,
Jakob Wirbel [aut, cre] ,
Georg Zeller [aut] ,
Morgan Essex [ctb],
Nicolai Karcher [ctb],
Kersten Breuer [ctb]| Title: | Statistical Inference of Associations between Microbial Communities And host phenoTypes |
|---|---|
| Description: | Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). |
| Authors: | Konrad Zych [aut] ,
Jakob Wirbel [aut, cre] ,
Georg Zeller [aut] ,
Morgan Essex [ctb],
Nicolai Karcher [ctb],
Kersten Breuer [ctb] |
| Maintainer: | Jakob Wirbel <[email protected]> |
| License: | GPL-3 |
| Version: | 2.11.0 |
| Built: | 2024-11-18 04:42:37 UTC |
| Source: | https://github.com/bioc/SIAMCAT |
Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots).
SIAMCAT is a pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes. A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed!
Maintainer: Jakob Wirbel [email protected] (ORCID)
Authors:
Konrad Zych [email protected] (ORCID)
Georg Zeller [email protected] (ORCID)
Other contributors:
Morgan Essex [email protected] [contributor]
Nicolai Karcher [contributor]
Kersten Breuer [contributor]
This function adds metadata to the feature matrix to be later used as predictors
add.meta.pred(siamcat, pred.names, std.meta = TRUE, feature.type='normalized', verbose = 1)add.meta.pred(siamcat, pred.names, std.meta = TRUE, feature.type='normalized', verbose = 1)
siamcat |
object of class siamcat-class |
pred.names |
vector of names of the variables within the metadata to be added to the feature matrix as predictors |
std.meta |
boolean, should added metadata features be standardized?,
defaults to |
feature.type |
string, on which type of features should the function
work? Can be either |
verbose |
integer, control output: |
This functions adds one or several metadata variables to the set of features, so that they can be included for model training.
Usually, this function should be called before train.model.
Numerical meta-variables are added as z-scores to the feature matrix unless specified otherwise.
Please be aware, that non-numerical metadata variables will be converted to
numerical values by using as.numeric() and could therefore lead to
errors. Thus, it makes sense to encode non-numerical metadata variables to
numerically before you start the SIAMCAT workflow.
an object of class siamcat-class with metadata added to the features
data(siamcat_example) # Add the Age of the patients as potential predictor siamcat_age_added <- add.meta.pred(siamcat_example, pred.names=c('Age')) # Add Age and BMI as potential predictors # Additionally, prevent standardization of the added features siamcat_meta_added <- add.meta.pred(siamcat_example, pred.names=c('Age', 'BMI'), std.meta=FALSE)data(siamcat_example) # Add the Age of the patients as potential predictor siamcat_age_added <- add.meta.pred(siamcat_example, pred.names=c('Age')) # Add Age and BMI as potential predictors # Additionally, prevent standardization of the added features siamcat_meta_added <- add.meta.pred(siamcat_example, pred.names=c('Age', 'BMI'), std.meta=FALSE)
Function to retrieve the list of parameters for association testing
assoc_param(siamcat, verbose=1) ## S4 method for signature 'siamcat' assoc_param(siamcat, verbose = 1)assoc_param(siamcat, verbose=1) ## S4 method for signature 'siamcat' assoc_param(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class containing the results from association testing |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function returns the list of parameters used in association testing. See check.associations for more details.
A list of parameters for association testing or NULL
data(siamcat_example) temp <- assoc_param(siamcat_example) names(temp)data(siamcat_example) temp <- assoc_param(siamcat_example) names(temp)
This function visualizes different measures of association between features and the label, computed previously with the check.associations function
association.plot(siamcat, fn.plot=NULL, color.scheme = "RdYlBu", sort.by = "fc", max.show = 50, plot.type = "quantile.box", panels = c("fc", "auroc"), prompt=TRUE, verbose = 1)association.plot(siamcat, fn.plot=NULL, color.scheme = "RdYlBu", sort.by = "fc", max.show = 50, plot.type = "quantile.box", panels = c("fc", "auroc"), prompt=TRUE, verbose = 1)
siamcat |
object of class siamcat-class |
fn.plot |
string, filename for the pdf-plot. If |
color.scheme |
valid R color scheme or vector of valid R colors (must
be of the same length as the number of classes), defaults to |
sort.by |
string, sort features by p-value ( |
max.show |
integer, how many associated features should be shown,
defaults to |
plot.type |
string, specify how the abundance should be plotted, must
be one of these: |
panels |
vector, name of the panels to be plotted next to the
abundances, possible entries are |
prompt |
boolean, turn on/off prompting user input when not plotting into a pdf-file, defaults to TRUE |
verbose |
integer, control output: |
This function visualizes the results of the computations carried
out in the check.associations function. It produces a plot of the
top max.show associated features at a user-specified significance
level alpha.
For binary classification problems, the plot will show the distribution of the log10-transformed abundances for both classes, a P-value from the significance test, and user-selected panels for the effect size (AU-ROC, prevalence shift, or generalized fold change). For regression problems, the plot will show the Spearman correlation, the significance, and the linear model effect size.
Does not return anything, but instead produces association plot
# Example data data(siamcat_example) # Simple example association.plot(siamcat_example, fn.plot = "./assoc_plot.pdf") # Plot associations as box plot association.plot(siamcat_example, fn.plot = "./assoc_plot_box.pdf", plot.type = "box") # Additionally, sort by p-value instead of by fold change association.plot(siamcat_example, fn.plot = "./assoc_plot_fc.pdf", plot.type = "box", sort.by = "p.val") # Custom colors association.plot(siamcat_example, fn.plot = "./assoc_plot_blue_yellow.pdf", plot.type = "box", color.scheme = c("cornflowerblue", "#ffc125"))# Example data data(siamcat_example) # Simple example association.plot(siamcat_example, fn.plot = "./assoc_plot.pdf") # Plot associations as box plot association.plot(siamcat_example, fn.plot = "./assoc_plot_box.pdf", plot.type = "box") # Additionally, sort by p-value instead of by fold change association.plot(siamcat_example, fn.plot = "./assoc_plot_fc.pdf", plot.type = "box", sort.by = "p.val") # Custom colors association.plot(siamcat_example, fn.plot = "./assoc_plot_blue_yellow.pdf", plot.type = "box", color.scheme = c("cornflowerblue", "#ffc125"))
Function to retrieve the results of association testing
associations(siamcat, verbose=1) ## S4 method for signature 'siamcat' associations(siamcat, verbose = 1)associations(siamcat, verbose=1) ## S4 method for signature 'siamcat' associations(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class containing the results of association testing |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function returns the results of the association testing procedure as dataframe. See check.associations for more details.
