Package 'CimpleG'

Title: A Method to Identify Single CpG Sites for Classification and Deconvolution
Description: DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. 'CimpleG' is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. 'CimpleG' is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG' based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, 'CimpleG' provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) <doi:10.1186/s13059-023-03000-0>.
Authors: Tiago F.V. Maié [aut, cre]
Maintainer: Tiago F.V. Maié <[email protected]>
License: GPL (>= 3)
Version: 1.0.1
Built: 2026-06-03 09:02:13 UTC
Source: https://github.com/costalab/cimpleg

Help Index

Find simple CpG (CimpleG) signatures.

Description

Train a classification model using (CpGs) as features for the given target data.

Usage

CimpleG(
  train_data,
  train_targets = NULL,
  target_columns = NULL,
  test_data = NULL,
  test_targets = NULL,
  method = c("CimpleG", "CimpleG_parab", "brute_force", "logistic_reg", "decision_tree",
    "boost_tree", "mlp", "rand_forest"),
  pred_type = c("both", "hypo", "hyper"),
  engine = c("glmnet", "xgboost", "nnet", "ranger"),
  rank_method = c("ac_rank", "a_rank", "c_rank"),
  k_folds = 10,
  grid_n = 10,
  param_p = 2,
  n_sigs = 1,
  quantile_threshold = 0.005,
  train_only = FALSE,
  split_data = FALSE,
  run_parallel = FALSE,
  deconvolution_reference = TRUE,
  has_annotation = FALSE,
  save_dir = NULL,
  save_format = c("lz4", "gzip", "bzip2", "xz", "nocomp"),
  verbose = 1,
  targets = NULL
)

cimpleg(
  train_data,
  train_targets = NULL,
  target_columns = NULL,
  test_data = NULL,
  test_targets = NULL,
  method = c("CimpleG", "CimpleG_parab", "brute_force", "logistic_reg", "decision_tree",
    "boost_tree", "mlp", "rand_forest"),
  pred_type = c("both", "hypo", "hyper"),
  engine = c("glmnet", "xgboost", "nnet", "ranger"),
  rank_method = c("ac_rank", "a_rank", "c_rank"),
  k_folds = 10,
  grid_n = 10,
  param_p = 2,
  n_sigs = 1,
  quantile_threshold = 0.005,
  train_only = FALSE,
  split_data = FALSE,
  run_parallel = FALSE,
  deconvolution_reference = TRUE,
  has_annotation = FALSE,
  save_dir = NULL,
  save_format = c("lz4", "gzip", "bzip2", "xz", "nocomp"),
  verbose = 1,
  targets = NULL
)

cpg(
  train_data,
  train_targets = NULL,
  target_columns = NULL,
  test_data = NULL,
  test_targets = NULL,
  method = c("CimpleG", "CimpleG_parab", "brute_force", "logistic_reg", "decision_tree",
    "boost_tree", "mlp", "rand_forest"),
  pred_type = c("both", "hypo", "hyper"),
  engine = c("glmnet", "xgboost", "nnet", "ranger"),
  rank_method = c("ac_rank", "a_rank", "c_rank"),
  k_folds = 10,
  grid_n = 10,
  param_p = 2,
  n_sigs = 1,
  quantile_threshold = 0.005,
  train_only = FALSE,
  split_data = FALSE,
  run_parallel = FALSE,
  deconvolution_reference = TRUE,
  has_annotation = FALSE,
  save_dir = NULL,
  save_format = c("lz4", "gzip", "bzip2", "xz", "nocomp"),
  verbose = 1,
  targets = NULL
)

Arguments

train_data

Training dataset. A matrix (s x f) with methylation data (Beta values) that will be used to train/find the predictors. Samples (s) must be in rows while features/CpGs (f) must be in columns.
train_targets

