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specmine (version 1.0)

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 and feature selection. Case studies can be found on the website: http://darwin.di.uminho.pt/metabolomics .

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Version

Install

install.packages('specmine')

Monthly Downloads

24

Version

1.0

License

GPL (>= 2)

Maintainer

Christopher Costa

Last Published

November 10th, 2015

Functions in specmine (1.0)

background_correction

Background correction
get_x_values_as_text

Get x-axis values as text
impute_nas_median

Impute missing values with median
ksTest_dataset

Kolmogorov-Smirnov tests on dataset
flat_pattern_filter

Flat pattern filter
cachexia

Human Cachexia data
apply_by_groups

Apply by groups
train_models_performance

Train models
hierarchical_clustering

Perform hierarchical clustering analysis
pca_importance

PCA importance
get_metadata_value

Get metadata value
create_dataset

Create dataset
indexes_to_xvalue_interval

Get the x-values of a vector of indexes
fold_change

Fold change analysis
low_level_fusion

Low level fusion
normalize_samples

Normalize samples
multifactor_aov_all_vars

Multifactor ANOVA
plot_regression_coefs_pvalues

Plot regression coefficient and p-values
get_samples_names_dx

Get sample's names from DX files
plot_spectra_simple

Plot spectra (simple)
plot_spectra

Plot spectra
replace_data_value

Replace data value
impute_nas_value

Impute missing values with value replacement
remove_data_variables

Remove data variables
read_dataset_dx

Read dataset from (J)DX files
spinalCord

Brazilian Propolis from different Harvest Seasons and different Agroecological Regions
apply_by_sample