A data.frame of association testing results or NULL
data(siamcat_example) temp <- associations(siamcat_example) head(temp)data(siamcat_example) temp <- associations(siamcat_example) head(temp)
This function computes different measures of association
between features and the label and stores the results in the
association slot of the SIAMCAT object
check.associations(siamcat, formula="feat~label", test='wilcoxon', alpha=0.05, mult.corr="fdr", log.n0=1e-06, pr.cutoff=1e-06, probs.fc=seq(.1, .9, .05), paired=NULL, feature.type='filtered', verbose = 1)check.associations(siamcat, formula="feat~label", test='wilcoxon', alpha=0.05, mult.corr="fdr", log.n0=1e-06, pr.cutoff=1e-06, probs.fc=seq(.1, .9, .05), paired=NULL, feature.type='filtered', verbose = 1)
siamcat |
object of class siamcat-class |
formula |
string, formula used for testing, see Details for more
information, defaults to |
test |
string, statistical test used for the association testing, can
be either |
alpha |
float, significance level, defaults to |
mult.corr |
string, multiple hypothesis correction method, see
|
log.n0 |
float, pseudo-count to be added before log-transformation of
the data, defaults to |
pr.cutoff |
float, cutoff for the prevalence computation, defaults to
|
probs.fc |
numeric vector, quantiles used to calculate the generalized
fold change between groups, see Details for more information,
defaults to |
paired |
character, column name of the meta-variable containing
information for a paired test, defaults to |
feature.type |
string, on which type of features should the function
work? Can be either If |
verbose |
integer, control output: |
object of class siamcat-class with the slot
associations filled
The function uses the Wilcoxon test as default statistical test for binary classification problems. Alternatively, a simple linear model (as implemented in lm) can be used as well. For regression problems, the function defaults to the linear model.
The function calculates several measures for the effect size of the assocations between microbial features and the label. For binary classification problems, these associations are:
AUROC (area under the Receiver Operating Characteristics curve) as a non-parametric measure of enrichment,
the generalized fold change (gFC), a pseudo-fold change which is calculated as geometric mean of the differences between quantiles across both groups,
prevalence shift (difference in prevalence between the two groups).
For regression problems, the effect sizes are:
Spearman correlation between the feature and the label.
To correct for possible confounders while testing for association, the
function uses linear mixed effect models as implemented in the
lmerTest package. To do so, the test formula needs to be adjusted
to include the confounder. For example, when correcting for the metadata
information Sex, the formula would be:
'feat~label+(1|Sex)' (see also the example below).
Please note that modifying the formula parameter in this function might lead to unexpected results!
For paired testing, e.g. when the same patient has been sampled before and after an intervention, the 'paired' parameter can be supplied to the function. This indicated a column in the metadata table that holds the information about pairing.
# Example data data(siamcat_example) # Simple example siamcat_example <- check.associations(siamcat_example) # Confounder-corrected testing (corrected for Sex) # # this is not run during checks # siamcat_example <- check.associations(siamcat_example, # formula='feat~label+(1|Sex)', test='lm') # Paired testing # # this is not run during checks # siamcat_paired <- check.associations(siamcat_paired, # paired='Individual_ID')# Example data data(siamcat_example) # Simple example siamcat_example <- check.associations(siamcat_example) # Confounder-corrected testing (corrected for Sex) # # this is not run during checks # siamcat_example <- check.associations(siamcat_example, # formula='feat~label+(1|Sex)', test='lm') # Paired testing # # this is not run during checks # siamcat_paired <- check.associations(siamcat_paired, # paired='Individual_ID')
Checks potential confounders in the metadata and visualize the results
check.confounders(siamcat, fn.plot, meta.in = NULL, feature.type='filtered', verbose = 1)check.confounders(siamcat, fn.plot, meta.in = NULL, feature.type='filtered', verbose = 1)
siamcat |
an object of class siamcat-class |
fn.plot |
string, filename for the pdf-plot |
meta.in |
vector, specific metadata variable names to analyze, defaults to NULL (all metadata variables will be analyzed) |
feature.type |
string, on which type of features should the function
work? Can be either |
verbose |
integer, control output: |
This function checks for associations between class labels and potential confounders (e.g. Age, Sex, or BMI) that are present in the metadata. Statistical testing is performed with Fisher's exact test or Wilcoxon test, while associations are visualized either as barplot or Q-Q plot, depending on the type of metadata.
Additionally, it evaluates associations among metadata variables using conditional entropy and associations with the label using generalized linear models, producing a correlation heatmap and appropriate quantitative barplots, respectively.
Please note that the confounder check is currently only available for binary classification problems!
Does not return anything, but outputs plots to specified pdf file
# Example data data(siamcat_example) # Simple working example check.confounders(siamcat_example, './conf_plot.pdf')# Example data data(siamcat_example) # Simple working example check.confounders(siamcat_example, './conf_plot.pdf')
This function prepares the cross-validation by splitting the
data into num.folds training and test folds for
num.resample times.
create.data.split(siamcat, num.folds = 2, num.resample = 1, stratify = TRUE, inseparable = NULL, verbose = 1)create.data.split(siamcat, num.folds = 2, num.resample = 1, stratify = TRUE, inseparable = NULL, verbose = 1)
siamcat |
object of class siamcat-class |
num.folds |
integer number of cross-validation folds (needs to be
|
num.resample |
integer, resampling rounds (values |
stratify |
boolean, should the splits be stratified so that an equal
proportion of classes are present in each fold?, will be ignored for
regression tasks, defaults to |
inseparable |
string, name of metadata variable to be inseparable,
defaults to |
verbose |
integer, control output: |
This function splits the labels within a siamcat-class object and prepares the internal cross-validation for the model training (see train.model).
The function saves the training and test instances for the different
cross-validation folds within a list in the data_split-slot of the
siamcat-class object, which is a list with four entries:
num.folds - the number of cross-validation folds
num.resample - the number of repetitions for the
cross-validation
training.folds - a list containing the indices for the
training instances
test.folds - a list containing the indices for the
test instances
If provided, the data split will take into account a metadata variable
for the data split (by providing the inseparable argument). For
example, if the data contains several samples for the same individual,
it makes sense to keep data from the same individual within the
same fold.