A data frame with the training target samples one-hot encoded. A data frame with at least 1 column, with as many rows and in the same order as 'train_data'. Target columns need to be one-hot encoded, meaning that, for that column the target samples should be encoded as '1' while every other sample should be encoded as '0'.
target_columns

A string specifying the name of the column in 'train_targets' to be used for training. Can be a character vector if there are several columns in 'train_targets' to be used for training. If this argument is a character vector, CimpleG will search for the best predictors for each target sequentially or in parallel depending on the value of 'run_parallel'
test_data

Testing dataset. A matrix (s x f) with methylation data (Beta values) that will be used to test the performance of the found predictors. Samples (s) must be in rows while features/CpGs (f) must be in columns. If 'test_data' *OR* 'test_targets' are NULL, CimpleG will generate a stratified test dataset based on 'train_targets' by removing 25 samples from 'train_data' and 'train_targets'.
test_targets

A data frame with the testing target samples one-hot encoded. A data frame with at least 1 column, with as many rows and in the same order as 'test_data'. Target columns need to be one-hot encoded, meaning that, for that column the target samples should be encoded as '1' while every other sample should be encoded as '0'. If 'test_data' *OR* 'test_targets' are NULL, CimpleG will generate a stratified test dataset based on 'train_targets' by removing 25 samples from 'train_data' and 'train_targets'.
method

A string specifying the method or type of machine learning model/algorithm to be used for training. These are divided in two main groups. * The simple models (classifiers that use a single feature), 'CimpleG' (default), 'brute_force', 'CimpleG_unscaled' or 'oner'; * the complex models (classifiers that use several features), 'logistic_reg', 'decision_tree', 'boost_tree', 'mlp' or 'rand_forest'.
pred_type

A string specifying the type of predictor/CpG to be searched for during training. Only used for simple models. One of 'both' (default), 'hypo' or 'hyper'. If 'hypo', only hypomethylated predictors will be considered. If 'hyper', only hypermethylated predictors will be considered.
engine

A string specifying the machine learning engine behind 'method'. Only used for complex models. Currently not in use.
rank_method

A string specifying the ranking strategy to rank the features during training.
k_folds

An integer specifying the number of folds (K) to be used in training for the stratified K-fold cross-validation procedure.
grid_n

An integer specifying the number of hyperparameter combinations to train for.
param_p

An even number in 'sigma / (delta^param_p)'. Tunes how much weight will be given to delta when doing feature selection. Default is 2.
n_sigs

Number of signatures to be saved for classification and used in deconvolution. Default is 1.
quantile_threshold

A number between 0 and 1. Determines how many features will be kept. Default is 0.005.
train_only

A boolean, if TRUE, CimpleG will only train (find predictors) but not test them against a test dataset.
split_data

A boolean, if 'TRUE', it will subset the train data provided, creating a smaller test set that will be used to test the models after training. This parameter is experimental. Default is 'FALSE'.
run_parallel

A boolean, if 'FALSE', the default, it will search for predictors for multiple targets sequentially. If 'TRUE' it will search for predictors for multiple targets at the same time (parallel processing) in order to save in computational time. You need to set up 'future::plan()' before running this function.
deconvolution_reference

A boolean, if 'TRUE', it will create a deconvolution reference matrix based on the training data. This can later be used to perform deconvolution. Default is 'FALSE'.
has_annotation

A boolean, if 'TRUE', it will get the CpG annotation from Illumina for the generated signature. Default is 'FALSE'.
save_dir

If defined it will save the resulting model to the given directory. Default is NULL.
save_format

Only used if save_dir is not NULL. One of "lz4", "gzip", "bzip2","xz", "nocomp". lz4 is the best option, fast compression and loading times, low space usage.
verbose

How verbose you want CimpleG to be while it is running. At 0, no message is displayed, at 3 every message is displayed. Default is 1.
targets

DEPRECATED use 'target_columns'.

Value

A CimpleG object with the results per target class.

