, doi:10.1038/s41467-022-35036-4

Usage

medium_raw

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 44 rows and 77 columns.

Examples

data(medium_raw)
head(medium_raw)

Description

Metabolomics workbench project PR001418, study ST002226 where we exported integrated raw peak values of intracellular metabolomics of HK2 and cccRCC cell lines 786-O, 786-M1A, 786-M2A, OS-RC-2, OS-LM1 and RFX-631 converted into an se object. -Conditions: Character vector indicating cell line identity -Biological_Replicates: Integer replicate number for biological replicates -GrowthFactor: Character vector indicating growth factor condition nitrogen supports renal cancer progression, Nature Communications 2022, doi:10.1038/s41467-022-35036-4

Usage

medium_raw_se

Format

An object of class SummarizedExperiment with 73 rows and 44 columns.

Examples

data(medium_raw_se)
head(medium_raw_se)

Description

Meta prior-knowledge

Usage

meta_pk(
  data,
  metadata_sample,
  metadata_info = NULL,
  save_table = "csv",
  path = NULL
)

Arguments

Value

DF with prior knowledge based on patient metadata

Examples

data(tissue_norm_se)
Res <- meta_pk(tissue_norm_se)

data(tissue_norm)
Tissue_Norm <- tissue_norm %>% tibble::column_to_rownames("Code")
Res <- meta_pk(
    data = Tissue_Norm[, -c(1:13)],
    metadata_sample = Tissue_Norm[, c(2, 4:5, 12:13)]
)

Description

Performs PCA analysis on input data and combines it with sample metadata to run ANOVA tests for identifying significant differences between groups.

Usage

metadata_analysis(
  data,
  metadata_sample = NULL,
  scaling = TRUE,
  percentage = 0.1,
  cutoff_stat = 0.05,
  cutoff_variance = 1,
  save_table = "csv",
  save_plot = "svg",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List of DFs: prcomp results, loadings, top-Bottom features, annova results, results summary

Examples

data(tissue_norm)
d <- tissue_norm[1:100, -c(2:14)] %>% tibble::column_to_rownames("Code")
d <- d[, vapply(d, function(x) length(unique(x)) > 1, logical(1))]
Res <- metadata_analysis(
    data = d,
    metadata_sample = tissue_norm[1:100, c(1, 5:6)] %>%
        tibble::column_to_rownames("Code"),
    save_plot = NULL,
    save_table = NULL,
    print_plot = FALSE
)

Description

MetaProViz (Metabolomics Processing, functional analysis and Visualization), a free open-source R-package that provides mechanistic hypotheses from metabolomics data by integrating prior knowledge from literature with metabolomics. MetaProViz offers an interactive framework consisting of five modules: Processing, differential analysis, prior knowledge access and refactoring, functional analysis and visualization of both intracellular and exometabolomics (consumption-release/core data).

Author(s)

Christina Schmidt <\email{[email protected]}> and Jannik Franken and Denes Turei <\email{[email protected]}> and Dimitrios Prymidis and Macabe Daley and Julio Saez-Rodriguez and Christian Frezza

See Also

Useful links:

• https://saezlab.github.io/MetaProViz

• Report bugs at https://github.com/saezlab/MetaProViz/issues

Description

Current config file path of MetaProViz

Usage

metaproviz_config_path(user = FALSE)

Arguments

Value

Character: path to the config file.

Examples

metaproviz_config_path()

Description

Load the package configuration from a config file

Usage

metaproviz_load_config(path = NULL, title = "default", user = FALSE, ...)

Arguments

Value

Invisibly returns the config as a list.

Examples

metaproviz_load_config()

Description

Browse the current MetaProViz log file

Usage

metaproviz_log()

Value

Returns NULL.

See Also

metaproviz_logfile

Examples

## Not run: 
metaproviz_log()
# then you can browse the log file, and exit with `q`

## End(Not run)

Description

Path to the current MetaProViz log file

Usage

metaproviz_logfile()

Value

Character: path to the current logfile, or NULL if no logfile is available.