If inseparable is given, the stratify argument will be
ignored.
object of class siamcat-class with the data_split-slot
filled
data(siamcat_example) # simple working example siamcat_split <- create.data.split(siamcat_example, num.folds=10, num.resample=5, stratify=TRUE)data(siamcat_example) # simple working example siamcat_split <- create.data.split(siamcat_example, num.folds=10, num.resample=5, stratify=TRUE)
This function creates a label object from metadata or an atomic vector
create.label(label, case, meta=NULL, control=NULL, p.lab = NULL, n.lab = NULL, remove.meta.column=FALSE, verbose=1)create.label(label, case, meta=NULL, control=NULL, p.lab = NULL, n.lab = NULL, remove.meta.column=FALSE, verbose=1)
label |
named vector to create the label or the name of the metadata column that will be used to create the label |
case |
name of the group that will be used as a positive label. If the variable is binary, the other label will be used as a negative one. If the variable has multiple values, all the other values will be used a negative label (testing one vs rest). |
meta |
metadata dataframe object or an object of class sample_data-class |
control |
name of a label or vector with names that will be used as a negative label. All values that are nor equal to case and control will be dropped. Default to NULL in which case: If the variable is binary, the value not equal to case will be used as negative. If the variable has multiple values, all the values not equal to cases will be used a negative label (testing one vs rest). |
p.lab |
name of the positive group (useful mostly for visualizations). Default to NULL in which case the value of the positive group will be used. |
n.lab |
name of the negative group (useful mostly for visualizations). Default to NULL in which case the value of the negative group will be used for binary variables and "rest" will be used for variables with multiple values. |
remove.meta.column |
boolean indicating if the label column in the
metadata should be retained. Please note that if this is set to
|
verbose |
integer, control output: |
The function creates a list to be used as label in a SIAMCAT
object. Mainly for interal use, but it can be used to customize your label
(p.lab and n.lab will be used as labels during plotting,
for example).
The input for the function can be either a named vector encoding the label or the name of a column in the metadata (needs to be provided as well) which contains the label information.
return either
a list to be used in a SIMCAT object OR
a list with entries meta and label,
if remove.meta.column is set to TRUE
data('meta_crc_zeller') label <- create.label(label='Group', case='CRC', meta=meta.crc.zeller)data('meta_crc_zeller') label <- create.label(label='Group', case='CRC', meta=meta.crc.zeller)
Function to retrieve the data split stored in the
data_split slot within a SIAMCAT object
data_split(siamcat, verbose=1) ## S4 method for signature 'siamcat' data_split(siamcat, verbose = 1)data_split(siamcat, verbose=1) ## S4 method for signature 'siamcat' data_split(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class containing a data split |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function returns a list containing information about the data split. See create.data.split for more details.
A list containing the data split information or NULL
data(siamcat_example) temp <- data_split(siamcat_example) names(temp)data(siamcat_example) temp <- data_split(siamcat_example) names(temp)
Function to retrieve the evaluation metrics from a SIAMCAT object
eval_data(siamcat, verbose=1) ## S4 method for signature 'siamcat' eval_data(siamcat, verbose = 1)eval_data(siamcat, verbose=1) ## S4 method for signature 'siamcat' eval_data(siamcat, verbose = 1)
siamcat |
(Required). A siamcat-class object that contains evaluation data |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The functions returns a list containing the evaluation metrics from a SIAMCAT object. See evaluate.predictions for more information on evaluation data.
The list of evaluation data or NULL
data(siamcat_example) temp <- eval_data(siamcat_example) names(temp) temp$aurocdata(siamcat_example) temp <- eval_data(siamcat_example) names(temp) temp$auroc
This function compares the predictions (from [make.predictions]) and true labels for all samples and evaluates the results.
evaluate.predictions(siamcat, verbose = 1)evaluate.predictions(siamcat, verbose = 1)
siamcat |
object of class siamcat-class |
verbose |
integer, control output: |
object of class siamcat-class with the slot
eval_data filled
This function calculates several metrics for the predictions in
the pred_matrix-slot of the siamcat-class-object.
The Area Under the Receiver Operating Characteristic (ROC) Curve (AU-ROC)
and the Precision-Recall Curve will be evaluated and the results will be
saved in the eval_data-slot of the supplied siamcat-class-
object. The eval_data-slot contains a list with several entries:
$roc - average ROC-curve across repeats or a single ROC-curve
on complete dataset (see roc);
$auroc - AUC value for the average ROC-curve;
$prc - list containing the positive predictive value
(precision) and true positive rate (recall) values used to plot the mean
PR curve;
$auprc - AUC value for the mean PR curve;
$ev - list containing for different decision thresholds the
number of false positives, false negatives, true negatives, and true
positives.
For the case of repeated cross-validation, the function will additionally return
$roc.all - list of roc objects (see roc)
for every repeat;
$auroc.all - vector of AUC values for the ROC curves
for every repeat;
$prc.all - list of PR curves for every repeat;
$auprc.all - vector of AUC values for the PR curves
for every repeat;
$ev.all - list of ev lists (see above)
for every repeat.
This function calculates several metrics for the evaluation of predictions
and will store the results in the eval_data-slot of the supplied
siamcat-class objects. The eval_data-slot will contain:
r2 - the mean R squared value across repeats or a single
R-squared value on the complete dataset;
mae - them mean absolute error of the predictions;
mse - the mean squared error of the predictions.
For the case of repeated cross-validation, the function will additionally
compute all three of these measures for the individual cross-validation
repeats and will store the results in the eval_data slot as
r2.all, mae.all, and mse.all.
data(siamcat_example) siamcat_evaluated <- evaluate.predictions(siamcat_example)data(siamcat_example) siamcat_evaluated <- evaluate.predictions(siamcat_example)
Feature matrix (as data.frame) of the CRC dataset from Zeller et al. MSB 2014 (see http://msb.embopress.org/content/10/11/766), containing 141 samples and 1754 bacterial species (features).
http://msb.embopress.org/content/10/11/766
Function to retrieve information on which type of features the models were trained
feature_type(siamcat, verbose=1) ## S4 method for signature 'siamcat' feature_type(siamcat, verbose = 1)feature_type(siamcat, verbose=1) ## S4 method for signature 'siamcat' feature_type(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class that contains trained models |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function extracts the information on which type of features the models were trained.
The string describing type of feature used for the model training
or NULL
data(siamcat_example) feature_type(siamcat_example)data(siamcat_example) feature_type(siamcat_example)
Function to extract the feature weights from a SIAMCAT object
feature_weights(siamcat, verbose=1) ## S4 method for signature 'siamcat' feature_weights(siamcat, verbose = 1)feature_weights(siamcat, verbose=1) ## S4 method for signature 'siamcat' feature_weights(siamcat, verbose = 1)
siamcat |
(Required). A siamcat-class object that contains trained models |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function extracts the weight matrix from all trained models (see weight_matrix) and computes several metrics on the feature weights:
mean.weight - mean weight across trained models
median.weight - median weight across trained models
sd.weight - standard deviation of the weight across trained
models
mean.rel.weight - mean relative weight across
trained models (each model is normalized by the absolute of all weights)
median.rel.weight - median relative weight across
trained models
sd.rel.weight - standard deviation of the relative
weight across trained models
percentage - percentage of models in which this feature was
selected (i.e. non-zero)
A dataframe containing mean/median feature weight and
additional info or NULL
data(siamcat_example) temp <- feature_weights(siamcat_example) head(temp)data(siamcat_example) temp <- feature_weights(siamcat_example) head(temp)
Function to retrieve the list of parameters for feature filtering
filt_params(siamcat, verbose=1) ## S4 method for signature 'siamcat' filt_params(siamcat, verbose = 1)filt_params(siamcat, verbose=1) ## S4 method for signature 'siamcat' filt_params(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class containing filtered features |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function returns the list of feature filtering parameters. See filter.features for more details.
