Package 'GSSTDA' reference manual

Title:	Progression Analysis of Disease with Survival using Topological Data Analysis
Description:	Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.
Authors:	Miriam Esteve [aut, cre] , Raquel Bosch [aut], Jaume Forés [aut] , Joan Climent [aut], Antonio Falco [aut]
Maintainer:	Miriam Esteve <[email protected]>
License:	GPL-3
Version:	1.0.0
Built:	2025-01-28 04:10:20 UTC
Source:	https://github.com/miriamesteve/gsstda

Case-control vector

Description

Character vector of length 121 containing the group to which each sample belongs.

Usage

data(case_tag, package = "GSSTDA")
data(case_tag, package = "GSSTDA")

Format

Character vector length 121.

"NT": control, sample from healthy tissue; "T": case, sample from neoplastic tissue.

Source

The data are from the study GSE42568. Information extracted from the file GSE42568_family.soft.gz available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568.

check_filter_values

Description

Checking the filter_values introduces in the mapper object.

Usage

check_filter_values(full_data, filter_values, na.rm = TRUE)
check_filter_values(full_data, filter_values, na.rm = TRUE)

Arguments

`full_data`	Matrix with the columns of the input matrix corresponding to the individuals belonging to the level. This matrix could be the genes_disease_component.
`filter_values`	Vector obtained after applying the filtering function to the input matrix, i.e, a vector with the filtering function values for each included sample.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows.

Value

filter_value and full_data without NAN's

check_full_data

Description

Checking the full_data introduces in the package

Usage

check_full_data(full_data, na.rm = TRUE)
check_full_data(full_data, na.rm = TRUE)

Arguments

`full_data`	Matrix with the columns of the input matrix corresponding to the individuals belonging to the level.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows.

Value

Return full_data without NAN's and as a matrix

check_gene_selection

Description

Checking the arguments introduces in the gene selection process.

Usage

check_gene_selection(num_genes, gen_select_type, percent_gen_select)
check_gene_selection(num_genes, gen_select_type, percent_gen_select)

Arguments

`num_genes`	Number of genes in the full_data
`gen_select_type`	Type of gene selection to be used. Choose between "top_bot" (top-botton) and "abs" (absolute)
`percent_gen_select`	Percentage of genes to be selected

Value

num_gen_select Number of genes to be selected according to the percent_gen_select value

check_vectors

Description

Checking the survival_time, survival_event and case_tag introduces in the GSSTDA object.

Usage

check_vectors(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag,
  na.rm = TRUE
)
check_vectors(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag,
  na.rm = TRUE
)

Arguments

`full_data`	The genes of the full_data (maybe remove by na.rm = TRUE)
`survival_time`	Time between disease diagnosis and event (if there was no event until the end of follow-up).
`survival_event`	`logical`. Whether or not the event has occurred.
`case_tag`	The tag of the healthy sample (healthy or not).
`control_tag`	Tag of the healthy sample.E.g. "T"
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows.

Value

control_tag Return the tag of the healthy sample.

Get clusters for all data level

Description

It performs the clustering of the samples in each of the levels. That is to say, in each interval of values of the filtering function, the samples with a value within that interval are clustered using the proposed clustering algorithm and the proposed method to determine the optimal number of clusters.

Usage

clust_all_levels(
  data,
  samp_in_lev,
  distance_type,
  clustering_type,
  linkage_type,
  optimal_clustering_mode,
  silhouette_threshold,
  num_bins_when_clustering
)
clust_all_levels(
  data,
  samp_in_lev,
  distance_type,
  clustering_type,
  linkage_type,
  optimal_clustering_mode,
  silhouette_threshold,
  num_bins_when_clustering
)

Arguments

`data`	Input data matrix whose columns are the individuals and rows are the features.BR cambiar nombre.
`samp_in_lev`	A list of character vectors with the individuals included in each of the levels (i.e. each of the intervals of the values of the filter functions). It is the output of the `samples_in_levels` function.
`distance_type`	Type of distance to be used for clustering. Choose between correlation ("correlation") and euclidean ("euclidean").
`clustering_type`	Type of clustering method. Choose between "hierarchical" and "PAM" (“partition around medoids”) options.
`linkage_type`	Linkage criteria used in hierarchical clustering. Choose between "single" for single-linkage clustering, "complete" for complete-linkage clustering or "average" for average linkage clustering (or UPGMA). Only necessary for hierarchical clustering.
`optimal_clustering_mode`	Method for selection optimal number of clusters. It is only necessary if the chosen type of algorithm is hierarchical. In this case, choose between "standard" (the method used in the original mapper article) or "silhouette". In the case of the PAM algorithm, the method will always be "silhouette". "silhouette".
`silhouette_threshold`	Minimum value of $\overline{s}$ that a set of clusters must have to be chosen as optimal. Within each interval of the filter function, the average silhouette values $\overline{s}$ are computed for all possible partitions from $2$ to $n-1$, where $n$ is the number of samples within a specific interval. The $n$ that produces the highest value of $\overline{s}$ and that exceeds a specific threshold is selected as the optimum number of clusters. If no partition produces an $\overline{s}$ exceeding the chosen threshold, all samples are then assigned to a unique cluster. The default value is $0.25$. The threshold of $0.25$ for $\overline{s}$ has been chosen based on standard practice, recognizing it as a moderate value that reflects adequate separation and cohesion within clusters.
`num_bins_when_clustering`	Number of bins to generate the histogram employed by the standard optimal number of cluster finder method. Parameter not necessary if the "optimal_clust_mode" option is "silhouette" or the "clust_type" is "PAM".

Value

List of interger vectors. Each of the vectors contains information about the nodes at each level and the individuals contained in them. The names of the vector values are the names of the samples and the vector values are the node number of that level to which the individual belongs.

Get clusters for a particular data level

Description

It performs clustering of the samples belonging to a particular level (to a particular interval of the filter function) with the proposed clustering algorithm and the proposed method to determine the optimal number of clusters.