See Also

metaproviz_log

Examples

metaproviz_logfile()
# [1] "path/metaproviz/metaproviz-log/metaproviz-20210309-1642.log"

Description

Restore the built-in default values of all config parameters of MetaProViz

Usage

metaproviz_reset_config(save = NULL, reset_all = FALSE)

Arguments

Value

The config as a list.

See Also

metaproviz_load_config, metaproviz_save_config

Examples

# restore the defaults and write them to the default config file:
metaproviz_reset_config()
metaproviz_save_config()

Description

Save the current package configuration

Usage

metaproviz_save_config(path = NULL, title = "default", local = FALSE)

Arguments

Value

Returns NULL.

Examples

# restore the defaults and write them to the default config file:
metaproviz_reset_config()
metaproviz_save_config()

Description

Sets the log level for the package logger

Usage

metaproviz_set_loglevel(level, target = "logfile")

Arguments

Value

Returns NULL.

Examples

metaproviz_set_loglevel(logger::FATAL, target = "console")

Description

Metabolite chemical classes from RaMP DB

Usage

metsigdb_chemicalclass(
  version = "2.5.4",
  save_table = "csv",
  path = NULL,
  exclude_metabolites = "all"
)

Arguments

Value

A data frame containing the Prior Knowledge.

Examples

ChemicalClass <- metsigdb_chemicalclass()

Description

KEGG pathways

Usage

metsigdb_kegg(exclude_metabolites = "all")

Arguments

Value

A data frame containing the KEGG pathways suitable for ORA.

Examples

metsigdb_kegg()

Description

Retrieves metabolite-cancer associations from MACDB via OmnipathR and summarizes them to unique metabolite-cancer associations (by term and Metabolite_PubchemID).

Usage

metsigdb_macdb(exclude_metabolites = "all")

Arguments

Value

A data frame with one row per unique metabolite-cancer association, collapsed metadata columns, and summary metrics (evidence_count, significance_count, association_score).

Description

Annotated metabolite-protein interactions from MetalinksDB

Usage

metsigdb_metalinks(
  types = NULL,
  cell_location = NULL,
  tissue_location = NULL,
  biospecimen_location = NULL,
  disease = NULL,
  pathway = NULL,
  hmdb_ids = NULL,
  uniprot_ids = NULL,
  save_table = "csv",
  path = NULL,
  exclude_metabolites = "all"
)

Arguments

Value

A data frame of metabolite-protein interactions from MetalinksDB. Source fields are retained and additional annotations are included. interaction_family uses protein role (protein_type and transport_direction) before falling back to edge type, while interaction_annotation_status records mixed source annotations and edge-type-based inferences. interaction combines transport_direction and type into "Transport_In", "Transport_Out", "Ligand-Receptor", "Production-Degradation" or "Unknown", and direction gives the matching edge direction ("to_term", i.e. metabolite to protein, or "to_metabolite") for metadata_info[["EdgeDirection"]] in viz_pk_network().

Examples

metsigdb_metalinks()

Description

Queries the OmniPath resource through OmniPathR to obtail Reactome pathway level metabolite sets.

Usage

metsigdb_reactome(
  species = "Homo sapiens",
  pathway_ids = NULL,
  out_path = NULL,
  exclude_metabolites = "all"
)

Arguments

Value

A tibble in long format containing one row per metabolite for the Reactome pathways.

Examples

## Not run: 
df <- metsigdb_reactome()
head(df)

## End(Not run)

Description

Retrieves pathway to metabolite mappings from WikiPathways (via wikipathways_metabolites_sparql()) via OmnipathR and returns a long-format table with one metabolite identifier per row.

Usage

metsigdb_wikipathways(species = "Homo sapiens", exclude_metabolites = "all")

Arguments

Value

A tibble in long format with columns pathway_id, pathway_name, pathway_url, n_metabolites_in_pathway, and metabolite_id.