A list of feature filtering parameters or NULL
data(siamcat_example) temp <- filt_params(siamcat_example) names(temp)data(siamcat_example) temp <- filt_params(siamcat_example) names(temp)
This function performs unsupervised feature filtering.
filter.features(siamcat, filter.method = "abundance", cutoff = 0.001, rm.unmapped = TRUE, feature.type='original', verbose = 1)filter.features(siamcat, filter.method = "abundance", cutoff = 0.001, rm.unmapped = TRUE, feature.type='original', verbose = 1)
siamcat |
an object of class siamcat-class |
filter.method |
string, method used for filtering the features, can be
one of these: |
cutoff |
float, abundace, prevalence, or variance cutoff, defaults
to |
rm.unmapped |
boolean, should unmapped reads be discarded?, defaults to
|
feature.type |
string, on which type of features should the function
work? Can be either |
verbose |
integer, control output: |
This function filters the features in a siamcat-class object in a unsupervised manner.
The different filter methods work in the following way:
'abundace' - remove features whose maximum abundance is
never above the threshold value in any of the samples
'cum.abundance' - remove features with very low abundance
in all samples, i.e. those that are never among the most abundant
entities that collectively make up (1-cutoff) of the reads in
any sample
'prevalence' - remove features with low prevalence across
samples, i.e. those that are undetected (relative abundance of 0)
in more than 1 - cutoff percent of samples.
'variance' - remove features with low variance across
samples, i.e. those that have a variance lower than cutoff
'pass' - pass-through filtering will not change the
features
Features can also be filtered repeatedly with different methods, e.g. first using the maximum abundance filtering and then using prevalence filtering. However, if a filtering method has already been applied to the dataset, SIAMCAT will default back on the original features for filtering.
siamcat an object of class siamcat-class
# Example dataset data(siamcat_example) # Simple examples siamcat_filtered <- filter.features(siamcat_example, filter.method='abundance', cutoff=1e-03) # 5% prevalence filtering siamcat_filtered <- filter.features(siamcat_example, filter.method='prevalence', cutoff=0.05) # filter first for abundance and then for prevalence siamcat_filt <- filter.features(siamcat_example, filter.method='abundance', cutoff=1e-03) siamcat_filt <- filter.features(siamcat_filt, filter.method='prevalence', cutoff=0.05, feature.type='filtered')# Example dataset data(siamcat_example) # Simple examples siamcat_filtered <- filter.features(siamcat_example, filter.method='abundance', cutoff=1e-03) # 5% prevalence filtering siamcat_filtered <- filter.features(siamcat_example, filter.method='prevalence', cutoff=0.05) # filter first for abundance and then for prevalence siamcat_filt <- filter.features(siamcat_example, filter.method='abundance', cutoff=1e-03) siamcat_filt <- filter.features(siamcat_filt, filter.method='prevalence', cutoff=0.05, feature.type='filtered')
This functions filters the label in a SIAMCAT object
filter.label(siamcat, ids, verbose = 1)filter.label(siamcat, ids, verbose = 1)
siamcat |
an object of class siamcat-class |
ids |
vector, can contain either names or indices of samples to be retained |
verbose |
integer, control output: |
This function filters the label contained in a SIAMCAT object,
based on the provided ids. The IDs can be either sample names or
indices to be retained.
Predominantly for internal use...
Please note: It makes sense to run validate.data after filtering the label.
siamcat an object of class siamcat-class
data(siamcat_example) # simple working example siamcat_filtered <- filter.label(siamcat_example, ids=c(1:20))data(siamcat_example) # simple working example siamcat_filtered <- filter.label(siamcat_example, ids=c(1:20))
Function to retrieve the filtered features from a SIAMCAT object
get.filt_feat.matrix(siamcat)get.filt_feat.matrix(siamcat)
siamcat |
(Required). An instance of siamcat-class containing filtered features |
The function returns the filtered features as matrix. See filter.features for more details.
A matrix containing the filtered features
data(siamcat_example) feat.filt <- get.filt_feat.matrix(siamcat_example) feat.filt[1:3, 1:3]data(siamcat_example) feat.filt <- get.filt_feat.matrix(siamcat_example) feat.filt[1:3, 1:3]
Function to retrieve the normalized features from a SIAMCAT object
get.norm_feat.matrix(siamcat)get.norm_feat.matrix(siamcat)
siamcat |
(Required). An instance of siamcat-class containing normalized features |
The function returns the normalized features as matrix. See normalize.features for more details.
A matrix containing the normalized features
data(siamcat_example) feat.norm <- get.norm_feat.matrix(siamcat_example) feat.norm[1:3, 1:3]data(siamcat_example) feat.norm <- get.norm_feat.matrix(siamcat_example) feat.norm[1:3, 1:3]
Function to retrieve the original features from a SIAMCAT object
get.orig_feat.matrix(siamcat)get.orig_feat.matrix(siamcat)
siamcat |
(Required). An instance of siamcat-class |
The function returns the original features as matrix.
A matrix containing the original features
data(siamcat_example) feat.original <- get.orig_feat.matrix(siamcat_example) feat.original[1:3, 1:3]data(siamcat_example) feat.original <- get.orig_feat.matrix(siamcat_example) feat.original[1:3, 1:3]
Retrieve the label from a SIAMCAT object
label(siamcat, verbose=1) ## S4 method for signature 'siamcat' label(siamcat, verbose = 1)label(siamcat, verbose=1) ## S4 method for signature 'siamcat' label(siamcat, verbose = 1)
siamcat |
(Required). A siamcat-class object |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
This function will retrieve the label information from a SIAMCAT object. The label will contain three entries:
label: The label as named vector, in which the classes are
encoded numerically
info: Information about the different classes
type: What kind of label is it?