Examples

library("CimpleG")

# read data
data(train_data)
data(train_targets)
data(test_data)
data(test_targets)

# run CimpleG
cimpleg_result <- CimpleG(
  train_data = train_data,
  train_targets = train_targets,
  test_data = test_data,
  test_targets = test_targets,
  method = "CimpleG",
  target_columns = c("glia","neurons")
)

# check signatures
cimpleg_result$signatures

Feature selection function used in the sigma delta space

Description

Feature selection function used in the sigma delta space

Usage

compute_ax(dm, sv, p)

Arguments

dm

delta (difference in mean values)
sv

sigma (sum of variance values)
p

even number, the greater 'p' is the more importance will be given to sigma

Value

numeric value, score used for feature selection

Compute diff mean sum var dataframe

Description

Compute diff mean sum var dataframe

Usage

compute_diffmeans_sumvar(data, target_vector)

Arguments

data

Matrix with beta values that will be used to compute diffmeans sumvar data frame
target_vector

boolean vector defining which samples in data are part of the target class

Value

data.frame with computed difference in means and sum of variances for target comparison (target v others)

Helper function to darken down a given color.

Description

Helper function to darken down a given color.

Usage

darken(color, factor = 0.5)

Arguments

color

Color name or hex code of a color
factor

Multiplicative factor by which 'color' will be darkened down

Value

a character value, hex color code of the darkened color provided

Scatter plots of observed (true) vs predicted values for deconvolution.

Description

Produces one plot panel per number of methods with predictions. Each plot panel has one plot per cell type.

Usage

deconv_pred_obs_plot(
  deconv_df,
  true_values_col,
  predicted_cols,
  sample_id_col,
  group_col,
  axis_lims = list(x = c(0, 1), y = c(0, 1))
)

Arguments

deconv_df

A data.frame with meta.data, true values and predictions for different methods as columns. Each row should be a prediction for a given sample and a given group/cell type.
true_values_col

A string with the name of the column with the true values in 'deconv_df'.
predicted_cols

A vector of strings with the name of the columns with the predictions for different methods in 'deconv_df'.
sample_id_col

A string with the name of the column with the sample name or ID in 'deconv_df'.
group_col

A string with the name of the column containing the cell types or groups in 'deconv_df'.
axis_lims

A list with two entries, 'x' and 'y', defining the limits of the x and y axis of the plot.

Value

list of ggplot2 objects

Boxplot and rankings of deconvolution metrics for deconvolution results.

Description

Produces data with varied deconvolution performance metrics. Produces one boxplot and one ranking plot with the for each metric.

Usage

deconv_ranking_plot(
  deconv_df,
  true_values_col,
  predicted_cols,
  sample_id_col,
  group_col,
  metrics = c("rmse", "r_squared", "adj.r.squared", "AIC"),
  custom_colours = NULL
)

Arguments

deconv_df

A data.frame with meta.data, true values and predictions for different methods as columns. Each row should be a prediction for a given sample and a given group/cell type.
true_values_col

A string with the name of the column with the true values in 'deconv_df'.
predicted_cols

A vector of strings with the name of the columns with the predictions for different methods in 'deconv_df'.
sample_id_col

A string with the name of the column with the sample name or ID in 'deconv_df'.
group_col

A string with the name of the column containing the cell types or groups in 'deconv_df'.
metrics

A list with two entries, 'x' and 'y', defining the limits of the x and y axis of the plot.
custom_colours

A named vector with colours, where the names are the values defined in 'predicted_cols'. If 'NULL', default colours will be used.