Description

Missing Value Imputation using half minimum value

Usage

mvi_imputation(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  mvi_percentage = 50
)

Arguments

Value

DF with imputed values

This function can be used on raw or already filtered data, provided sample metadata and condition information are available.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- mvi_imputation(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    )
)

Description

outlier_detection

Usage

outlier_detection(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  hotellins_confidence = 0.99
)

Arguments

Value

List with two elements: : DF (including output tables) and Plot (including all plots generated)

This function can be run independently, but PCA-based outlier detection is recommended on already preprocessed data such as filtered, imputed, and where appropriate normalized input.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- outlier_detection(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    )
)

Description

Find metabolites with high variability across total pool samples

Usage

pool_estimation(
  data,
  metadata_sample = NULL,
  metadata_info = NULL,
  cutoff_cv = 30,
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List with two elements: DF (including input and output table) and Plot (including all plots generated)

Examples

data(intracell_raw_se)
Res <- pool_estimation(
    data = intracell_raw_se,
    metadata_info = c(PoolSamples = "Pool", Conditions = "Conditions")
)

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- pool_estimation(
    data = Intra[, -c(1:3)],
    metadata_sample = Intra[, c(1:3)],
    metadata_info = c(PoolSamples = "Pool", Conditions = "Conditions")
)

Description

Applies modular normalization including 80% filtering rule, total-ion count normalization, missing value imputation, and outlier detection using Hotelling's T2 test.

Usage

processing(
  data,
  metadata_sample = NULL,
  metadata_feature = NULL,
  metadata_info = c(Conditions = "Conditions"),
  featurefilt = "Modified",
  cutoff_featurefilt = 0.8,
  tic = TRUE,
  mvi = TRUE,
  mvi_percentage = 50,
  hotellins_confidence = 0.99,
  core = FALSE,
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List with two elements: DF (all output tables generated) and Plot (all plots generated). If featurefilt is not NULL, DF includes a Feature_matrix table listing every input feature (not only the removed ones) with a FilteredFeatures column (TRUE/FALSE). If data is a SummarizedExperiment, the same FilteredFeatures column is added to the rowData (feature matrix) of the returned data_Rawdata SummarizedExperiment.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
ResI <- processing(
    data = Intra[1:30, -c(1:3)],
    metadata_sample = Intra[1:30, c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    ),
    save_plot = NULL,
    save_table = NULL,
    print_plot = FALSE
)

Description

Merges the analytical replicates of an experiment

Usage

replicate_sum(
  data,
  metadata_sample = NULL,
  metadata_info = c(Conditions = "Conditions", Biological_Replicates =
    "Biological_Replicates", Analytical_Replicates = "Analytical_Replicates"),
  save_table = "csv",
  path = NULL
)

Arguments

Value

DF with the merged analytical replicates

Examples

data(intracell_raw_se)
Res <- replicate_sum(data = intracell_raw_se)

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- replicate_sum(
    data = Intra[-c(49:58), -c(1:3)],
    metadata_sample = Intra[-c(49:58), c(1:3)]
)

Description

Creates a long-format permutation table where each row represents one unique unordered pair of seed IDs from the same input row, then flags whether each pair is compatible via direct or secondary graph connections.

Usage

seed_id_compatibility_check(
  data,
  id_types = c("HMDB", "KEGG", "CHEBI", "PUBCHEM"),
  delimiter = c(";", ","),
  verbose = FALSE,
  edge_table = NULL,
  handle_partially_compatible = FALSE,
  handle_completely_incompatible = FALSE,
  completely_incompatible_priority = c("HMDB", "CHEBI", "PUBCHEM", "KEGG")
)

Arguments

Value

Named list with at least five data frames:

If either handling option is enabled, the return object also includes handling_summary_text and handling_summary_metrics.

Description

Can be applied on the result of differential metabolite analysis (DMA), requires a pathway list (from databases).

Usage

standard_ora(
  data,
  metadata_info = c(pvalColumn = "p.adj", percentageColumn = "t.val", PathwayTerm =
    "term", PathwayFeature = "Metabolite"),
  cutoff_stat = 0.05,
  cutoff_percentage = 10,
  input_pathway,
  pathway_name = "",
  min_gssize = 10,
  max_gssize = 1000,
  save_table = "csv",
  path = NULL
)

Arguments

Value

Saves results as individual .csv files.