The label or NULL.
data(siamcat_example) temp <- label(siamcat_example) head(temp$label) temp$info temp$typedata(siamcat_example) temp <- label(siamcat_example) head(temp$label) temp$info temp$type
This function takes a siamcat-class-object containing a model trained by train.model and performs predictions on a given test-set.
make.predictions(siamcat, siamcat.holdout = NULL, normalize.holdout = TRUE, verbose = 1)make.predictions(siamcat, siamcat.holdout = NULL, normalize.holdout = TRUE, verbose = 1)
siamcat |
object of class siamcat-class |
siamcat.holdout |
optional, object of class siamcat-class on
which to make predictions, defaults to |
normalize.holdout |
boolean, should the holdout features be normalized
with a frozen normalization (see normalize.features) using the
normalization parameters in |
verbose |
integer, control output: |
This functions uses the model in the model_list-slot of the
siamcat object to make predictions on a given test set. The test set
can either consist of the test instances in the cross-validation, saved in
the data_split-slot of the same siamcat object, or a
completely external feature set, given in the form of another
siamcat object (siamcat.holdout).
object of class siamcat-class with the slot
pred_matrix filled
data(siamcat_example) # Simple example siamcat_example <- train.model(siamcat_example, method='lasso') siamcat.pred <- make.predictions(siamcat_example) # Predictions on a holdout-set (not run) # pred.mat <- make.predictions(siamcat.trained, siamcat.holdout, # normalize.holdout=TRUE)data(siamcat_example) # Simple example siamcat_example <- train.model(siamcat_example, method='lasso') siamcat.pred <- make.predictions(siamcat_example) # Predictions on a holdout-set (not run) # pred.mat <- make.predictions(siamcat.trained, siamcat.holdout, # normalize.holdout=TRUE)
Retrieve the metadata from a SIAMCAT object
meta(siamcat) ## S4 method for signature 'siamcat' meta(siamcat) ## S4 method for signature 'sample_data' meta(siamcat)meta(siamcat) ## S4 method for signature 'siamcat' meta(siamcat) ## S4 method for signature 'sample_data' meta(siamcat)
siamcat |
(Required). A siamcat-class object |
This function will retrieve the metadata from a SIAMCAT object. The metadata is a object of the sample_data-class.
The metadata as sample_data-class object
data(siamcat_example) temp <- meta(siamcat_example) head(temp)data(siamcat_example) temp <- meta(siamcat_example) head(temp)
Metadata (as data.frame) of the CRC dataset from Zeller et al. MSB 2014 (see http://msb.embopress.org/content/10/11/766), containing 6 metadata variables variables (e.g. Age or BMI) for 141 samples.
http://msb.embopress.org/content/10/11/766
Function to retrieve information on which type of machine learning method was used for model training
model_type(siamcat, verbose=1) ## S4 method for signature 'siamcat' model_type(siamcat, verbose = 1)model_type(siamcat, verbose=1) ## S4 method for signature 'siamcat' model_type(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class that contains trained models |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function extracts the information on which type of machine learning method was used for model training.
The string describing the machine learning method or NULL
data(siamcat_example) model_type(siamcat_example)data(siamcat_example) model_type(siamcat_example)
Produces plots for model evaluation.
model.evaluation.plot(..., fn.plot = NULL, colours=NULL, show.all=FALSE, verbose = 1)model.evaluation.plot(..., fn.plot = NULL, colours=NULL, show.all=FALSE, verbose = 1)
... |
one or more object of class siamcat-class, can be named |
fn.plot |
string, filename for the pdf-plot |
colours |
colour specification for the different siamcat-class-
objects, defaults to |
show.all |
boolean, Should the results from repeated cross-validation
models be plotted? Defaults to |
verbose |
control output: |
Does not return anything, but produces the model evaluation plot.
The first plot shows the Receiver Operating Characteristic (ROC)-curve,
the other plot the Precision-recall (PR)-curve for the model. If
show.all == FALSE (which is the default), a single line representing
the mean across cross-validation repeats will be plotted, otherwise the
individual cross-validation repeats will be included as
lightly shaded lines.
For regression problems, this function will produce a scatter plot between the real and predicted values. If several siamcat-class-objects are supplied, a single plot for each object will be produced.
data(siamcat_example) # simple working example model.evaluation.plot(siamcat_example, fn.plot='./eval.pdf') # plot several named SIAMCAT object # although we use only one example object here model.evaluation.plot('Example_1'=siamcat_example, 'Example_2'=siamcat_example, colours=c('red', 'blue'), fn.plot='./eval.pdf') # show indiviudal cross-validation repeats model.evaluation.plot(siamcat_example, fn.plot='./eval.pdf', show.all=TRUE)data(siamcat_example) # simple working example model.evaluation.plot(siamcat_example, fn.plot='./eval.pdf') # plot several named SIAMCAT object # although we use only one example object here model.evaluation.plot('Example_1'=siamcat_example, 'Example_2'=siamcat_example, colours=c('red', 'blue'), fn.plot='./eval.pdf') # show indiviudal cross-validation repeats model.evaluation.plot(siamcat_example, fn.plot='./eval.pdf', show.all=TRUE)
This function produces a plot for model interpretation
model.interpretation.plot(siamcat, fn.plot = NULL, color.scheme = "BrBG", consens.thres = 0.5, heatmap.type = "zscore", limits = c(-3, 3), log.n0 = 1e-06, max.show = 50, prompt=TRUE, verbose = 1)model.interpretation.plot(siamcat, fn.plot = NULL, color.scheme = "BrBG", consens.thres = 0.5, heatmap.type = "zscore", limits = c(-3, 3), log.n0 = 1e-06, max.show = 50, prompt=TRUE, verbose = 1)
siamcat |
object of class siamcat-class |
fn.plot |
string, filename for the pdf-plot |
color.scheme |
color scheme for the heatmap, defaults to |
consens.thres |
float, minimal ratio of models incorporating a feature
in order to include it into the heatmap, defaults to |
heatmap.type |
string, type of the heatmap, can be either |
limits |
vector, cutoff for extreme values in the heatmap,
defaults to |
log.n0 |
float, pseudocount to be added before log-transformation
of features, defaults to |
max.show |
integer, maximum number of features to be shown in the model interpretation plot, defaults to 50 |
prompt |
boolean, turn on/off prompting user input when not plotting into a pdf-file, defaults to TRUE |
verbose |
control output: |
Produces a plot consisting of
a barplot showing the feature weights and their robustness (i.e. in what proportion of models have they been incorporated)
a heatmap showing the z-scores of the metagenomic features across samples
another heatmap displaying the metadata categories (if applicable)
a boxplot displaying the poportion of weight per model that is
actually shown for the features that are incorporated into more than
consens.thres percent of the models.