Value

list object with data and deconvolution performance plots

Stacked barplot of deconvolution results

Description

Stacked barplot of deconvolution results

Usage

deconvolution_barplot(
  deconvoluted_data,
  meta_data,
  sample_id_column,
  true_label_column,
  color_dict = NULL,
  show_x_label = FALSE,
  base_size = 14,
  ...
)

Arguments

deconvoluted_data

Result from running 'run_deconvolution'
meta_data

Data.frame containing metadata from deconvoluted samples
sample_id_column

Name of the column containing the sample id in the meta_data data.frame
true_label_column

Name of the column containing the true labels of the samples in the meta_data data.frame
color_dict

Named string featuring colors as values and labels (true labels) as names
show_x_label

A boolean, if 'TRUE' the sample labels in the X axis will be shown. Default is 'FALSE'.
base_size

An integer defining the base size of the text in the plot. Default is '14'.
...

Parameters passed to the ggplot2::theme function.

Value

A list with the data and the ggplot2 plot object.

EpiDISH deconvolution

Description

EpiDISH deconvolution

Usage

deconvolution_epidish(
  ref_mat,
  new_data,
  epidish_method = "CBS",
  epidish_nuv = seq(0.1, 1, 0.1),
  epidish_maxit = 10000
)

Arguments

ref_mat

Reference matrix.
new_data

New data matrix.
epidish_method

One of 'CBS' (Cibersort), 'RPC' (Robust Partial Correlations), 'CP' (Constrained Projection). Default is 'CBS'. See 'EpiDISH' documentation for more information.
epidish_nuv

A vector of candidate values used for svm. Only used when epidish_method is set to 'CBS'. See 'EpiDISH' documentation for more information.
epidish_maxit

Integer with the number of max iterations for IWLS (Iterative Weighted Least Squares). Only used when epidish_method is set to 'RPC'.

NMF deconvolution

Description

NMF deconvolution

Usage

deconvolution_nmf(weights_mat, values_mat, ...)

Arguments

weights_mat

Reference matrix.
values_mat

New data matrix.
...

Extra parameters to be set NMF options. Most relevant parameters are probably 'method' and 'beta'.

NNLS deconvolution

Description

NNLS deconvolution

Usage

deconvolution_nnls(dt, compute_cols, ref_mat)

Arguments

dt

A data.table with the new data with features/predictions on rows and samples on columns.
compute_cols

A character vector with the columns for which the deconvolution algorithm should be ran.
ref_mat

The reference matrix as created by CimpleG.

Creates the old version of the difference in means by sum of variances plot

Description

Represent CpGs in the difference in means, sum of variances space. This plot is often used to select CpGs that would be good classifiers. These CpGs are often located on the bottom left and bottom right of this plot.

Usage

diffmeans_sumvariance_plot(
  data,
  xcol = "diff_means",
  ycol = "sum_variance",
  feature_id_col = "id",
  is_feature_selected_col = NULL,
  label_var1 = "Target",
  label_var2 = "Others",
  target_vector = NULL,
  mean_cutoff = NULL,
  var_cutoff = NULL,
  threshold_func = NULL,
  func_factor = NULL,
  feats_to_highlight = NULL,
  cpg_ranking_df = NULL,
  color_all_points = NULL,
  plot_density = TRUE,
  density_type = c("density", "histogram", "boxplot", "violin", "densigram"),
  plot_dir = NULL,
  id_tag = NULL,
  file_tag = NULL,
  custom_mods = FALSE
)

Arguments

data

Data to create difference in means, sum of variances plot. Either a data.frame with 'xcol','ycol' and 'feature_id_col' or, if 'target_vector' is not 'NULL' a matrix with beta values from which, given the target, the difference in means between the target and others, and the sum of variances within the target and others will be calculated.
xcol

Column with x-axis data
ycol

Column with y-axis data
feature_id_col

Column with the feature ID
is_feature_selected_col

NULL or column with TRUE/FALSE for features which should be highlighted as selected
label_var1