Usage

clust_lev(
  data_i,
  distance_type,
  clustering_type,
  linkage_type,
  optimal_clustering_mode,
  silhouette_threshold = 0.25,
  num_bins_when_clustering,
  level_name
)
clust_lev(
  data_i,
  distance_type,
  clustering_type,
  linkage_type,
  optimal_clustering_mode,
  silhouette_threshold = 0.25,
  num_bins_when_clustering,
  level_name
)

Arguments

`data_i`	Matrix with the columns of the input matrix corresponding to the individuals belonging to the level.
`distance_type`	Type of distance to be used for clustering. Choose between correlation ("correlation") and euclidean ("euclidean").
`clustering_type`	Type of clustering method. Choose between "hierarchical" and "PAM" (“partition around medoids”) options.
`linkage_type`	Linkage criteria used in hierarchical clustering. Choose between "single" for single-linkage clustering, "complete" for complete-linkage clustering or "average" for average linkage clustering (or UPGMA). Only necessary for hierarchical clustering. The value provided if the type of clustering chosen is hierarchical will be ignored
`optimal_clustering_mode`	Method for selection optimal number of clusters. It is only necessary if the chosen type of algorithm is hierarchical. In this case, choose between "standard" (the method used in the original mapper article) or "silhouette". In the case of the PAM algorithm, the method will always be "silhouette".
`silhouette_threshold`	Minimum value of $\overline{s}$ that a set of clusters must have to be chosen as optimal. Within each interval of the filter function, the average silhouette values $\overline{s}$ are computed for all possible partitions from $2$ to $n-1$, where $n$ is the number of samples within a specific interval. The $n$ that produces the highest value of $\overline{s}$ and that exceeds a specific threshold is selected as the optimum number of clusters. If no partition produces an $\overline{s}$ exceeding the chosen threshold, all samples are then assigned to a unique cluster. The default value is $0.25$. The threshold of $0.25$ for $\overline{s}$ has been chosen based on standard practice, recognizing it as a moderate value that reflects adequate separation and cohesion within clusters.
`num_bins_when_clustering`	Number of bins to generate the histogram employed by the standard optimal number of cluster finder method. Parameter not necessary if the "optimal_clust_mode" option is "silhouette" or the "clust_type" is "PAM".
`level_name`	Name of the studied level. # ERROR No usado

Value

Returns a interger vector with the samples included in each cluster for the specific level analyzed. The names of the vector values are the names of the samples and the vector values are the node number to which the individual belongs.

Computes the adjacency matrix.

Description

It computes the adjacency matrix between nodes. Two nodes are considered connected if they share at least one individual.

Usage

compute_node_adjacency(nodes_list)
compute_node_adjacency(nodes_list)

Arguments

nodes_list

Output of the levels_to_nodes function. List of character vectors. Each of the vectors contains the names of the individuals at each node.

Value

It returns a matrix of magnitude n nodes x n nodes that stores a 1 if there are shared samples in two given nodes and a 0 otherwise.

Survival analysis based on gene expression levels.

Description

It carries out univariate cox proportional hazard models for the expression levels of each gene included in the provided dataset (case_full_data) and their link with relapse-free or overall survival.

Usage

cox_all_genes(case_full_data, survival_time, survival_event)
cox_all_genes(case_full_data, survival_time, survival_event)

Arguments

`case_full_data`	Input matrix whose columns correspond to the patients and rows to the genes, having selected only the columns belonging to disease samples. The names of the rows must be the names of the genes.
`survival_time`	Numeric vector that includes time to the event information
`survival_event`	Numeric vector that indicates if relapse or death have been produced (0 and 1s).

Value

A matrix with the results of the application of proportional hazard models using the expression levels of each gene as covariate. The coef column corresponds to the regression coefficient; the exp_coef column corresponds to the value of e^coef (which is interpreted as the odds ratio); the se_coef column corresponds to the standard error of each coefficient; the Z column corresponds to the value of coef/se_coef (the higher the Z value, the higher the significance of the variable) and the Pr_z column corresponds to the p-value for each Z value.

Rectangular Matrix Denoiser.

Description

It takes a rectangular matrix composed by the addition of a signal matrix and a Gaussian noise matrix and returns a matrix of the same dimension that is denoised through a Singular Value Decomposition truncation process. The selection of the number of singular values is chosen following the proposal by "The optimal hard threshold for singular values is $\sqrt(4/ 3)$ ". It should be used after the function flatten_normal_tiss.

Usage

denoise_rectangular_matrix(matrix_flatten_normal_tiss, gamma)
denoise_rectangular_matrix(matrix_flatten_normal_tiss, gamma)

Arguments

`matrix_flatten_normal_tiss`	A rectangular noisy matrix to denoise. It is return by `flatten_normal_tiss` function.
`gamma`	A parameter that indicates the magnitude of the noise assumed in the flat data matrix for the generation of the Healthy State Model. If it takes the value `NA` the magnitude of the noise is assumed to be unknown. By default gamma is unknown.

Value

A the normal space which has the same dimension denoised version of the matrix returned by flatten_normal_tiss.

Disease-Specific Genomic Analysis

Description

Disease-Specific Genomic Analysis (dsga). This analysis, developed by Nicolau et al., allows the calculation of the "disease component" of a expression matrix which consists of, through linear models, eliminating the part of the data that is considered normal or healthy and keeping only the component that is due to the disease. It is intended to precede other techniques like classification or clustering. For more information see Disease-specific genomic analysis: identifying the signature of pathologic biology (doi: 10.1093/bioinformatics/btm033).