Does not return anything, but produces the model interpretation plot.
data(siamcat_example) # simple working example siamcat_example <- train.model(siamcat_example, method='lasso') model.interpretation.plot(siamcat_example, fn.plot='./interpretion.pdf', heatmap.type='zscore')data(siamcat_example) # simple working example siamcat_example <- train.model(siamcat_example, method='lasso') model.interpretation.plot(siamcat_example, fn.plot='./interpretion.pdf', heatmap.type='zscore')
Function to retrieve the list of trained models
models(siamcat, verbose=1) ## S4 method for signature 'siamcat' models(siamcat, verbose = 1)models(siamcat, verbose=1) ## S4 method for signature 'siamcat' models(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class that contains trained models |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function extracts the list of trained models.
The list of models or NULL
data(siamcat_example) temp <- models(siamcat_example) temp[[1]]data(siamcat_example) temp <- models(siamcat_example) temp[[1]]
Function to retrieve the list of parameters for feature normalization
norm_params(siamcat, verbose=1) ## S4 method for signature 'siamcat' norm_params(siamcat, verbose = 1)norm_params(siamcat, verbose=1) ## S4 method for signature 'siamcat' norm_params(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class containing normalized features |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function returns the list of normalization parameters used in the feature normalization procedure. See normalize.features for more details.
A list of normalization parameters or NULL
data(siamcat_example) temp <- norm_params(siamcat_example) names(temp)data(siamcat_example) temp <- norm_params(siamcat_example) names(temp)
This function performs feature normalization according to user-specified parameters.
normalize.features(siamcat, norm.method = c("rank.unit", "rank.std", "log.std", "log.unit", "log.clr", "std", "pass"), norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1, n.p = 2, norm.margin = 1), feature.type='filtered', verbose = 1)normalize.features(siamcat, norm.method = c("rank.unit", "rank.std", "log.std", "log.unit", "log.clr", "std", "pass"), norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1, n.p = 2, norm.margin = 1), feature.type='filtered', verbose = 1)
siamcat |
an object of class siamcat-class |
norm.method |
string, normalization method, can be one of these:
|
norm.param |
list, specifying the parameters of the different normalization methods, see Details for more information |
feature.type |
string, on which type of features should the function
work? Can be either |
verbose |
integer, control output: |
an object of class siamcat-class with normalized features
There are seven different normalization methods available, which
might need additional parameters, which are passed via the norm.param
list:
'rank.unit' - converts features to ranks and normalizes
each column (=sample) by the square root of the sum of ranks
This method does not require additional parameters.
'rank.std' - converts features to ranks and applies z-score
standardization.
This method requires sd.min.q (minimum quantile of the standard
deviation to be added to all features in order to avoid underestimation of
standard deviation) as additional parameter.
'log.clr' - centered log-ratio transformation.
This methods requires a pseudocount (log.n0) before
log-transformation.
'log.std' - log-transforms features and applies z-score
standardization.
This method requires both a pseudocount (log.n0) and sd.min.q
'log.unit' - log-transforms features and normalizes by
features or samples with different norms.
This method requires a pseudocount (log.n0) and then additionally the
parameters norm.maring (margin over which to normalize, similarly to
the apply-syntax: Allowed values are 1 for normalization
over features, 2 over samples, and 3 for normalization
by the global maximum) and the parameter n.p (vector norm to be
used, can be either 1 for x/sum(x) or 2 for
x/sqrt(sum(x^2))).
'std' - z-score standardization without any other
transformation
This method only requires the sd.min.q parameter
'pass' - pass-through normalization will not change
the features
The function additionally allows to perform a frozen normalization on a
different dataset. After normalizing the first dataset, the norm_feat
slot in the SIAMCAT object contains all parameters of the normalization,
which you can access via the norm_params accessor.
In order to perform a frozen normalization of a new dataset, you can run the
function supplying the normalization parameters as argument to
norm.param:
norm.param=norm_params(siamcat_reference). See also the example below.
# Example data data(siamcat_example) # Simple example siamcat_norm <- normalize.features(siamcat_example, norm.method='rank.unit') # log.unit example siamcat_norm <- normalize.features(siamcat_example, norm.method='log.unit', norm.param=list(log.n0=1e-05, n.p=1, norm.margin=1)) # log.std example siamcat_norm <- normalize.features(siamcat_example, norm.method='log.std', norm.param=list(log.n0=1e-05, sd.min.q=.1)) # Frozen normalization # normalize the object siamcat with the same parameters as used in # siamcat_reference # # this is not run # siamcat_norm <- normalize.features(siamcat, # norm.param=norm_params(siamcat_reference))# Example data data(siamcat_example) # Simple example siamcat_norm <- normalize.features(siamcat_example, norm.method='rank.unit') # log.unit example siamcat_norm <- normalize.features(siamcat_example, norm.method='log.unit', norm.param=list(log.n0=1e-05, n.p=1, norm.margin=1)) # log.std example siamcat_norm <- normalize.features(siamcat_example, norm.method='log.std', norm.param=list(log.n0=1e-05, sd.min.q=.1)) # Frozen normalization # normalize the object siamcat with the same parameters as used in # siamcat_reference # # this is not run # siamcat_norm <- normalize.features(siamcat, # norm.param=norm_params(siamcat_reference))
Function to retrieve the prediction matrix from a SIAMCAT object
pred_matrix(siamcat, verbose=1) ## S4 method for signature 'siamcat' pred_matrix(siamcat, verbose = 1)pred_matrix(siamcat, verbose=1) ## S4 method for signature 'siamcat' pred_matrix(siamcat, verbose = 1)
siamcat |
(Required). A siamcat-class object that contains a prediction matrix |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The functions returns a matrix containing the predictions for all samples across the different cross-validation repeats. See make.predictions for more information.
A matrix containing predictions or NULL
data(siamcat_example) temp <- pred_matrix(siamcat_example) head(temp)data(siamcat_example) temp <- pred_matrix(siamcat_example) head(temp)
Read label information from a file
read.label(fn.in.label)read.label(fn.in.label)
fn.in.label |
name of the tsv file containing labels |
This function reads in a tsv file with labels and converts it into a label.
First row is expected to be
#BINARY:1=[label for cases];
-1=[label for controls].
Second row should contain the sample identifiers as tab-separated list (consistent with feature and metadata).
Third row is expected to contain the actual class labels (tab-separated):
1 for each case and -1 for each control.