Label of the target class
label_var2

Label of the other classes
target_vector

if not NULL a vector target class assignment, see data
mean_cutoff

a numeric draw mean cutoff at given position
var_cutoff

a numeric draw variance cutoff at given position
threshold_func

specification of the parabola function, see examples
func_factor

argument to be passed to the parabola function, see examples
feats_to_highlight

features (CpGs) to be highlighted in the plot
cpg_ranking_df

data.frame with ranked features (CpGs) to be highlighted in the plot, if present must have the following columns: .id, predType, Rank and DiffAndFoldScaledAUPR
color_all_points

color that all non-highlighted points should have, argument defaults to NULL, the default color is black
plot_density

A boolean, if TRUE (default) the function will produce density plots on top/side of scatterplot
density_type

One of "density", "histogram", "boxplot", "violin" or "densigram". Defines the type of density plot if 'plot_density = TRUE'
plot_dir

path to directory where to save the plot. If NULL (default), plot will not be saved.
id_tag

character string to identify plots, is displayed in the plot and present in the file name
file_tag

character string to identify plots, tags only the file name
custom_mods

a boolean, if TRUE will add some custom labels to the plot. Default is FALSE

Value

a ggplot2 object with the dmsv plot.

Examples

library("CimpleG")

# read data
data(train_data)
data(train_targets)

# make basic plot
plt <- diffmeans_sumvariance_plot(
  train_data,
  target_vector = train_targets$blood_cells == 1
)
print(plt)

# make plot with parabola, colored and highlighted features
df_dmeansvar <- compute_diffmeans_sumvar(
  train_data,
  target_vector = train_targets$blood_cells==1
)
parab_param <- .7
df_dmeansvar$is_selected <- select_features(
    x = df_dmeansvar$diff_means,
    y = df_dmeansvar$sum_variance,
    a = parab_param
)

plt <- diffmeans_sumvariance_plot(
  data=df_dmeansvar,
  label_var1="Leukocytes",
  color_all_points="red",
  is_feature_selected_col="is_selected",
  feats_to_highlight=c("cg10456121"),
  threshold_func=function(x,a) (a*x)^2,
  func_factor=parab_param
)
print(plt)

Creates the old version of the difference in means by sum of variances plot

Description

Represent CpGs in the difference in means, sum of variances space. This plot is often used to select CpGs that would be good classifiers. These CpGs are often located on the bottom left and bottom right of this plot.

Usage

dmsv_plot(
  dat,
  target_vector = NULL,
  x_var = "diff_means",
  y_var = "sum_variance",
  id_var = "id",
  highlight_var = NULL,
  display_var = NULL,
  label_var1 = "Target",
  label_var2 = "Others",
  point_color = "black",
  subtitle = NULL
)

Arguments

dat

Data to create dmsv plot (difference in means, sum of variances plot). Either a data.frame with 'x_var','y_var' and 'id_var' or, if 'target_vector' is not 'NULL' a matrix with beta values from which, given the target, the difference in means between the target and others, and the sum of variances within the target and others will be calculated.
target_vector

if not NULL a boolean vector with target class assignment, see data
x_var

Name of the column with x-axis data (difference of means).
y_var

Name of the column with y-axis data (sum of variances).
id_var

Name of the column with the feature/CpG ID.
highlight_var

(Optional) Name of the column with the highlighted features. Values in this column should be boolean (TRUE for selected, FALSE for not selected).
display_var

(Optional) Name of the column with the features that should be displayed in the plot as a label. Values in this column should be boolean (TRUE for feature that should be displayed, FALSE for feature that should not be displayed).
label_var1

Label of the target class. Default is "Target".
label_var2

Label of the other classes. Default is "Others".
point_color

Color of the features/CpGs in the plot. Default is "black". If features are highlighted, non-highlighted features will have a lighter color.
subtitle

Subtitle to be displayed in the plot. Default is NULL.

Value

a ggplot2 object with the dmsv plot.