Examples

KEGG_Pathways <- metsigdb_kegg()
data(intracell_dma) # loads the object into your environment
DMAres <- intracell_dma %>%
    dplyr::filter(!is.na(KEGGCompound)) %>%
    tibble::column_to_rownames("KEGGCompound") %>%
    dplyr::select(-"Metabolite")
RES <- standard_ora(
    data = DMAres,
    input_pathway = KEGG_Pathways
)

Description

Total ion count normalization

Usage

tic_norm(data, metadata_sample, metadata_info, tic = TRUE)

Arguments

Value

List with two elements: DF (including output table) and Plot (including all plots generated)

This function can be used independently on numeric data with matching sample metadata. Missing values and zero handling should already be in a reasonable state for the intended normalization.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- tic_norm(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(Conditions = "Conditions")
)

Description

We performed differential metabolite analysis comparing ccRCC tissue versus adjacent normal tissue using median normalised data from the supplementary table 2 of Hakimi et. al.(="Tissue_Norm"). metabolite values used as input with row names being metabolite trivial names. doi:10.1016/j.ccell.2015.12.004

Usage

tissue_dma

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 570 rows and 17 columns.

Examples

data(tissue_dma)
head(tissue_dma)

Description

We performed differential metabolite analysis comparing ccRCC tissue versus adjacent normal tissue of the patient's subset of old patient's (age > 58 years) using median normalised data from the supplementary table 2 of Hakimi et. al.(="Tissue_Norm"). metabolite values used as input with row names being metabolite trivial names. doi:10.1016/j.ccell.2015.12.004

Usage

tissue_dma_old

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 570 rows and 17 columns.

Examples

data(tissue_dma_old)
head(tissue_dma_old)

Description

We performed differential metabolite analysis comparing ccRCC tissue versus adjacent normal tissue of the patient's subset of young patient's (age <42 years) using median normalised data from the supplementary table 2 of Hakimi et. al.(="Tissue_Norm"). metabolite values used as input with row names being metabolite trivial names. doi:10.1016/j.ccell.2015.12.004

Usage

tissue_dma_young

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 570 rows and 17 columns.

Examples

data(tissue_dma_young)
head(tissue_dma_young)

Description

In Hakimi et. al. metabolites were assigned to metabolite IDs, pathways, platform, mass and other fetaure metainformation. row names being metabolite trivial names. doi:10.1016/j.ccell.2015.12.004

Usage

tissue_meta

Format

An object of class tbl_df (inherits from tbl, data.frame) with 877 rows and 11 columns.

Examples

data(tissue_meta)
head(tissue_meta)

Description

This is median normalised data from the supplementary table 2 of Hakimi et al with metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs. measured metabolite (normalised data) doi:10.1016/j.ccell.2015.12.004

Usage

tissue_norm

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 276 rows and 584 columns.

Examples

data(tissue_norm)
head(tissue_norm)

Description

This is median normalised data from the supplementary table 2 of Hakimi et al with metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs. coldata include patient metadata (e.g. age, gender, sage, etc.) doi:10.1016/j.ccell.2015.12.004

Usage

tissue_norm_se

Format

An object of class SummarizedExperiment with 570 rows and 276 columns.

Examples

data(tissue_norm_se)
head(tissue_norm_se)

Description

The processed proteomics data was downloaded from the supplementary table 3 of Mora & Schmidt et. al., which used the study from Clark et. al. under Proteomics data Commons PDC000127. on their regulation regulation in renal cancer, Genome Medicine 2024, doi:10.1186/s13073-024-01415-3 Clark et. al, Integrated proteogenomic characterization of clear cell renal cell carcinoma, Cell 2019, doi:10.1016/j.cell.2019.10.007

Usage

tissue_tvn_proteomics

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 8769 rows and 10 columns.

Examples

data(tissue_tvn_proteomics)
head(tissue_tvn_proteomics)

Description

The processed transcriptomics data was downloaded from the supplementary table 3 of Mora & Schmidt et. al., which used the study from Clark et. al. under Proteomics data Commons PDC000127. on their regulation regulation in renal cancer, Genome Medicine 2024, doi:10.1186/s13073-024-01415-3 Clark et. al, Integrated proteogenomic characterization of clear cell renal cell carcinoma, Cell 2019, doi:10.1016/j.cell.2019.10.007

Usage

tissue_tvn_rnaseq

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 29283 rows and 10 columns.