Usage

dsga(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag = NA,
  gamma = NA,
  na.rm = TRUE
)
dsga(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag = NA,
  gamma = NA,
  na.rm = TRUE
)

Arguments

`full_data`	Input matrix whose columns correspond to the patients and rows to the genes.
`survival_time`	Numerical vector of the same length as the number of columns of `full_data`. In addition, the patients must be in the same order as in `full_data`. For the patients whose sample is pathological should be indicated the time between the disease diagnosis and event (death, relapse or other). If the event has not occurred, it should be indicated the time until the end of follow-up. Patients whose sample is from healthy tissue must have an NA value
`survival_event`	Numerical vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. For the the patients with pathological sample should be indicated whether the event has occurred (1) or not (0). Only these values are valid and healthy patients must have an NA value.
`case_tag`	Character vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. It must be indicated for each patient whether its sample is from pathological or healthy tissue. One value should be used to indicate whether the patient's sample is healthy and another value should be used to indicate whether the patient's sample is pathological. The user will then be asked which one indicates whether the patient is healthy. Only two values are valid in the vector in total.
`control_tag`	Tag of the healthy sample.E.g. "T"
`gamma`	A parameter that indicates the magnitude of the noise assumed in the flat data matrix for the generation of the Healthy State Model. If it takes the value `NA` the magnitude of the noise is assumed to be unknown. By default gamma is unknown.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

A dsga object. It contains: the full_data without NAN's values, the label designated for healthy samples (control_tag), the case_tag vector without NAN's values, the survival_event, the the survival_time the matrix with the normal space (linear space generated from normal tissue samples) and the matrix of the disease components (the transformed full_data matrix from which the normal component has been removed).

Examples


dsga_obj <- dsga(full_data,  survival_time, survival_event, case_tag)
dsga_obj <- dsga(full_data,  survival_time, survival_event, case_tag)

Flatten normal tissues

Description

Given a matrix containing the expression values of n healthy tissue samples, it produces the flattened vector matrix as reported in "Disease-specific genomic analysis: identifying the signature of pathology biology".

Usage

flatten_normal_tiss(normal_tiss)
flatten_normal_tiss(normal_tiss)

Arguments

normal_tiss

A normal tissue data gene expression matrix. The columns should be the samples and the rows should be the genes.

Value

A gene expression matrix containing the flattened version of the vectors.

Examples


normal_tissue_matrix <- matrix(stats::rnorm(36), nrow=6)
flatten_normal_tiss(normal_tissue_matrix)

normal_tissue_matrix <- matrix(stats::rnorm(36), nrow=6)
flatten_normal_tiss(normal_tissue_matrix)

Gene expression matrix

Description

Matrix containing gene expression profiling of 104 breast cancer and 17 normal breast biopsies. Expression profiling data by array (platform HG-U133_Plus_2).

Usage

data(full_data, package = "GSSTDA")
data(full_data, package = "GSSTDA")

Format

Gene expression matrix with 4165 rows and 121 columns.

The columns correspond to the patients and the rows to the genes. The column names correspond to the patient identifier in GEO. The row names correspond to the gene names.

Details

Normalized gene expression data GSE42568. Background correction, summarization, and quantile normalization were carried out using the fRMA method implemented in the fRMA package. Filtered probes that did not target genes with valid gene id were filtered and those probes targeting the same gene were collapse by thanking those presenting the highest row variance using the WGCNA::collapseRows function. Subsequently, due to CRAN publication requirements, the number of genes is randomly reduced to 4165 genes.

Source

The data are from the study GSE42568 available in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568. The data were processed as explained in the details section.

Gene selection and filter function

Description

Gene selection and calculation of filter function values. After fitting a Cox proportional hazard model to each gene, this function makes a selection of genes according to both their variability within the database and their relationship with survival. Subsequently, with the genes selected, the values of the filtering functions are calculated for each patient. The filter function allows to summarise each vector of each individual in a single data. This function takes into account the survival associated with each gene. In particular, the implemented filter function performs the vector magnitude in the Lp norm (as well as k powers of this magnitude) of the vector resulting of weighting each element of the column vector by the Z score obtained in the cox proportional hazard model.

Usage

gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)
gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)

Arguments

`data_object`	Object with: full_data Input matrix whose columns correspond to the patients and rows to the genes. survival_time Numerical vector of the same length as the number of columns of `full_data`. In addition, the patients must be in the same order as in `full_data`. For the patients whose sample is pathological should be indicated the time between the disease diagnosis and event (death, relapse or other). If the event has not occurred, it should be indicated the time until the end of follow-up. Patients whose sample is from healthy tissue must have an NA value survival_event Numerical vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. For the the patients with pathological sample should be indicated whether the event has occurred (1) or not (0). Only these values are valid and healthy patients must have an NA value. case_tag Character vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. It must be indicated for each patient whether its sample is from pathological or healthy tissue. One value should be used to indicate whether the patient's sample is healthy and another value should be used to indicate whether the patient's sample is pathological. The user will then be asked which one indicates whether the patient is healthy. Only two values are valid in the vector in total.
`gen_select_type`	Option. Options on how to select the genes to be used in the mapper. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis). "Top_Bot" default option.
`percent_gen_select`	Percentage (from zero to one hundred) of genes to be selected to be used in mapper. 10 default option.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

A gene_selection_object. It contains:

the full_data without NAN's values (data)
the cox_all_matrix (a matrix with the results of the application of proportional hazard models: with the regression coefficients, the odds ratios, the standard errors of each coefficient, the Z values (coef/se_coef) and the p-values for each Z value)
a vector with the name of the selected genes
the matrix of disease components with only the rows of the selected genes (genes_disease_component)
and the vector of the values of the filter function.

Examples


data_object <- list("full_data" = full_data, "survival_time" = survival_time,
"survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"
gene_selection_obj <- gene_selection(data_object,
gen_select_type ="top_bot", percent_gen_select=10)
data_object <- list("full_data" = full_data, "survival_time" = survival_time,
"survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"
gene_selection_obj <- gene_selection(data_object,
gen_select_type ="top_bot", percent_gen_select=10)

Private gene_selection_

Description

Private function to gene selection

Usage

gene_selection_(
  full_data,
  survival_time,
  survival_event,
  control_tag_cases,
  gen_select_type,
  num_gen_select,
  matrix_disease_component = NULL
)
gene_selection_(
  full_data,
  survival_time,
  survival_event,
  control_tag_cases,
  gen_select_type,
  num_gen_select,
  matrix_disease_component = NULL
)