Apply function to samples
remove_x_values_by_interval

Remove x-values by interval
train_and_predict

Train and predict
mean_centering

Mean centering
shift_correction

Shift correction
subset_random_samples

Subset random samples
correlation_test

Correlation test of two variables or samples
variables_as_metadata

Variables as metadata
pca_robust

PCA analysis (robust)
pca_biplot

PCA biplot
convert_to_hyperspec

Convert to hyperspec
MAIT_identify_metabolites

MAIT metabolite identification
linreg_all_vars

Linear Regression
replace_metadata_value

Replace metadata's value
remove_samples_by_nas

Remove samples by NAs
kmeans_result_df

Show cluster's members
group_peaks

Group peaks
log_transform

Logarithmic transformation.
impute_nas_linapprox

Impute missing values with linear approximation
num_x_values

Get number of x values
read_multiple_csvs

Read multiple CSVs
savitzky_golay

Savitzky-golay transformation
baseline_correction

Baseline correction
count_missing_values_per_variable

Count missing values per variable
subset_x_values_by_interval

Subset x-values by interval
offset_correction

Offset correction
kruskalTest_dataset

Kruskal-Wallis tests on dataset
read_data_dx

Read data from (J)DX files
linreg_rsquared

Linear regression r-squared
pca_scoresplot3D

3D PCA scores plot
remove_peaks_interval

Remove interval of peaks
boxplot_vars_factor

Boxplot of variables with metadata's variable factors
get_x_values_as_num

Get x-axis values as numbers
kmeans_clustering

Perform k-means clustering analysis
plot_kruskaltest

Plot Kruskal-Wallis tests results
multiplot

Multiplot
convert_from_hyperspec

Convert from hyperspec
remove_samples_by_na_metadata

Remove samples by NA on metadata
correlations_dataset

Dataset correlations
stats_by_sample

Statistics of samples
pca_kmeans_plot2D

2D PCA k-means plot
dendrogram_plot

Plot dendrogram
plot_anova

Plot ANOVA results
get_data_values

Get data values
linreg_pvalue_table

Linear regression p-values table
plot_ttests

Plot t-tests results
dendrogram_plot_col

Plot dendrogram
pca_plot_3d

3D pca plot
check_dataset

Check dataset
get_sample_names

Get sample names
linreg_coef_table

Linear regression coefficient table
remove_variables_by_nas

Remove variables by NAs
subset_by_samples_and_xvalues

Subset by samples and x-values
pca_scoresplot2D

2D PCA scores plot
plot_kstest

Plot Kolmogorov-Smirnov tests results
impute_nas_mean

Impute missing values with mean
remove_metadata_variables

Remove metadata's variables
x_values_to_indexes

Get x-values indexes
smoothing_interpolation

Smoothing interpolation
num_samples

Get number of samples
pca_pairs_plot

PCA pairs plot
remove_peaks_interval_sample_list

Remove interval of peaks (sample list)
scaling_samples

Scale data matrix
subset_metadata

Subset metadata
peaks_per_sample

Peaks per sample
values_per_peak

Values per peak
subset_samples

Subset samples
volcano_plot_fc_tt

Volcano plot
read_csvs_folder

Read CSVs from folder
absorbance_to_transmittance

Convert absorbance to transmittance
boxplot_variables

Boxplot of variables
clustering

Perform cluster analysis
pca_pairs_kmeans_plot

PCA k-means pairs plot
peaks_per_samples

Peaks per samples
aggregate_samples

Aggregate samples
get_samples_names_spc

Get sample's names from SPC files
compare_regions_by_sample

Compare regions by sample
read_metadata

Read metadata
cubic_root_transform

Cubic root transformation
apply_by_variable

Apply function to variables
set_value_label

Set value label
kmeans_plot

Plot kmeans clusters
plotvar_twofactor

Plot variable distribution on two factors
snv_dataset

Standard Normal Variate
get_type

Get type of data
subset_samples_by_metadata_values

Subset samples by metadata values
is_spectra

Check type of data
set_metadata

Set new metadata
read_data_spc

Read data from SPC files
xvalue_interval_to_indexes

Get indexes of an interval of x-values
dataset_from_peaks

Dataset from peaks
heatmap_correlations

Correlations heatmap
get_metadata

Get metadata
summary_var_importance

Summary of variables importance
multiClassSummary

Multi Class Summary
filter_feature_selection

Perform selection by filter
propolisSampleList

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

Missing values imputation
impute_nas_knn

Impute missing values with KNN
merge_datasets

Merge two datasets
train_classifier

Train classifier
transmittance_to_absorbance

Convert transmittance to absorbance
read_data_csv

Read CSV data
metadata_as_variables

Metadata as variables
pca_screeplot

PCA scree plot
remove_samples

Remove samples
stats_by_variable

Statistics of variables
transform_data

Transform data
set_sample_names

Set samples names
convert_to_factor

Convert metadata to factor
apply_by_group

Apply by group
get_value_label

Get value label
get_data_as_df

Get data as data frame
fold_change_var

Fold change applied on two variables
pca_scoresplot3D_rgl

3D PCA scores plot (interactive)
linregression_onevar

Linear regression on one variable
recursive_feature_elimination

Perform recursive feature elimination
normalize

Normalize data
msc_correction

Multiplicative scatter correction
sum_dataset

Dataset summary
pca_kmeans_plot3D

3D PCA k-means plot (interactive)
predict_samples

Predict samples
read_dataset_spc

Read dataset from SPC files
scaling

Scale dataset
set_x_values

Set new x-values
pca_biplot3D

3D PCA biplot (interactive)
get_data

Get data
set_x_label

Set x-label
propolis

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

Subset x-values
multifactor_aov_varexp_table

Multifactor ANOVA variability explained table
tTests_dataset

t-Tests on dataset
cassavaPPD

Cassava Postharvest Physiological Deterioration
aov_all_vars

Analysis of variance
count_missing_values

Count missing values
convert_from_chemospec

Convert from ChemoSpec
find_equal_samples

Find equal samples
count_missing_values_per_sample

Count missing values per sample
get_peak_values

Get peak values
first_derivative

First derivative
get_x_label

Get x-axis label
correlations_test

Correlations test
merge_data_metadata

Merge data and metadata
data_correction

Data correction
feature_selection

Perform feature selection
get_metadata_var

Get metadata variable
get_data_value

Get data value
pca_analysis_dataset

PCA analysis (classical)
values_per_sample

Values per peak
multifactor_aov_pvalues_table

Multifactor ANOVA p-values table
plot_fold_change

Plot fold change results
read_dataset_csv

Read dataset from CSV
remove_data

Remove data
read_ms_spectra

Read MS spectra