Note: Labels can take other numeric values (but not characters or strings); importantly, the label for cases has to be greater than the one for controls
label object containing several entries:
$label named vector containing the numerical labels from the
file;
$info information about the classes in the label;
$type information about the label type (e.g. BINARY);
# run with example data fn.label <- system.file('extdata', 'label_crc_zeller_msb_mocat_specI.tsv', package = 'SIAMCAT') crc.zeller.label <- read.label(fn.label)# run with example data fn.label <- system.file('extdata', 'label_crc_zeller_msb_mocat_specI.tsv', package = 'SIAMCAT') crc.zeller.label <- read.label(fn.label)
This function select samples based on information given in the metadata
select.samples(siamcat, filter, allowed.set = NULL, allowed.range = NULL, verbose = 1)select.samples(siamcat, filter, allowed.set = NULL, allowed.range = NULL, verbose = 1)
siamcat |
an object of class siamcat-class |
filter |
string, name of the meta variable on which the selection should be done |
allowed.set |
a vector of allowed values |
allowed.range |
a range of allowed values |
verbose |
integer, control output: |
This functions selects labels and metadata based on a specific column in the metadata. Provided with a column-name in the metadata and a range or a set of allowed values, the function will filter the siamcat-class object accordingly.
an object of class siamcat-class with labels and metadata filtered in order to contain only allowed values
data(siamcat_example) # Select all samples that fall into an Age-range between 25 and 80 years siamcat_selected <- select.samples(siamcat_example, filter='Age', allowed.range=c(25, 80)) # Select only female samples siamcat_female <- select.samples(siamcat_example, filter='Gender', allowed.set=c('F'))data(siamcat_example) # Select all samples that fall into an Age-range between 25 and 80 years siamcat_selected <- select.samples(siamcat_example, filter='Age', allowed.range=c(25, 80)) # Select only female samples siamcat_female <- select.samples(siamcat_example, filter='Gender', allowed.set=c('F'))
Function to construct an object of class siamcat-class
siamcat(..., feat=NULL, label=NULL, meta=NULL, phyloseq=NULL, validate=TRUE, verbose=3)siamcat(..., feat=NULL, label=NULL, meta=NULL, phyloseq=NULL, validate=TRUE, verbose=3)
... |
additional arguments |
feat |
feature information for SIAMCAT (see details) |
label |
label information for SIAMCAT (see details) |
meta |
(optional) metadata information for SIAMCAT (see details) |
phyloseq |
(optional) a phyloseq object for the creation of an SIAMCAT object (see details) |
validate |
boolean, should the newly constructed SIAMCAT object be validated? defaults to TRUE (we strongly recommend against setting this parameter to FALSE) |
verbose |
control output: |
Build siamcat-class objects from their components.
This functions creates a SIAMCAT object (see siamcat-class). In order to do so, the function needs
feat the feature information for SIAMCAT, should be either a
matrix, a data.frame, or a otu_table-class. The columns
should correspond to the different samples (e.g. patients) and the rows the
different features (e.g. taxa). Columns and rows should be named.
meta metadata information for the different samples in the
feature matrix. Metadata is optional for the SIAMCAT workflow. Should be
either a data.frame (with the rownames corresponding to the sample
names of the feature matrix) or an object of class
sample_data-class
phyloseq Alternatively to supplying both feat and meta,
SIAMCAT can also work with a phyloseq object containing an otu_table and
other optional slots (like sample_data for meta-variables).
Notice: do supply either the feature information as matrix/data.frame/otu_table (and optionally metadata) or a phyloseq object, but not both.
The label information for SIAMCAT can take several forms:
metadata column: if there is metadata (either via meta or as
sample_data in the phyloseq object), the label object can be created
by taking the information in a specific metadata column. In order to
do so, label should be the name of the column, and case
should indicate which group(s) should be the positive group(s). A
typical example could look like that:
siamcat <- siamcat(feat=feat.matrix, meta=metadata,
label='DiseaseState', case='CRC')
for the construction of a label to predict CRC status (which is encoded
in the column "DiseaseState" of the metadata). For more control
(e.g. specific labels for plotting or specific control state), the
label can also be created outside of the siamcat function using
the create.label function.
named vector: the label can also be supplied as named vector which
encodes the label either as characters (e.g. "Healthy" and "Diseased"),
as factor, or numerically (e.g. -1 and 1). The vector must be named
with the names of samples (corresponding to the samples in features).
Also here, the information about the positive group(s) is needed via
the case parameter. Internally, the vector is given to the
create.label function.
label object: A label object can be created with the create.label function or by reading a dedicated label file with read.label.
A new siamcat-class object
# example with package data data("feat_crc_zeller", package="SIAMCAT") data("meta_crc_zeller", package="SIAMCAT") siamcat <- siamcat(feat=feat.crc.zeller, meta=meta.crc.zeller, label='Group', case='CRC')# example with package data data("feat_crc_zeller", package="SIAMCAT") data("meta_crc_zeller", package="SIAMCAT") siamcat <- siamcat(feat=feat.crc.zeller, meta=meta.crc.zeller, label='Group', case='CRC')
Reduced version of the CRC dataset from Zeller et al. MSB 2014 (see http://msb.embopress.org/content/10/11/766), containing 100 features (15 associated features at 5% FDR in the original dataset and 85 random other features) and 141 samples, saved after the complete SIAMCAT pipelinehas been run.
Thus, the example dataset contains entries in every slot of the SIAMCAT
object (see siamcat-class), e.g, eval_data or
data_split.
Mainly used for running the examples in the function documentation.
http://msb.embopress.org/content/10/11/766
The SIAMCAT class
The S4 SIAMCAT class stores the results from the SIAMCAT workflow in different slots. The different slots will be filled by different functions (referenced in the description below).
In order to contruct a SIAMCAT class object, please refer to the documentation of the construction function siamcat.
The SIAMCAT class is based on the phyloseq-class. Therefore,
you can easily import a phyloseq object into SIAMCAT.