Examples

data(tissue_tvn_rnaseq)
head(tissue_tvn_rnaseq)

Description

Translate IDs to/from KEGG, PubChem, Chebi, HMDB

Usage

translate_id(
  data,
  metadata_info = c(InputID = "MetaboliteID", grouping_variable = "term"),
  from = "kegg",
  to = c("pubchem", "chebi", "hmdb", "cas"),
  summary = FALSE,
  save_table = "csv",
  path = NULL
)

Arguments

Value

List with at least three DFs: 1) Original data and the new column of translated ids spearated by comma. 2) Mapping information between Original ID to Translated ID. 3) Mapping summary between Original ID to Translated ID.

Examples

## Not run: 
KEGG_Pathways <- metsigdb_kegg()
Res <- translate_id(
    data = KEGG_Pathways,
    metadata_info = c(
        InputID = "MetaboliteID",
        grouping_variable = "term"
    ),
    from = c("kegg"),
    to = c("pubchem", "hmdb")
)

## End(Not run)

Description

Traverses pairwise RaMP mappings from OmnipathR::ramp_id_mapping_table() across selected metabolite ID types until no new IDs are found.

Usage

traverse_ids(
  data,
  id_types = c("HMDB", "KEGG", "CHEBI", "PUBCHEM"),
  delimiter = c(";", ","),
  save_table = "csv",
  path = NULL,
  verbose = FALSE,
  run_compatibility_check = TRUE,
  edge_table = NULL
)

Arguments

Value

Named list with three data frames:

Examples

input_df <- data.frame(
    name = c(
        "Acetone ; Propanal ; acetone",
        "Acetaldehyde oxime ; HMDB01122",
        "acetate",
        "Urea"
    ),
    all_ids = c(
        "HMDB01659 ; HMDB03366 ; C00207",
        "HMDB03656 ; HMDB01122",
        "C00033",
        "C00086"
     ),
     HMDB = c(
        "HMDB01659; HMDB03366",
        "HMDB03656; HMDB01122",
        NA,
        NA
     ),
     KEGG = c(
        "C00207",
        NA,
        "C00033",
        "C00086"
     ),
     CHEBI = NA,
     stringsAsFactors = FALSE
)

res <- traverse_ids(input_df)

df_translated <- res$ExpandedDF
head(df_translated)

Description

Graph visualization for clustered terms

Usage

viz_graph(
  similarity_matrix,
  clusters,
  plot_threshold = 0.5,
  plot_name = "ClusterGraph",
  max_nodes = NULL,
  min_degree = NULL,
  node_sizes = NULL,
  show_density = FALSE,
  save_plot = "svg",
  print_plot = TRUE,
  path = NULL,
  plot_width = 3000,
  plot_height = 2000,
  plot_unit = "px"
)

Arguments

Value

Graph plot as a ggplot object.

Examples

# Create toy similarity matrix and clusters
sim <- matrix(
    c(1, 0.8, 0.3, 0.1,
      0.8, 1, 0.2, 0.1,
      0.3, 0.2, 1, 0.7,
      0.1, 0.1, 0.7, 1),
    nrow = 4,
    dimnames = list(
        c("Pathway_A", "Pathway_B", "Pathway_C", "Pathway_D"),
        c("Pathway_A", "Pathway_B", "Pathway_C", "Pathway_D")
    )
)
clusters <- c(
    Pathway_A = "1", Pathway_B = "1",
    Pathway_C = "2", Pathway_D = "2"
)
viz_graph(
    sim, clusters,
    plot_threshold = 0.2,
    save_plot = NULL,
    print_plot = FALSE
)

Description

Heatmap visualization

Usage

viz_heatmap(
  data,
  metadata_info = NULL,
  metadata_sample = NULL,
  metadata_feature = NULL,
  plot_name = "",
  scale = "row",
  save_plot = "svg",
  enforce_featurenames = FALSE,
  enforce_samplenames = FALSE,
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List with two elements: Plot and Plot_Sized

Examples

data(intracell_raw_se)
Res <- viz_heatmap(data = intracell_raw_se)

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- viz_heatmap(data = Intra[, -c(1:3)])