Examples

library("CimpleG")

# load CimpleG example data
data(train_data)
data(train_targets)

# make basic plot straight from the data
plt <- dmsv_plot(
  dat = train_data,
  target_vector = train_targets$blood_cells == 1
)
print(plt)

# make plot with highlighted features
# first create a diffmeans sumvar data frame from the data
df_dmeansvar <- compute_diffmeans_sumvar(
  train_data,
  target_vector = train_targets$blood_cells==1
)
# adding a column to this data frame \code{hl_col} with random CpGs
# selected (as TRUE) or not (as FALSE) to be highlighted and displayed.
df_dmeansvar$hl_col <- sample(c(TRUE,FALSE),nrow(df_dmeansvar),replace=TRUE,prob=c(0.1,0.9))
df_dmeansvar$dp_col <- df_dmeansvar$hl_col

plt <- dmsv_plot(
  dat=df_dmeansvar,
  highlight_var="hl_col",
  display_var="dp_col",
  label_var1="Leukocytes",
  point_color="red",
  subtitle="method: CimpleG"
)
print(plt)

Evaluation of produced models on test data

Description

Evaluation of produced models on test data

Usage

eval_test_data(test_data, final_model, method = "oner", verbose = 1)

Arguments

test_data

Test data.
final_model

Model to be tested.
method

Method used to train model.
verbose

How verbose the logs should be.

Value

a data.frame with the evaluation statistics

Get CpG annotation from Illumina

Description

Get CpG annotation from Illumina

Usage

get_cpg_annotation(
  cpg_id,
  is_epic = TRUE,
  short_annotation = TRUE,
  silence_warnings = TRUE
)

Arguments

cpg_id

A character vector with the CpG IDs from Illumina to annotate.
is_epic

A boolean, if TRUE, the annotation will be fetched from the EPIC array, otherwise from the 450k array. Default is TRUE.
short_annotation

A boolean, if TRUE, only a small number of columns from the full annotation reference will be kept. This leads to an easier to read output. Default is TRUE.
silence_warnings

A boolean, if TRUE, warnings produced during the downloading and loading of the data will be silenced. Default is TRUE.

Value

A table with the annotated CpGs in the same order as the provided signatures.

Examples

library("CimpleG")

# read data
signatures <- c("cg14501977", "cg24548498")

# Get signature annotation
signature_annotation <- get_cpg_annotation(signatures)

# check signature annotation
signature_annotation

Helper function to lighten up a given color.

Description

Helper function to lighten up a given color.

Usage

lighten(color, factor = 0.5)

Arguments

color

Color name or hex code of a color
factor

Multiplicative factor by which 'color' will be lightened up

Value

a character value, hex color code of the lightened color provided

Load an R object saved with CimpleG or an RDS file.

Description

Load an R object saved with CimpleG or an RDS file.

Usage

load_object(file_name)

Arguments

file_name

File name in the working directory or path to file to be loaded. Files saved with CimpleG::save_object and base::saveRDS files are supported.

Value

the loaded R object

Make color palette data frame

Description

Make color palette data frame

Usage

make_color_palette(classes)

Arguments

classes

Vector with classes for which to create a color palette

Value

data.frane with colors defined for each class provided

Make tidy data for use in deconvolution plots

Description

Produces data with varied deconvolution performance metrics.

Usage

make_deconv_pred_obs_data(
  dat,
  true_values_col,
  predicted_cols,
  sample_id_col,
  group_col
)

Arguments

dat

data.frame with predictions as columns, each row should be a prediction for a given sample and given group/celltype
true_values_col

A string with the name of the column with the true values in 'dat'. true values should be between 0 and 1.
predicted_cols

A vector of strings with the name of the columns with the predictions for different methods in 'dat'. predictions should be between 0 and 1
sample_id_col

A string with the name of the column with the sample name or ID in 'dat'.
group_col

A string with the name of the column containing the cell types or groups in 'dat'. group col should be a factor, otherwise the function will make it a factor

Value

tibble with tidied up deconvolution performance data in nested fields

Build deconvolution reference matrix

Description

Build deconvolution reference matrix

Usage

make_deconv_ref_matrix(cpg_obj, ref_data, ref_data_labels, method = NULL)