Arguments

`full_data`	Input matrix whose columns correspond to the patients and rows to the genes.
`survival_time`	Numerical vector of the same length as the number of columns of `full_data`. In addition, the patients must be in the same order as in `full_data`. For the patients whose sample is pathological should be indicated the time between the disease diagnosis and event (death, relapse or other). If the event has not occurred, it should be indicated the time until the end of follow-up. Patients whose sample is from healthy tissue must have an NA value
`survival_event`	Numerical vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. For the the patients with pathological sample should be indicated whether the event has occurred (1) or not (0). Only these values are valid and healthy patients must have an NA value.
`control_tag_cases`	Numeric vector with the indices of the columns corresponding to the healthy sample patients.
`gen_select_type`	Option. Options on how to select the genes to be used in the mapper. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis). "Top_Bot" default option.
`num_gen_select`	Number of genes to be selected to be used in mapper.
`matrix_disease_component`	Optional, only necessary in case of gene selection after dsga has been performed. Matrix of the disease components (the transformed `full_data` matrix from which the normal component has been removed) from the `dsga_function`.

Value

A gene_selection_object. It contains:

the matrix with which the gene selection has been performed without NAN's values (data). It is the matrix_disease_component in case it has been performed from a dsga_object or full_data in the opposite case.
the cox_all_matrix (a matrix with the results of the application of proportional hazard models: with the regression coefficients, the odds ratios, the standard errors of each coefficient, the Z values (coef/se_coef) and the p-values for each Z value)
a vector with the name of the selected genes
the matrix of disease components with only the rows of the selected genes (genes_disease_component)
and the vector of the values of the filter function.

Examples


gen_select_type <- "Top_Bot"
percent_gen_select <- 10
control_tag_cases <- which(case_tag == "NT")
gene_selection_obj <- gene_selection_(full_data, survival_time, survival_event,
control_tag_cases, gen_select_type ="top_bot", num_gen_select = 10)
gen_select_type <- "Top_Bot"
percent_gen_select <- 10
control_tag_cases <- which(case_tag == "NT")
gene_selection_obj <- gene_selection_(full_data, survival_time, survival_event,
control_tag_cases, gen_select_type ="top_bot", num_gen_select = 10)

Gene selection based on variability and the relationship to survival.

Description

It selects genes for mapper based on the product of standard deviation of the rows (genes) in the disease component matrix plus one times the Z score obtained by fitting a cox proportional hazard model to the level of each gene. For further information see "Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival"

Usage

gene_selection_surv(
  case_disease_component,
  cox_all_matrix,
  gen_select_type,
  num_gen_select
)
gene_selection_surv(
  case_disease_component,
  cox_all_matrix,
  gen_select_type,
  num_gen_select
)

Arguments

`case_disease_component`	Disease component matrix (output of the function `generate_disease_component`) having selected only the columns belonging to disease samples. The names of the rows must be the names of the genes.
`cox_all_matrix`	Output from the `cox_all_genes` function. Data.frame with information on the relationship between genes and survival.
`gen_select_type`	Option. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis).
`num_gen_select`	Number of genes to be selected (those with the highest product value).

Value

Character vector with the names of the selected genes.

gene_selection_classes.default

Description

Private function to select Gene without dsga process

Usage

## Default S3 method:
gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)
## Default S3 method:
gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)

Arguments

`data_object`	Object with: full_data Input matrix whose columns correspond to the patients and rows to the genes. survival_time Numerical vector of the same length as the number of columns of `full_data`. In addition, the patients must be in the same order as in `full_data`. For the patients whose sample is pathological should be indicated the time between the disease diagnosis and event (death, relapse or other). If the event has not occurred, it should be indicated the time until the end of follow-up. Patients whose sample is from healthy tissue must have an NA value survival_event Numerical vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. For the the patients with pathological sample should be indicated whether the event has occurred (1) or not (0). Only these values are valid and healthy patients must have an NA value. case_tag Character vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. It must be indicated for each patient whether its sample is from pathological or healthy tissue. One value should be used to indicate whether the patient's sample is healthy and another value should be used to indicate whether the patient's sample is pathological. The user will then be asked which one indicates whether the patient is healthy. Only two values are valid in the vector in total.
`gen_select_type`	Option. Options on how to select the genes to be used in the mapper. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis). "Top_Bot" default option.
`percent_gen_select`	Percentage (from zero to one hundred) of genes to be selected to be used in mapper. 10 default option.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

A gene_selection object. It contains: the full_data without NAN's values, the control tag of the healthy patient, the matrix with the normal space and the matrix of the disease components.

Examples


data_object <- list("full_data" = full_data, "survival_time" = survival_time,
"survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"
gene_selection_object <- gene_selection(data_object, gen_select_type ="top_bot",
                                      percent_gen_select = 10)
data_object <- list("full_data" = full_data, "survival_time" = survival_time,
"survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"
gene_selection_object <- gene_selection(data_object, gen_select_type ="top_bot",
                                      percent_gen_select = 10)

gene_selection_classes.dsga_object

Description

Private function to select Gene with dsga object

Usage

## S3 method for class 'dsga_object'
gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)
## S3 method for class 'dsga_object'
gene_selection(data_object, gen_select_type, percent_gen_select, na.rm = TRUE)

Arguments

`data_object`	dsga object information
`gen_select_type`	Option. Options on how to select the genes to be used in the mapper. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis). "Top_Bot" default option.
`percent_gen_select`	Percentage (from zero to one hundred) of genes to be selected to be used in mapper. 10 default option.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

A gene_selection object. It contains: the full_data without NAN's values, the control tag of the healthy patient, the matrix with the normal space and the matrix of the disease components.

Examples


dsga_obj <- dsga(full_data, survival_time, survival_event, case_tag, na.rm = "checked")

gene_selection_object <- gene_selection(dsga_obj, gen_select_type ="top_bot",
                                      percent_gen_select = 10)
dsga_obj <- dsga(full_data, survival_time, survival_event, case_tag, na.rm = "checked")

gene_selection_object <- gene_selection(dsga_obj, gen_select_type ="top_bot",
                                      percent_gen_select = 10)

Generate disease component matrix.

