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specmine (version 2.0.3)

Metabolomics and Spectral Data Analysis and Mining

Description

Provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, feature selection and pathway analysis. Case studies can be found on the website: . 'rcytoscapejs' is not present in a mainstream repository, but it can be obtained by typing 'devtools::install_github('cytoscape/r-cytoscape.js')' on the R command line.

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Version

Install

install.packages('specmine')

Monthly Downloads

24

Version

2.0.3

License

GPL (>= 2)

Maintainer

Miguel Rocha

Last Published

May 15th, 2018

Functions in specmine (2.0.3)

MAIT_identify_metabolites

MAIT metabolite identification

apply_by_sample

Apply function to samples

convert_keggpathway_2_reactiongraph

Convert KEGGPathway object to graph object.

Analysis of variance

convert_to_factor

Convert metadata to factor

background_correction

Background correction

baseline_correction

Baseline correction

boxplot_variables

Boxplot of variables

detect_nmr_peaks_from_dataset

Detection of the peaks in an NMR spectra dataset.

boxplot_vars_factor

Boxplot of variables with metadata's variable factors

Perform cluster analysis

apply_by_variable

Apply function to variables

apply_by_groups

Apply by groups

feature_selection

Perform feature selection

get_OrganismsCodes

Get all organisms in KEGG.

Human Cachexia data

compare_regions_by_sample

Compare regions by sample

Get the names of the compounds that correspond to the kegg codes given.

Cassava Postharvest Physiological Deterioration

count_missing_values

Count missing values

create_pathway_with_reactions

Creates the pathway, with reactions included in the nodes.

count_missing_values_per_sample

Count missing values per sample

Find the best correlation value for the clustering of peaks

cubic_root_transform

Cubic root transformation

choose_nmr_references

Choose the metabolites' spectra to be the reference.

filter_feature_selection

Perform selection by filter

Get type of data

get_metadata_value

Get metadata value

correlations_dataset

Dataset correlations

correlations_test

Correlations test

correlation_test

Correlation test of two variables or samples

get_metabolights_study_list

List of metabolights studies available to download.

convert_to_hyperspec

Convert to hyperspec

count_missing_values_per_variable

Count missing values per variable

get_metabolights_study

Download Metabolights study files.

find_equal_samples

Find equal samples

convert_from_hyperspec

Convert from hyperspec

get_value_label

Get value label

dendrogram_plot

Plot dendrogram

Get x-axis label

get_x_values_as_num

Get x-axis values as numbers

convert_hmdb_to_kegg

Convert HMDB codes to KEGG codes.

Impute missing values with KNN

dendrogram_plot_col

Plot dendrogram

get_x_values_as_text

Get x-axis values as text

impute_nas_linapprox

Impute missing values with linear approximation

flat_pattern_filter

Flat pattern filter

linreg_coef_table

Linear regression coefficient table

Check type of data

first_derivative

First derivative

data_correction

Data correction

linreg_pvalue_table

Linear regression p-values table

Logarithmic transformation.

dataset_from_peaks

Dataset from peaks

Data frame with the connections between the metabolic pathways.

Cluster of variables (peaks) in a NMR peaks dataset for metabolite identification.

low_level_fusion

Low level fusion

Fold change analysis

fold_change_var

Fold change applied on two variables

get_metabPaths_org

Get the metabolic pathways present in given organism.

get_MetabolitePath

Returns an object of KEGGPathway of the pathway especified in pathcode.

get_hmdbs_with_specs_id

HMDB code and name of HMDB 1d-NMR spectra.

Get data as data frame

get_samples_names_dx

Get sample's names from DX files

get_samples_names_spc

Get sample's names from SPC files

impute_nas_value

Impute missing values with value replacement

indexes_to_xvalue_interval

Get the x-values of a vector of indexes

multifactor_aov_all_vars

Multifactor ANOVA

multifactor_aov_pvalues_table

Multifactor ANOVA p-values table

get_data_values

Get data values

get_peak_values

Get peak values

3D PCA biplot (interactive)

get_sample_names

Get sample names

impute_nas_median

Impute missing values with median

impute_nas_mean

Impute missing values with mean

Kolmogorov-Smirnov tests on dataset

get_metadata_var

Get metadata variable

nmr_identification

Function to do NMR metabolite identification

linreg_rsquared

Linear regression r-squared

pca_scoresplot2D

2D PCA scores plot

linreg_all_vars

Linear Regression

get_paths_with_cpds_org

Get only the paths of the organism that contain one or more of the given compounds.

heatmap_correlations

Correlations heatmap

linregression_onevar

Linear regression on one variable

pca_scoresplot3D

3D PCA scores plot

hierarchical_clustering

Perform hierarchical clustering analysis

pca_analysis_dataset

PCA analysis (classical)

List of reference 1D-NMR peak lists.

nmr_1d_spectra_options

Conditions of acquirement of the different refernece peak lists.

