Package: specmine 3.1.8

Pedro Fontão

specmine: Metabolomics and Spectral Data Analysis and Mining

Provides methods for metabolomics and spectral data analysis, including data import, preprocessing, visualization, univariate and multivariate analysis, machine learning, feature selection, and pathway analysis. The package supports analytical workflows for different data types used in metabolomics and spectroscopy. Some optional functionality uses the suggested packages 'cyjShiny' and 'specmine.datasets'. The package 'specmine.datasets' is maintained separately at <https://github.com/PedroFontao/specmine.datasets>.

Authors:Christopher Costa [aut], Marcelo Maraschin [aut], Miguel Rocha [aut], Sara Cardoso [aut], Telma Afonso [aut], Bruno Pereira [aut], Pedro Fontão [aut, cre], C. Beleites [cph], Jie Hao [cph]

specmine_3.1.8.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
specmine/json (API)

# Install 'specmine' in R:

install.packages('specmine', repos = c('https://pedrofontao.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pedrofontao/specmine/issues

Datasets:

spectra_options - Spectra processing options

On CRAN:

4.68 score 1 stars 12 scripts 4 downloads 4 mentions 86 exports 162 dependencies

Last updated from:895de2693d. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	353
source / vignettes	OK	358
linux-release	OK	344
macos-release	OK	228
macos-oldrel	OK	239
windows-devel	OK	220
windows-release	OK	183
windows-oldrel	OK	202
wasm-release	OK	239

Exports:aggregate_samples aov_all_vars aov_one_var apply_by_group apply_by_groups baseline_correction convert_chebi_to_kegg convert_from_chemospec convert_hmdb_to_kegg convert_keggpathway_2_reactiongraph convert_multiple_spcmnm_to_kegg count_missing_values count_missing_values_per_sample count_missing_values_per_variable create_dataset create_pathway_with_reactions dataset_from_peaks feature_selection filter_feature_selection flat_pattern_filter get_cpd_names get_files_list_per_assay get_metabolights_study get_metabolights_study_files_assay get_metabolights_study_metadata_assay get_metabolights_study_samples_files get_MetabolitePath get_metabPaths_org get_OrganismsCodes get_paths_with_cpds_org get_peak_values get_samples_names_dx get_x_label get_x_values_as_text impute_nas_knn impute_nas_mean impute_nas_median impute_nas_value merge_data_metadata metabolights_studies_list missingvalues_imputation multiClassSummary pathway_analysis pca_analysis_dataset pca_biplot pca_biplot3D pca_kmeans_plot2D pca_kmeans_plot3D pca_pairs_kmeans_plot pca_pairs_plot pca_plot_3d pca_robust pca_scoresplot2D pca_scoresplot3D pca_scoresplot3D_rgl pca_screeplot peak_detection2d read_csvs_folder read_data_csv read_data_dx read_dataset_csv read_dataset_dx read_metadata read_ms_spectra read_multiple_csvs read_spc_nosubhdr recursive_feature_elimination remove_data remove_data_variables remove_metadata_variables remove_samples remove_samples_by_na_metadata remove_samples_by_nas remove_variables_by_nas remove_x_values_by_interval subset_by_samples_and_xvalues subset_metadata subset_random_samples subset_samples subset_samples_by_metadata_values subset_x_values subset_x_values_by_interval summary_var_importance train_and_predict train_classifier train_models_performance

Dependencies:annotate AnnotationDbi askpass base64enc baseline Biobase BiocBaseUtils BiocGenerics Biostrings bit bit64 blob bslib cachem caret class cli clock codetools colorspace commonmark compare cpp11 crayon crosstalk curl data.table DBI diagram digest dplyr e1071 ellipse evaluate farver fastmap fontawesome forcats foreach forecast fracdiff fs future future.apply genefilter generics GGally ggplot2 ggstats ggtext globals glue gower gridtext gtable hardhat highr hms htmltools htmlwidgets httr impute imputeTS ipred IRanges isoband iterators jpeg jquerylib jsonlite KEGGREST KernSmooth knitr labeling later lattice lava lazyeval lifecycle listenv litedown lmtest lubridate magrittr markdown MASS Matrix MatrixGenerics matrixStats memoise Metrics mime ModelMetrics mvtnorm narray nlme nnet numDeriv openssl otel parallelly patchwork pcaPP pillar pkgconfig plotly pls plyr png prettyunits pROC prodlim progress progressr promises proxy purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo readJDX recipes reshape2 rlang rmarkdown rpart RSQLite S4Vectors S7 sass scales Seqinfo shape SparseM sparsevctrs SQUAREM stinepack stringi stringr survival sys tibble tidyr tidyselect timechange timeDate tinytex tzdb urca utf8 vctrs viridisLite withr xfun XML xml2 xtable XVector yaml zoo