Description

This function produces a disease component matrix from an expression matrix and the denoised flattened matrix constructed from "healthy tissue data".

Usage

generate_disease_component(full_data, normal_space)
generate_disease_component(full_data, normal_space)

Arguments

`full_data`	Input matrix whose columns correspond to the patients and rows to the gens. Both tumour and healthy samples should be included.
`normal_space`	Denoised flattened matrix constructed from "healthy tissue data". Output of the function `denoise_rectangular_matrix`.

Value

Disease component matrix that contains the disease component of the provided normal space

Examples


full_data <- matrix(stats::rnorm(120),ncol=20)
normal_tissue <- t(full_data)
normal_tissue_f <- flatten_normal_tiss(normal_tissue)
normal_tissue_f_d <- denoise_rectangular_matrix(normal_tissue_f, gamma=NA)
disease_component <- generate_disease_component(t(full_data),normal_tissue_f_d)
full_data <- matrix(stats::rnorm(120),ncol=20)
normal_tissue <- t(full_data)
normal_tissue_f <- flatten_normal_tiss(normal_tissue)
normal_tissue_f_d <- denoise_rectangular_matrix(normal_tissue_f, gamma=NA)
disease_component <- generate_disease_component(t(full_data),normal_tissue_f_d)

Extract intervals from filter function output values.

Description

It calculates the intervals of the given output values of a filter function according to the given number and percentage of overlap.

Usage

get_intervals_One_D(filter_values, num_intervals, percent_overlap)
get_intervals_One_D(filter_values, num_intervals, percent_overlap)

Arguments

`filter_values`	Vector obtained after applying the filtering function to the input matrix, i.e, a vector with the filtering function values for each included sample.
`num_intervals`	Number of intervals to divide the filtering function values in.
`percent_overlap`	Percentage of overlap between intervals.

Value

Returns a list with the set of intervals for the filtering function values.

Gene Structure Survival using Topological Data Analysis (GSSTDA).

Description

Gene Structure Survival using Topological Data Analysis. This function implements an analysis for expression array data based on the Progression Analysis of Disease developed by Nicolau et al. (doi: 10.1073/pnas.1102826108) that allows the information contained in an expression matrix to be condensed into a combinatory graph. The novelty is that information on survival is integrated into the analysis.

The analysis consists of 3 parts: a preprocessing of the data, the gene selection and the filter function, and the mapper algorithm. The preprocessing is specifically the Disease Specific Genomic Analysis (proposed by Nicolau et al.) that consists of, through linear models, eliminating the part of the data that is considered "healthy" and keeping only the component that is due to the disease. The genes are then selected according to their variability and whether they are related to survival and the values of the filtering function for each patient are calculated taking into account the survival associated with each gene. Finally, the mapper algorithm is applied from the disease component matrix and the values of the filter function obtaining a combinatory graph.

Usage

gsstda(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag = NA,
  gamma = NA,
  gen_select_type = "Top_Bot",
  percent_gen_select = 10,
  num_intervals = 5,
  percent_overlap = 40,
  distance_type = "correlation",
  clustering_type = "hierarchical",
  num_bins_when_clustering = 10,
  linkage_type = "single",
  optimal_clustering_mode = NA,
  silhouette_threshold = 0.25,
  na.rm = TRUE
)
gsstda(
  full_data,
  survival_time,
  survival_event,
  case_tag,
  control_tag = NA,
  gamma = NA,
  gen_select_type = "Top_Bot",
  percent_gen_select = 10,
  num_intervals = 5,
  percent_overlap = 40,
  distance_type = "correlation",
  clustering_type = "hierarchical",
  num_bins_when_clustering = 10,
  linkage_type = "single",
  optimal_clustering_mode = NA,
  silhouette_threshold = 0.25,
  na.rm = TRUE
)

Arguments

`full_data`	Input matrix whose columns correspond to the patients and rows to the genes.
`survival_time`	Numerical vector of the same length as the number of columns of `full_data`. In addition, the patients must be in the same order as in `full_data`. For the patients whose sample is pathological should be indicated the time between the disease diagnosis and event (death, relapse or other). If the event has not occurred, it should be indicated the time until the end of follow-up. Patients whose sample is from healthy tissue must have an NA value
`survival_event`	Numerical vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. For the the patients with pathological sample should be indicated whether the event has occurred (1) or not (0). Only these values are valid and healthy patients must have an NA value.
`case_tag`	Character vector of the same length as the number of columns of `full_data`. Patients must be in the same order as in `full_data`. It must be indicated for each patient whether its sample is from pathological or healthy tissue. One value should be used to indicate whether the patient's sample is healthy and another value should be used to indicate whether the patient's sample is pathological. The user will then be asked which one indicates whether the patient is healthy. Only two values are valid in the vector in total.
`control_tag`	Tag of the healthy sample.E.g. "T"
`gamma`	A parameter that indicates the magnitude of the noise assumed in the flat data matrix for the generation of the Healthy State Model. If it takes the value `NA` the magnitude of the noise is assumed to be unknown. By default gamma is unknown.
`gen_select_type`	Option. Options on how to select the genes to be used in the mapper. Select the "Abs" option, which means that the genes with the highest absolute value are chosen, or the "Top_Bot" option, which means that half of the selected genes are those with the highest value (positive value, i.e. worst survival prognosis) and the other half are those with the lowest value (negative value, i.e. best prognosis). "Top_Bot" default option.
`percent_gen_select`	Percentage (from zero to one hundred) of genes to be selected to be used in mapper. 10 default option.
`num_intervals`	Parameter for the mapper algorithm. Number of intervals used to create the first sample partition based on filtering values. 5 default option.
`percent_overlap`	Parameter for the mapper algorithm. Percentage of overlap between intervals. Expressed as a percentage. 40 default option.
`distance_type`	Parameter for the mapper algorithm. Type of distance to be used for clustering. Choose between correlation ("correlation") and euclidean ("euclidean"). "correlation" default option.
`clustering_type`	Parameter for the mapper algorithm. Type of clustering method. Choose between "hierarchical" and "PAM" (“partition around medoids”) options. "hierarchical" default option.
`num_bins_when_clustering`	Parameter for the mapper algorithm. Number of bins to generate the histogram employed by the standard optimal number of cluster finder method. Parameter not necessary if the "optimal_clust_mode" option is "silhouette" or the "clust_type" is "PAM". 10 default option.
`linkage_type`	Parameter for the mapper algorithm. Linkage criteria used in hierarchical clustering. Choose between "single" for single-linkage clustering, "complete" for complete-linkage clustering or "average" for average linkage clustering (or UPGMA). Only necessary for hierarchical clustering. "single" default option.
`optimal_clustering_mode`	Method for selection optimal number of clusters. It is only necessary if the chosen type of algorithm is hierarchical. In this case, choose between "standard" (the method used in the original mapper article) or "silhouette". In the case of the PAM algorithm, the method will always be "silhouette".
`silhouette_threshold`	Minimum value of $\overline{s}$ that a set of clusters must have to be chosen as optimal. Within each interval of the filter function, the average silhouette values $\overline{s}$ are computed for all possible partitions from $2$ to $n-1$, where $n$ is the number of samples within a specific interval. The $n$ that produces the highest value of $\overline{s}$ and that exceeds a specific threshold is selected as the optimum number of clusters. If no partition produces an $\overline{s}$ exceeding the chosen threshold, all samples are then assigned to a unique cluster. The default value is $0.25$. The threshold of $0.25$ for $\overline{s}$ has been chosen based on standard practice, recognizing it as a moderate value that reflects adequate separation and cohesion within clusters.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