phyloseqobject of class phyloseq-class
labellist containing the label information for the samples and some metadata about the label, created by create.label or when creating the siamcat-class object by calling siamcat
filt_featlist containing the filtered features as matrix and the list of filtering parameters, created by calling the filter.features function
associationslist containing the parameters for association testing and the results of association testing with these parameters in a dataframe, created by calling the check.associations function
norm_featlist containing the normalized features as matrix and the list of normalziation parameters (for frozen normalization), created by calling the normalize.features function
data_splitlist containing cross-validation instances, created by calling the create.data.split function
model_listlist containing the trained models, the type of model that was trained, and on which kind of features it was trained, created by calling the train.model function
pred_matrixmatrix of predictions, created by calling the make.predictions function
eval_datalist containing different evaluation metrics, created by calling the evaluate.predictions function
This function trains the a machine learning model on the training data
train.model(siamcat, method = "lasso", measure = "classif.acc", param.set = NULL, grid.size=11, min.nonzero=5, perform.fs = FALSE, param.fs = list(no_features = 100, method = "AUC", direction="absolute"), feature.type='normalized', verbose = 1)train.model(siamcat, method = "lasso", measure = "classif.acc", param.set = NULL, grid.size=11, min.nonzero=5, perform.fs = FALSE, param.fs = list(no_features = 100, method = "AUC", direction="absolute"), feature.type='normalized', verbose = 1)
siamcat |
object of class siamcat-class |
method |
string, specifies the type of model to be trained, may be one
of these: |
measure |
character, specifies the model selection criterion during
internal cross-validation, see mlr_measures for more details,
defaults to |
param.set |
list, set of extra parameters for mlr, see below for
details, defaults to |
grid.size |
integer, grid size for internal tuning (needed for some
machine learning methods, for example |
min.nonzero |
integer number of minimum nonzero coefficients that
should be present in the model (only for |
perform.fs |
boolean, should feature selection be performed? Defaults
to |
param.fs |
list, parameters for the feature selection, see Details,
defaults to |
feature.type |
string, on which type of features should the function
work? Can be either |
verbose |
integer, control output: |
object of class siamcat-class with added model_list
This functions performs the training of the machine learning model
and functions as an interface to the mlr3-package.
The function expects a siamcat-class-object with a prepared
cross-validation (see create.data.split) in the
data_split-slot of the object. It then trains a model for each fold
of the data split.
The different machine learning methods are implemented as Learners from the mlr3learners package:
'lasso', 'enet', and 'ridge' use the
'classif.cv_glmnet' or 'regr.cv_glmnet' Learners, which
interface to the glmnet package,
'lasso_ll' and 'ridge_ll' use a custom Learner, which
is only available for classification tasks. The underlying package is the
LiblineaR packge.
'randomForest' is implemented via the 'classif.ranger'
or regr.ranger Learners available trough the ranger
package.
There is additional control over the machine learning procedure by
supplying information through the param.set parameter within the
function. We encourage you to check out the excellent
mlr documentation
for more in-depth information.
Here is a short overview which parameters you can supply in which form:
enet The alpha parameter describes the mixture between
lasso and ridge penalty and is -per default- determined using internal
cross-validation (the default would be equivalent to
param.set=list('alpha'=c(0,1))). You can supply either the limits of
the hyperparameter exploration (e.g. with limits 0.2 and 0.8:
param.set=list('alpha'=c(0.2,0.8))) or you can supply a fixed alpha
value as well (param.set=list('alpha'=0.5)).
lasso_ll/ridge_ll You can supply both class.weights and
the cost parameter (cost of the constraints violation, see
LiblineaR for more info). The default values would be
equal to param.set=list('class.weights'=c(5, 1),
'cost'=c(-2, 3)).
randomForest You can supply the two parameters num.trees
(Number of trees to grow) and mtry (Number of variables randomly
sampled as candidates at each split). See also
ranger for more info. The default values
correspond to
param.set=list('num.trees'=c(100, 1000), 'mtry'=
c(round(sqrt.mdim / 2), round(sqrt.mdim), round(sqrt.mdim * 2))) with
sqrt.mdim=sqrt(nrow(data)).
If feature selection should be performed (for example for functional data
with a large number of features), the param.fs list should contain:
no_features - Number of features to be retained after
feature selection,
method - method for the feature selection, may be
AUC, gFC, or Wilcoxon for binary classification
problems or spearman, pearson, or MI (mutual
information) for regression problems
direction - indicates if the feature selection should be
performed in a single direction only. Can be either
absolute - select the top associated features (independent of
the sign of enrichment),
positivethe top positively associated featured (enriched in
the case group for binary classification or enriched in higher values
for regression),
negative the top negatively associated features (inverse of
positive)
Direction will be ignored for Wilcoxon and MI.
data(siamcat_example) # simple working example siamcat_example <- train.model(siamcat_example, method='lasso')data(siamcat_example) # simple working example siamcat_example <- train.model(siamcat_example, method='lasso')
This function checks if labels are available for all samples in features. Additionally validates metadata, if available.
validate.data(siamcat, verbose = 1)validate.data(siamcat, verbose = 1)
siamcat |
an object of class siamcat-class |
verbose |
integer, control output: |
This function validates the data by checking that labels are available for all samples in the feature matrix. Furthermore, the number of samples per class is checked to ensure a minimum number. If metadata is available, the overlap between labels and metadata is checked as well.
This function is run when a siamcat-class object is created.
an object of class siamcat-class
data(siamcat_example) # validate.data should be run before completing the pipeline # since the complete pipeline had been run on siamcat_example, we # construct a new siamcat object for the example feat <- orig_feat(siamcat_example) label <- label(siamcat_example) siamcat <- siamcat(feat=feat, label=label, validate=FALSE) siamcat <- validate.data(siamcat, verbose=2)data(siamcat_example) # validate.data should be run before completing the pipeline # since the complete pipeline had been run on siamcat_example, we # construct a new siamcat object for the example feat <- orig_feat(siamcat_example) label <- label(siamcat_example) siamcat <- siamcat(feat=feat, label=label, validate=FALSE) siamcat <- validate.data(siamcat, verbose=2)
This function creates a volcano plot to vizualize the association between features and the label
volcano.plot(siamcat, fn.plot=NULL, color.scheme="RdYlBu", annotate=5)volcano.plot(siamcat, fn.plot=NULL, color.scheme="RdYlBu", annotate=5)
siamcat |
object of class siamcat-class |
fn.plot |
string, filename for the pdf-plot. If |
color.scheme |
valid R color scheme or vector of valid R colors (must
be of the same length as the number of classes), defaults to |
annotate |
integer, number of features to annotate with the name |
bla bla bla
Does not return anything, but produces a volcano plot based on association measures
# Example data data(siamcat_example) # Simple example volcano.plot(siamcat_example, fn.plot='./volcano.pdf')# Example data data(siamcat_example) # Simple example volcano.plot(siamcat_example, fn.plot='./volcano.pdf')
Function to retrieve the feature weights from a SIAMCAT object
weight_matrix(siamcat, verbose=1) ## S4 method for signature 'siamcat' weight_matrix(siamcat, verbose = 1)weight_matrix(siamcat, verbose=1) ## S4 method for signature 'siamcat' weight_matrix(siamcat, verbose = 1)
siamcat |
(Required). An instance of siamcat-class that contains trained models |
verbose |
integer, if the slot is empty, should a message be printed?
values can be either |
The function extracts the feature weights from all trained models acorss all cross-validation folds and repeats.
A matrix containing the feature weights or NULL
data(siamcat_example) temp <- weight_matrix(siamcat_example) temp[1:3, 1:3]data(siamcat_example) temp <- weight_matrix(siamcat_example) temp[1:3, 1:3]