User guide to analyze 2D-NMR metabolomics' experiments using specmine package

Rendered fromdataset_2d_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-07-15
Started: 2026-07-15

Help page	Topics
Aggregate samples	aggregate_samples
Analysis of variance	aov_all_vars
Analysis of variance for one variable	aov_one_var
Apply by group	apply_by_group
Apply by groups	apply_by_groups
Get kegg codes from hmdb codes:	convert_hmdb_to_kegg
Convert KEGGPathway object to graph object	convert_keggpathway_2_reactiongraph
Get kegg codes from spcmnm codes:	convert_multiple_spcmnm_to_kegg
Count missing values	count_missing_values
Count missing values per sample	count_missing_values_per_sample
Count missing values per variable	count_missing_values_per_variable
Create dataset	create_dataset
Creates the pathway, with reactions included in the nodes	create_pathway_with_reactions
Create a dataset from peak lists	dataset_from_peaks
Feature Selection Using Univariate Filters	filter_feature_selection
Flat pattern filter	flat_pattern_filter
Get compound names from KEGG codes	get_cpd_names
Download a complete MetaboLights study	get_metabolights_study
Download files for one MetaboLights assay	get_metabolights_study_files_assay
Get metadata for one MetaboLights assay	get_metabolights_study_metadata_assay
Get sample-file mapping for one MetaboLights assay	get_metabolights_study_samples_files
Returns an object of KEGGPathway of the pathway especified in pathcode	get_MetabolitePath
Get vector with paths numbers that occur in the given organism, named with the full path name:	get_metabPaths_org
Get code, t number, full name and phylogeny of all organisms in KEGG:	get_OrganismsCodes
Get only the paths of the organism that contain given compounds:	get_paths_with_cpds_org
Get x label	get_x_label
Get x values as text	get_x_values_as_text
Impute missing values with kNN	impute_nas_knn
Impute missing values with mean	impute_nas_mean
Impute missing values with median	impute_nas_median
Impute missing values with a constant	impute_nas_value
Merge data and metadata	merge_data_metadata
List public MetaboLights studies	metabolights_studies_list
Missing values imputation	missingvalues_imputation
Multi-class summary metrics	multiClassSummary
Creates the pathway wanted. If any of the given compounds is present in the pathway, it is coloured differently.	pathway_analysis
PCA biplot	pca_biplot
PCA 3D biplot	pca_biplot3D
PCA 2D k-means plot	pca_kmeans_plot2D
PCA 3D k-means plot	pca_kmeans_plot3D
PCA pairs plot with k-means clusters	pca_pairs_kmeans_plot
PCA pairs plot	pca_pairs_plot
Robust PCA analysis	pca_robust
PCA 2D scores plot	pca_scoresplot2D
PCA 3D scores plot	pca_scoresplot3D
PCA 3D scores plot using rgl	pca_scoresplot3D_rgl
PCA scree plot	pca_screeplot
Detect peaks in 2D NMR spectra	peak_detection2d
Read all CSV peak files in a folder	read_csvs_folder
Read JDX spectra files from a folder	read_data_dx
Reads a dataset from CSV files	read_dataset_csv
Read a dataset from JDX files	read_dataset_dx
Reads metadata from a CSV file	read_metadata
Read multiple CSV peak files	read_multiple_csvs
Import for Thermo Galactic's spc file format These functions allow to import .spc files. A detailed description of the .spc file format is available at	read_spc_nosubhdr
Recursive Feature Elimination	recursive_feature_elimination
Remove data	remove_data
Remove data variables	remove_data_variables
Remove metadata variables	remove_metadata_variables
Remove samples	remove_samples
Remove samples by NA metadata	remove_samples_by_na_metadata
Remove samples by NAs	remove_samples_by_nas
Remove variables by NAs	remove_variables_by_nas
Remove x values by interval	remove_x_values_by_interval
Spectra processing options	spectra_options
Subset by samples and x values	subset_by_samples_and_xvalues
Subset metadata	subset_metadata
Subset random samples	subset_random_samples
Subset samples	subset_samples
Subset samples by metadata values	subset_samples_by_metadata_values
Subset x values	subset_x_values
Subset x values by interval	subset_x_values_by_interval
Summarise variable importance tables	summary_var_importance
Train a classifier and predict new samples	train_and_predict
Train a classifier	train_classifier
Train multiple models and compare their performance	train_models_performance

Package: specmine 3.1.8

specmine: Metabolomics and Spectral Data Analysis and Mining

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