A gsstda object. It contains:

the matrix with the normal space normal_space,
the matrix of the disease components normal_space matrix_disease_component,
a matrix with the results of the application of proportional hazard models for each gene (cox_all_matrix),
the genes selected for mapper genes_disease_componen,
the matrix of the disease components with information from these genes only genes_disease_component
and a mapper_obj object. This mapper_obj object contains the values of the intervals (interval_data), the samples included in each interval (sample_in_level), information about the cluster to which the individuals in each interval belong (clustering_all_levels), a list including the individuals contained in each detected node (node_samples), their size (node_sizes), the average of the filter function values of the individuals of each node (node_average_filt) and the adjacency matrix linking the nodes (adj_matrix). Moreover, information is provided on the number of nodes, the average node size, the standard deviation of the node size, the number of connections between nodes, the proportion of connections to all possible connections and the number of ramifications.

Examples


gsstda_object <- gsstda(full_data,  survival_time, survival_event, case_tag, gamma=NA,
                 gen_select_type="Top_Bot", percent_gen_select=10,
                 num_intervals = 4, percent_overlap = 50,
                 distance_type = "euclidean", num_bins_when_clustering = 8,
                 clustering_type = "hierarchical", linkage_type = "single")
gsstda_object <- gsstda(full_data,  survival_time, survival_event, case_tag, gamma=NA,
                 gen_select_type="Top_Bot", percent_gen_select=10,
                 num_intervals = 4, percent_overlap = 50,
                 distance_type = "euclidean", num_bins_when_clustering = 8,
                 clustering_type = "hierarchical", linkage_type = "single")

Incomplete Marcenko-Pastur Integral

Description

Calculates the incomplete Marcenko-Pastur integral from a lower limit to the upper bound of the distribution's support.

Usage

incMarPas(x0, beta, gamma)
incMarPas(x0, beta, gamma)

Arguments

`x0`	A numeric value representing the lower limit of the integral.
`beta`	A numeric value representing the aspect ratio $m/n$ , where $0 < \beta \leq 1$ .
`gamma`	A numeric value representing an exponent parameter.

Value

A numeric value representing the incomplete Marcenko-Pastur integral.

Extract Information about Nodes

Description

Extract the nodes information based on information about clustering. The individuals who are part of each node are identified

Usage

levels_to_nodes(clust_all_levels_list)
levels_to_nodes(clust_all_levels_list)

Arguments

clust_all_levels_list

A list with information on the levels obtained from the clust_all_levels function.

Value

A list including the individuals content of each detected node. List of character vectors. Each of the vectors contains the names of the individuals at each node.

Filtering function

Description

A filtering function for mapper that projects $$R$^n$ into $R$. It calculates for each column of the matrix (each patient), its value of the filtering function. Specifically, it computes the vector magnitude in the Lp norm (as well as k powers of this magnitude) of the vector resulting of weighting each element of the column vector by the Z score obtained by fitting a cox proportional hazard model to the level of each gene. For further information see "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies"

Usage

lp_norm_k_powers_surv(genes_disease_component, p, k, cox_all_matrix)
lp_norm_k_powers_surv(genes_disease_component, p, k, cox_all_matrix)

Arguments

`genes_disease_component`	Disease component matrix (output of the function of `generate_disease_component`), after having selected the rows corresponding to the selected genes.
`p`	integer. It indicates the p norm to be calculated. If k = 1 and p = 2, the function computes the standard (Euclidean) vector magnitude of each column. For larger values of p the weight of genes with larger levels is greater.
`k`	integer. Powers of the vector magnitude. If k = 1 and p = 2, the function computes the standard (Euclidean) vector magnitude of each column.
`cox_all_matrix`	A matrix with the output of the `cox_all_genes` function that stores the information of all cox proportional hazard model tests for each gene in the dataset.

Value

A numeric vector including the values produced by the function for each sample in the dataset.

Map to color

Description

Auxiliary function that maps a numeric vector, the average node filtering function values, to a color vector.

Usage

map_to_color(x, limits = NULL)
map_to_color(x, limits = NULL)

Arguments

`x`	A vector of numeric values storing the average filtering function values found in the samples placed into a specific node.
`limits`	A two element numeric vector including the range of values. This is optional.

Value

A vector of the same length of x with colors ranging from blue to red.

Mapper object

Description

TDA are persistent homology and mapper. Persistent homology borrows ideas from abstract algebra to identify particular aspects related to the shape of the data such as the number of connected components and the presence of higher-dimensional holes, whereas mapper condenses the information of high-dimensional datasets into a combinatory graph or simplicial complex that is referred to as the skeleton of the dataset. This implementation is the mapper of one dimension, i.e. using only one filter function value.