Description

PCA plot visualization

Usage

viz_pca(
  data,
  metadata_info = NULL,
  metadata_sample = NULL,
  color_palette = NULL,
  scale_color = "discrete",
  shape_palette = NULL,
  show_loadings = FALSE,
  scaling = TRUE,
  pcx = 1,
  pcy = 2,
  theme = NULL,
  plot_name = "",
  save_plot = "svg",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List with two elements: Plot and Plot_Sized

Examples

data(intracell_raw_se)
Res <- viz_pca(intracell_raw_se)

data(intracell_raw)
Intra <- intracell_raw[, -c(2:4)] %>% tibble::column_to_rownames("Code")
Res <- viz_pca(Intra)

Description

Plot measured metabolites together with the prior knowledge (PK) terms they are linked to, e.g. proteins from metsigdb_metalinks(), pathways from metsigdb_kegg() or cancer types from metsigdb_macdb(). Every link between a metabolite and a term in input_pk becomes an edge.

Usage

viz_pk_network(
  feature_metadata,
  input_pk,
  metadata_info,
  term_metadata = NULL,
  id_type = "HMDB",
  id_sep = ";",
  label_mode = c("reduced", "all"),
  label_max_chars = 20,
  label_degree_min = 2,
  label_repel = TRUE,
  seed = NULL,
  layout = "fr",
  plot_name = "PK_Network",
  save_plot = "svg",
  save_table = NULL,
  print_plot = TRUE,
  path = NULL,
  plot_width = 25,
  plot_height = 20,
  plot_unit = "cm"
)

Arguments

Details

The metabolites in feature_metadata are matched to input_pk by ID. A cell may hold several IDs separated by id_sep. With id_type = "HMDB", HMDB IDs are brought into the same 7-digit form on both sides (e.g. "HMDB00123" and "HMDB0000123" match); all other IDs are matched exactly. Use translate_id() beforehand if the data and the PK use different ID types.

Node colours and sizes and edge colours, line types and widths can be mapped to columns via metadata_info. Numeric columns get a continuous scale (diverging around 0 if the values have both signs, e.g. Log2FC), other columns a discrete one. If several rows map to the same node or edge, numeric values are averaged and otherwise the first non-missing value is used. Nodes without a size value are drawn at the smallest size.

Edges are undirected unless metadata_info contains EdgeDirection. This column must hold "to_term" (arrow from metabolite to term) or "to_metabolite" (arrow from term to metabolite); edges with other values stay undirected. metsigdb_metalinks() provides such a column, direction, together with the interaction category interaction.

Value

A list with

See Also

viz_shared_pk_network() to connect metabolites by the terms they share; cluster_pk() to connect terms by the metabolites they share.

Examples

# Biocrates amino acids and the transporters they interact with
data(biocrates_features)
amino_acids <- biocrates_features %>%
    dplyr::filter(Class == "Aminoacids") %>%
    dplyr::select(TrivialName, HMDB)
aa_hmdb <- trimws(unlist(strsplit(amino_acids$HMDB, ",")))

transporters <- metsigdb_metalinks(
    hmdb_ids = aa_hmdb,
    save_table = NULL,
    exclude_metabolites = NULL
) %>%
    dplyr::filter(interaction_family == "Transporter-metabolite")

network <- viz_pk_network(
    feature_metadata = amino_acids,
    input_pk = transporters,
    metadata_info = c(
        InputID = "HMDB",
        InputLabel = "TrivialName",
        PriorID = "hmdb",
        PriorTerm = "gene_symbol",
        EdgeColor = "interaction",
        EdgeDirection = "direction"
    ),
    id_sep = ",",
    seed = 1,
    save_plot = NULL
)

Description

Connect measured metabolites that are linked to the same prior knowledge (PK) terms, e.g. metabolites binding the same receptors in metsigdb_metalinks(), taking part in the same pathways in metsigdb_kegg() or reported in the same cancer types in metsigdb_macdb(). Node size and the number beneath each label show how many terms a metabolite is linked to; edge width and labels show the similarity of two metabolites.