Arguments

cpg_obj

A CimpleG object.
ref_data

A matrix with the reference data to be used to build the reference matrix.
ref_data_labels

A character vector with the true labels of the samples in the 'reference_data'.
method

Method used to train models in the CimpleG object. If not provided (NULL), method will be taken from the CimpleG object. Creates the old version of the difference in means by sum of variances plot

Value

A list object containing the deconvolution reference matrix

Predict outcome from a CimpleG signatures on new data

Description

Predict outcome from a CimpleG signatures on new data

Usage

## S3 method for class 'CimpleG'
predict(object, ..., new_data, class_labels = NULL)

Arguments

object

CimpleG object.
...

Not used at the moment.
new_data

Data to be predicted, samples should be in rows and features in columns. Last column of 'new_data' should have the target/class labels coded as 0 or 1.
class_labels

Class labels of new data if these are not provided directly with it.

Value

prediction object, list with an entry for each signature

Perform deconvolution on a new set of samples, based on the CimpleG models trained

Description

Perform deconvolution on a new set of samples, based on the CimpleG models trained

Usage

run_deconvolution(
  cpg_obj = NULL,
  new_data = NULL,
  ref_mat = NULL,
  deconvolution_method = c("NNLS", "EpiDISH", "NMF"),
  ...
)

Arguments

cpg_obj

A CimpleG object. When creating/training CimpleG the parameter 'deconvolution_reference' should be set to 'TRUE'.
new_data

Matrix or data.frame that should have the samples you want to perform deconvolution on. Samples should be in rows and probes/CpGs in columns.
ref_mat

If the CimpleG object does not have the reference matrix, you can provide it here instead. See 'make_deconv_ref_matrix'
deconvolution_method

Deconvolution method to be used. One of #TODO
...

Extra parameters only used when deconvolution_method is set to 'NMF'. The most relevant parameter are probably 'method' and 'beta'.

Value

a data.table with the deconvolution results

Save an R object to disk with fast and efficient compression algorithms.

Description

Save an R object to disk with fast and efficient compression algorithms.

Usage

save_object(object, file_name, file_format = "lz4")

Arguments

object

Object to be saved to disk.
file_name

Name of the file where the R object is saved to.
file_format

One of "lz4", "gzip", "bzip2","xz", "nocomp". lz4 is the best option, fast compression and loading times, low space usage. Format "lz4" is only available if package archive is installed. Format "zstd" is not supported anymore as the library now needs to be precompiled with R.

Value

NULL invisibly

Feature selection function used in the diffmeans, sumvariance space

Description

Feature selection function used in the diffmeans, sumvariance space

Usage

select_features(x, y, a)

Arguments

x

difference in means value
y

sum of variances value
a

parabola parameter, scales how open/closed the parabola is, the higher the value, the more closed the parabola is.

Value

bool vector

CpG signature plot

Description

CpG signature plot

Usage

signature_plot(
  cpg_obj,
  data,
  meta_data,
  sample_id_column,
  true_label_column,
  color_dict = NULL,
  color_others = "black",
  as_panel = TRUE,
  is_beta = TRUE,
  base_size = 14,
  ...
)

Arguments

cpg_obj

A CimpleG object, as generated by the CimpleG function. Alternatively a names character vector or list with the signatures.
data

Matrix or data.frame that should have the samples and signatures to plot. Samples should be in rows and probes/CpGs in columns.
meta_data

Data.frame containing metadata from samples in 'data'.
sample_id_column

Name of the column containing the sample id in the meta_data data.frame
true_label_column

Name of the column containing the true labels of the samples in the meta_data data.frame
color_dict

Named string featuring colors as values and labels (true labels) as names
color_others

The name or hex code of a color by which the non-target samples should be colored by.
as_panel

A boolean, if TRUE (default) a single figure panel with all the signatures will be generated. Otherwise, the individual plots will be returned as a list.
is_beta

A boolean, if TRUE (default) the values will be plotted in a scale suitable for Beta values. Otherwise, the values will be plotted in scale suitable for M values.
base_size

An integer defining the base size of the text in the plot. Default is '14'.
...