Usage

mapper(
  data,
  filter_values,
  num_intervals = 5,
  percent_overlap = 40,
  distance_type = "correlation",
  clustering_type = "hierarchical",
  num_bins_when_clustering = 10,
  linkage_type = "single",
  optimal_clustering_mode = NA,
  silhouette_threshold = 0.25,
  na.rm = TRUE
)
mapper(
  data,
  filter_values,
  num_intervals = 5,
  percent_overlap = 40,
  distance_type = "correlation",
  clustering_type = "hierarchical",
  num_bins_when_clustering = 10,
  linkage_type = "single",
  optimal_clustering_mode = NA,
  silhouette_threshold = 0.25,
  na.rm = TRUE
)

Arguments

`data`	Input matrix whose columns correspond to the individuals and rows to the features.
`filter_values`	Vector obtained after applying the filtering function to the input matrix, i.e, a vector with the filtering function values for each included sample.
`num_intervals`	Number of intervals used to create the first sample partition based on filtering values. 5 default option.
`percent_overlap`	Percentage of overlap between intervals. Expressed as a percentage. 40 default option.
`distance_type`	Type of distance to be used for clustering. Choose between correlation ("correlation") and euclidean ("euclidean"). "correlation" default option.
`clustering_type`	Type of clustering method. Choose between "hierarchical" and "PAM" (“partition around medoids”) options. "hierarchical" default option.
`num_bins_when_clustering`	Number of bins to generate the histogram employed by the standard optimal number of cluster finder method. Parameter not necessary if the "optimal_clustering_mode" option is "silhouette" or the "clustering_type" is "PAM". 10 default option.
`linkage_type`	Linkage criteria used in hierarchical clustering. Choose between "single" for single-linkage clustering, "complete" for complete-linkage clustering or "average" for average linkage clustering (or UPGMA). Only necessary for hierarchical clustering. "single" default option.
`optimal_clustering_mode`	Method for selection optimal number of clusters. It is only necessary if the chosen type of algorithm is hierarchical. In this case, choose between "standard" (the method used in the original mapper article) or "silhouette". In the case of the PAM algorithm, the method will always be "silhouette".
`silhouette_threshold`	Minimum value of $\overline{s}$ that a set of clusters must have to be chosen as optimal. Within each interval of the filter function, the average silhouette values $\overline{s}$ are computed for all possible partitions from $2$ to $n-1$, where $n$ is the number of samples within a specific interval. The $n$ that produces the highest value of $\overline{s}$ and that exceeds a specific threshold is selected as the optimum number of clusters. If no partition produces an $\overline{s}$ exceeding the chosen threshold, all samples are then assigned to a unique cluster. The default value is $0.25$. The threshold of $0.25$ for $\overline{s}$ has been chosen based on standard practice, recognizing it as a moderate value that reflects adequate separation and cohesion within clusters.
`na.rm`	`logical`. If `TRUE`, `NA` rows are omitted. If `FALSE`, an error occurs in case of `NA` rows. TRUE default option.

Value

Examples


control_tag_cases <- which(case_tag == "NT")
gene_selection_object <- gene_selection_(full_data, survival_time, survival_event,
control_tag_cases, gen_select_type ="top_bot", num_gen_select = 10)

mapper_object <- mapper(data = gene_selection_object[["genes_disease_component"]],
filter_values = gene_selection_object[["filter_values"]],
num_intervals = 5,
percent_overlap = 40, distance_type = "correlation",
clustering_type = "hierarchical",
linkage_type = "single")
control_tag_cases <- which(case_tag == "NT")
gene_selection_object <- gene_selection_(full_data, survival_time, survival_event,
control_tag_cases, gen_select_type ="top_bot", num_gen_select = 10)

mapper_object <- mapper(data = gene_selection_object[["genes_disease_component"]],
filter_values = gene_selection_object[["filter_values"]],
num_intervals = 5,
percent_overlap = 40, distance_type = "correlation",
clustering_type = "hierarchical",
linkage_type = "single")

Median of the Marcenko-Pastur Distribution

Description

Calculates the median of the Marcenko-Pastur distribution for a given aspect ratio.

Usage

MedianMarcenkoPastur(beta)
MedianMarcenkoPastur(beta)

Arguments

beta

A numeric value representing the aspect ratio $m/n$ , where $0 < \beta \leq 1$ .

Value

A numeric value representing the median of the Marcenko-Pastur distribution for the specified beta.

one_D_Mapper

Description

Wrapping function to carry out the complete process.

Usage

one_D_Mapper(mapper_object_ini)
one_D_Mapper(mapper_object_ini)

Arguments

mapper_object_ini

Mapper TDA initializated object generated by mapper function.

Value

A mapper_obj object. It contains the values of the intervals (interval_data), the samples included in each interval (sample_in_level), information about the cluster to which the individuals in each interval belong (clustering_all_levels), a list including the individuals contained in each detected node (node_samples), their size (node_sizes), the average of the filter function values of the individuals of each node (node_average_filt) and the adjacency matrix linking the nodes (adj_matrix). Moreover, information is provided on the number of nodes, the average node size, the standard deviation of the node size, the number of connections between nodes, the proportion of connections to all possible connections and the number of ramifications.

Optimal SVHT Coefficient with Known Noise Level

Description

Computes the optimal SVHT coefficient when the noise level is known.

Usage

optimal_SVHT_coef_gamma_known(beta)
optimal_SVHT_coef_gamma_known(beta)

Arguments

beta

A numeric vector representing the aspect ratio $m/n$ , where $0 < \beta \leq 1$ .

Value

A numeric vector of optimal SVHT coefficients corresponding to each aspect ratio in beta.

Examples

beta <- 0.5
optimal_SVHT_coef_gamma_known(beta)
beta <- 0.5
optimal_SVHT_coef_gamma_known(beta)

Optimal SVHT Coefficient with Unknown Noise Level

Description

Computes the optimal SVHT coefficient when the noise level is unknown.