Usage

viz_shared_pk_network(
  feature_metadata,
  input_pk,
  metadata_info,
  similarity = c("shared", "jaccard"),
  threshold = 0,
  show_unconnected = FALSE,
  edge_labels = TRUE,
  id_type = "HMDB",
  id_sep = ";",
  label_mode = c("all", "reduced"),
  label_max_chars = 20,
  label_degree_min = 1,
  label_repel = TRUE,
  seed = NULL,
  layout = "stress",
  plot_name = "Shared_PK_Network",
  save_plot = "svg",
  save_table = NULL,
  print_plot = TRUE,
  path = NULL,
  plot_width = 25,
  plot_height = 20,
  plot_unit = "cm"
)

Arguments

Details

Metabolites are matched to input_pk as in viz_pk_network(), so the same metadata_info can be passed to both functions. Only InputID, InputLabel, PriorID, PriorTerm, MetaboliteColor and MetaboliteSize are used here; other entries are ignored.

The raw number of shared terms favours metabolites with many terms. Use similarity = "jaccard" to compare the term profiles of two metabolites as a whole. The Jaccard index is calculated as in cluster_pk().

Metabolites that share no terms with any other plotted metabolite are left out of the plot unless show_unconnected = TRUE; they are still listed in the returned nodes table with a degree of 0.

To connect terms by the metabolites they share instead, use cluster_pk(): with input_format = "enrichment" it takes an enrichment result and only uses the measured metabolites behind each term.

Value

A list with

See Also

viz_pk_network() to show the terms themselves.

Examples

# Biocrates amino acids connected by the transporters they share
data(biocrates_features)
amino_acids <- biocrates_features %>%
    dplyr::filter(Class == "Aminoacids") %>%
    dplyr::select(TrivialName, HMDB)
aa_hmdb <- trimws(unlist(strsplit(amino_acids$HMDB, ",")))

transporters <- metsigdb_metalinks(
    hmdb_ids = aa_hmdb,
    save_table = NULL,
    exclude_metabolites = NULL
) %>%
    dplyr::filter(interaction_family == "Transporter-metabolite")

shared <- viz_shared_pk_network(
    feature_metadata = amino_acids,
    input_pk = transporters,
    metadata_info = c(
        InputID = "HMDB",
        InputLabel = "TrivialName",
        PriorID = "hmdb",
        PriorTerm = "gene_symbol"
    ),
    similarity = "jaccard",
    id_sep = ",",
    seed = 1,
    save_plot = NULL
)

Description

Bar, Box or Violin plot in Superplot style visualization

Usage

viz_superplot(
  data,
  metadata_sample = NULL,
  metadata_info = c(Conditions = "Conditions", Superplot = NULL),
  plot_type = "Box",
  plot_name = "",
  plot_conditions = NULL,
  stat_comparison = NULL,
  pval = NULL,
  padj = NULL,
  xlab = NULL,
  ylab = NULL,
  theme = NULL,
  color_palette = NULL,
  color_palette_dot = NULL,
  save_plot = "svg",
  print_plot = TRUE,
  path = NULL
)

Arguments

Value

List with two elements: Plot and Plot_Sized

Examples

data(intracell_raw_se)
# only plot the first 2 metabolites
Res <- viz_superplot(data = intracell_raw_se[1:2, , drop = FALSE])

data(intracell_raw)
Intra <- intracell_raw[, c(1:6)] %>% tibble::column_to_rownames("Code")
Res <- viz_superplot(
    data = Intra[, -c(1:3)],
    metadata_sample = Intra[, c(1:3)]
)

Description

Volcano plot

Usage

viz_volcano(
  plot_types = "Standard",
  data,
  metadata_info = NULL,
  metadata_feature = NULL,
  data2 = NULL,
  y = "p.adj",
  x = "Log2FC",
  xlab = NULL,
  ylab = NULL,
  cutoff_x = 0.5,
  cutoff_y = 0.05,
  connectors = FALSE,
  select_label = "",
  plot_name = "",
  subtitle = "",
  name_comparison = c(data = "Cond1", data2 = "Cond2"),
  color_palette = NULL,
  shape_palette = NULL,
  theme = NULL,
  save_plot = "svg",
  path = NULL,
  feature = "Metabolites",
  print_plot = TRUE
)

Arguments

Value

List with two elements: Plot and Plot_Sized

Examples

data(intracell_dma)
Intra <- intracell_dma %>% tibble::column_to_rownames("Metabolite")
Res <- viz_volcano(data = Intra)