Plot the peaks of a MS or NMR dataset.

plot_regression_coefs_pvalues

Plot regression coefficient and p-values

kmeans_result_df

Show cluster's members

kmeans_clustering

Perform k-means clustering analysis

predict_samples

Predict samples

kruskalTest_dataset

Kruskal-Wallis tests on dataset

normalize_samples

Normalize samples

metadata_as_variables

Metadata as variables

missingvalues_imputation

Missing values imputation

PCA analysis (robust)

plot_kruskaltest

Plot Kruskal-Wallis tests results

Brazilian Propolis from different Harvest Seasons and different Agroecological Regions (dataset)

pca_scoresplot3D_rgl

3D PCA scores plot (interactive)

multifactor_aov_varexp_table

Multifactor ANOVA variability explained table

recursive_feature_elimination

Perform recursive feature elimination

merge_data_metadata

Merge data and metadata

Plot kmeans clusters

Plot Kolmogorov-Smirnov tests results

Get number of samples

Read data from (J)DX files

Merge two datasets

Get number of x values

Multiplicative scatter correction

read_ms_spectra

Read MS spectra

pca_kmeans_plot2D

2D PCA k-means plot

Plot ANOVA results

multiClassSummary

Multi Class Summary

pca_kmeans_plot3D

3D PCA k-means plot (interactive)

plot_fold_change

Plot fold change results

offset_correction

Offset correction

remove_samples_by_nas

Remove samples by NAs

replace_metadata_value

Replace metadata's value

remove_variables_by_nas

Remove variables by NAs

Plot t-tests results

pathway_analysis

Creates the metabolic pathway wanted. If any of the given compounds is present in the pathway, it is coloured differently.

remove_samples_by_na_metadata

Remove samples by NA on metadata

Set new x-values

plotvar_twofactor

Plot variable distribution on two factors

pca_pairs_kmeans_plot

PCA k-means pairs plot

shift_correction

Shift correction

peaks_per_sample

Peaks per sample

read_dataset_dx

Read dataset from (J)DX files

Savitzky-golay transformation

read_dataset_spc

Read dataset from SPC files

peaks_per_samples

Peaks per samples

read_Bruker_files

Read Bruker processed spectra.

plot_spectra_simple

Plot spectra (simple)

read_csvs_folder

Read CSVs from folder

smoothing_interpolation

Smoothing interpolation

Standard Normal Variate

remove_x_values_by_interval

Remove x-values by interval

replace_data_value

Replace data value

subset_samples_by_metadata_values

Subset samples by metadata values

Dataset summary

subset_x_values

Subset x-values

subset_x_values_by_interval

Subset x-values by interval

propolisSampleList

Brazilian Propolis from different Harvest Seasons and different Agroecological Regions (sample list)

read_spc_nosubhdr

Import for Thermo Galactic's spc file format These functions allow to import .spc files.

summary_var_importance

Summary of variables importance

scaling_samples

Scale data matrix

x_values_to_indexes

Get x-values indexes

stats_by_variable

Statistics of variables

Read data from SPC files

read_varian_spectra_raw

Function that reads raw spectra (intensity over time spectra) from the varian format and processes them to ppm spectra.

xvalue_interval_to_indexes

Get indexes of an interval of x-values

read_multiple_csvs

Read multiple CSVs

t-Tests on dataset

subset_by_samples_and_xvalues

Subset by samples and x-values

train_and_predict

Train and predict

read_dataset_csv

Read dataset from CSV

variables_as_metadata

Variables as metadata

remove_peaks_interval

Remove interval of peaks

set_value_label

Set value label

remove_peaks_interval_sample_list

Remove interval of peaks (sample list)

remove_data_variables

Remove data variables

volcano_plot_fc_tt

Brazilian Propolis from different Harvest Seasons and different Agroecological Regions

remove_metadata_variables

Remove metadata's variables

Set new metadata

train_classifier

Train classifier

stats_by_sample

Statistics of samples

set_sample_names

Set samples names

train_models_performance

subset_metadata

Subset metadata

subset_random_samples

Subset random samples

transmittance_to_absorbance

Convert transmittance to absorbance

values_per_peak

Values per peak

values_per_sample

Values per peak

absorbance_to_transmittance

Convert absorbance to transmittance

aggregate_samples

Aggregate samples

conversion_table

Conversion table.

convert_from_chemospec

Convert from ChemoSpec