Parameters passed to the ggplot2::theme function.

Value

A list with the data and the ggplot2 plot object.

Cell line test data

Description

Cell line test data

Usage

test_data

Format

A matrix with beta values for 1000 CpGs. Features/variables as columns and 170 samples as rows

Cell line test data targets

Description

Cell line test data targets

Usage

test_targets

Format

A data frame with 18 variables for 170 samples as rows.

gsm

GSM identifier (GEO accession number) of the sample

cell_type

the cell type of the respective sample

adipocytes

one-hot encoded (1 or 0) column defining if a given sample is an adipocyte

astrocytes

one-hot encoded (1 or 0) column defining if a given sample is an astrocyte

blood_cells

one-hot encoded (1 or 0) column defining if a given sample is a blood cell

endothelial_cells

one-hot encoded (1 or 0) column defining if a given sample is an endothelial cell

epidermal_cells

one-hot encoded (1 or 0) column defining if a given sample is an epidermal cell

epithelial_cells

one-hot encoded (1 or 0) column defining if a given sample is an epithelial cell

fibroblasts

one-hot encoded (1 or 0) column defining if a given sample is a fibroblast

glia

one-hot encoded (1 or 0) column defining if a given sample is a glia cell

hepatocytes

one-hot encoded (1 or 0) column defining if a given sample is an hepatocyte

ips_cells

one-hot encoded (1 or 0) column defining if a given sample is an ipsc

msc

one-hot encoded (1 or 0) column defining if a given sample is an msc

muscle_cells

one-hot encoded (1 or 0) column defining if a given sample is a muscle cell

neurons

one-hot encoded (1 or 0) column defining if a given sample is a neuron

muscle_sc

one-hot encoded (1 or 0) column defining if a given sample is a muscle stem cell

group_data

to which dataset these data belong to (train or test)

description

the cell type of the respective sample, in long form

Cell line train data

Description

Cell line train data

Usage

train_data

Format

A matrix with beta values for 1000 CpGs. Features/variables as columns and 409 samples as rows

Cell line train data targets

Description

Cell line train data targets

Usage

train_targets

Format

A data frame with 18 variables for 409 samples as rows.

gsm

GSM identifier (GEO accession number) of the sample

cell_type

the cell type of the respective sample

adipocytes

one-hot encoded (1 or 0) column defining if a given sample is an adipocyte

astrocytes

one-hot encoded (1 or 0) column defining if a given sample is an astrocyte

blood_cells

one-hot encoded (1 or 0) column defining if a given sample is a blood cell

endothelial_cells

one-hot encoded (1 or 0) column defining if a given sample is an endothelial cell

epidermal_cells

one-hot encoded (1 or 0) column defining if a given sample is an epidermal cell

epithelial_cells

one-hot encoded (1 or 0) column defining if a given sample is an epithelial cell

fibroblasts

one-hot encoded (1 or 0) column defining if a given sample is a fibroblast

glia

one-hot encoded (1 or 0) column defining if a given sample is a glia cell

hepatocytes

one-hot encoded (1 or 0) column defining if a given sample is an hepatocyte

ips_cells

one-hot encoded (1 or 0) column defining if a given sample is an ipsc

msc

one-hot encoded (1 or 0) column defining if a given sample is an msc

muscle_cells

one-hot encoded (1 or 0) column defining if a given sample is a muscle cell

neurons

one-hot encoded (1 or 0) column defining if a given sample is a neuron

muscle_sc

one-hot encoded (1 or 0) column defining if a given sample is a muscle stem cell

group_data

to which dataset these data belong to (train or test)

description

the cell type of the respective sample, in long form