Usage

optimal_SVHT_coef_gamma_unknown(beta)
optimal_SVHT_coef_gamma_unknown(beta)

Arguments

beta

A numeric vector representing the aspect ratio $m/n$ , where $0 < \beta \leq 1$ .

Value

A numeric vector of optimal SVHT coefficients corresponding to each aspect ratio in beta.

Examples

beta <- 0.5
optimal_SVHT_coef_gamma_unknown(beta)
beta <- 0.5
optimal_SVHT_coef_gamma_unknown(beta)

plot dsga

Description

It draws the heatmap of the dsga result by selecting the 100 genes with the highest variability between samples.

Usage

plot_dsga(selected_matrix_disease_component, case_tag)
plot_dsga(selected_matrix_disease_component, case_tag)

Arguments

selected_matrix_disease_component

Disease component matrix of the selected genes that contains the disease component of all patients. Output of the function generate_disease_component.

case_tag

Character vector of the same length as the number of columns of full_data. Patients must be in the same order as in full_data. It must be indicated for each patient whether he/she is healthy or not. One value should be used to indicate whether the patient is healthy and another value should be used to indicate whether the patient's sample is tumourous. The user will then be asked which one indicates whether the patient is healthy. Only two values are valid in the vector in total.

Value

The heatmap of the dsga result.

Plot mapper

Description

This function produces an interactive network plot using the visNetork function from the mapper results.

Usage

plot_mapper(mapper_object, trans_node_size = TRUE, exp_to_res = 1/2)
plot_mapper(mapper_object, trans_node_size = TRUE, exp_to_res = 1/2)

Arguments

`mapper_object`	A list produced as an output of the `one_D_Mapper` function.
`trans_node_size`	Logical, it indicates whether you want to resize the size of the nodes. `TRUE` default option.
`exp_to_res`	Only necessary if trans_node_size is `TRUE`. An exponent of the form 1/n to which the node sizes must be raised in order to resize them.

Value

Plots an interactive network using the visNetwork function.

Examples


# Create data object
data_object <- list("full_data" = full_data, "survival_time" = survival_time,
                   "survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"

#Select gene from data object
gene_selection_object <- gene_selection(data_object, gen_select_type="top_bot",
 percent_gen_select=10)

mapper_object <- mapper(data = gene_selection_object[["genes_disease_component"]],
filter_values = gene_selection_object[["filter_values"]],
num_intervals = 5,
percent_overlap = 40, distance_type = "correlation",
clustering_type = "hierarchical",
linkage_type = "single")
plot_mapper(mapper_object)
# Create data object
data_object <- list("full_data" = full_data, "survival_time" = survival_time,
                   "survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"

#Select gene from data object
gene_selection_object <- gene_selection(data_object, gen_select_type="top_bot",
 percent_gen_select=10)

mapper_object <- mapper(data = gene_selection_object[["genes_disease_component"]],
filter_values = gene_selection_object[["filter_values"]],
num_intervals = 5,
percent_overlap = 40, distance_type = "correlation",
clustering_type = "hierarchical",
linkage_type = "single")
plot_mapper(mapper_object)

results dsga

Description

It calculates the 100 genes with the highest variability in the matrix disease component between samples and use them to draw the heat map.

Usage

results_dsga(matrix_disease_component, case_tag)
results_dsga(matrix_disease_component, case_tag)

Arguments

matrix_disease_component

Disease component matrix that contains the disease component of all patients. Output of the function generate_disease_component.

case_tag

Value

A heatmap of the 100 genes with the highest variability in the matrix disease component.

Samples in levels

Description

This function returns a list of vectors containing the individuals included at each level, i.e. the vectors of individuals with a value of the filter function within each of the intervals.

Usage

samples_in_levels(interval_data, filter_values)
samples_in_levels(interval_data, filter_values)

Arguments

`interval_data`	Filter function intervals. List with the set of intervals for the filtering function values produced by the `get_intervals_One_D` function.
`filter_values`	Vector obtained after applying the filtering function to the input matrix, i.e, a vector with the filtering function values for each included sample.

Value

A list of character vectors with the samples included in each of the levels (i.e. each of the intervals of the values of the filter functions).

Survival event vector

Description

Character vector of length 121 containing whether or not the patient is relapsed.

Usage

data(survival_event, package = "GSSTDA")
data(survival_event, package = "GSSTDA")

Format

Character vector of length 121.

A value of "0" indicates that the patient did not relapse during follow-up, a value of "1" indicates that the patient did. Samples from healthy tissue contain a value of NA.

Source

The data are from the study GSE42568. Information extracted from the file GSE42568_family.soft.gz available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568.

Survival time vector

Description

Numeric vector of length 121 containing the time in months until the relapse of the patient or until the end of the follow-up in case the patient has not relapsed.

Usage

data(survival_time, package = "GSSTDA")
data(survival_time, package = "GSSTDA")

Format

Numeric vector of length 121.

Time in months. Samples from healthy tissue contain a value of NA.

Source

The data are from the study GSE42568. Information extracted from the file GSE42568_family.soft.gz available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568

Package 'GSSTDA'

Help Index

Case-control vector

Description

Usage

Format

Source

check_arg_mapper

Description

Usage

Arguments

Value

check_filter_values

Description

Usage

Arguments

Value

check_full_data

Description

Usage

Arguments

Value

check_gene_selection

Description

Usage

Arguments

Value

check_vectors

Description

Usage

Arguments

Value

Get clusters for all data level

Description

Usage

Arguments

Value

Get clusters for a particular data level

Description

Usage

Arguments

Value

Computes the adjacency matrix.

Description

Usage

Arguments

Value

Survival analysis based on gene expression levels.

Description

Usage

Arguments

Value

Rectangular Matrix Denoiser.

Description

Usage

Arguments

Value

Disease-Specific Genomic Analysis

Description

Usage

Arguments

Value

Examples

Flatten normal tissues

Description

Usage

Arguments

Value

Examples

Gene expression matrix

Description

Usage

Format

Details

Source

Gene selection and filter function

Description

Usage

Arguments

Value