This vignette represents an introduction to the use of the package G-SS-TDA.
library(GSSTDA)
Loading data:
See GSSTDA documentation for further information.
data("full_data")
data("survival_time")
data("survival_event")
data("case_tag")
Declare the necessary parameters of the GSSTDA object.
The gen_select_type parameter is used to choose the option on how to select the genes to be used in the mapper. Choose between “Abs” and “Top_Bot”. The percent_gen_select parameter is the percentage of genes to be selected to be used in mapper.
# Gene selection information
<- "Top_Bot"
gen_select_type <- 10 # Percentage of genes to be selected percent_gen_select
For the mapper, it is necessary to know the number of intervals into which the values of the filter functions will be divided and the overlap between them (). Default are 5 and 40 respectively. It is also necessary to choose the type of distance to be used for clustering within each interval (choose between correlation (“cor”), default, and euclidean (“euclidean”)) and the clustering type (choose between “hierarchical”, default, and “PAM” (“partition around medoids”) options).
For hierarchical clustering only, you will be asked by the console to choose the mode in which the number of clusters will be chosen (choose between “silhouette”, default, and “standard”). If the mode is “standard” you can indicate the number of bins to generate the histogram (, by default 10). If the clustering method is “PAM”, the default method will be “silhouette”. Also, if the clustering type is hierarchical you can choose the type of linkage criteria ( choose between “single”, “complete” and “average”).
#Mapper information
<- 5
num_intervals <- 40
percent_overlap <- "cor"
distance_type <- "hierarchical"
clustering_type <- "single" # only necessary if the type of clustering is hierarchical
linkage_type # num_bins_when_clustering <- 10 # only necessary if the type of clustering is hierarchical
# and the optimal_clustering_mode is "silhouette"
# (this is not the case)
The package allows the various steps required for GSSTDA to be performed separately or together in one function.
This analysis, developed by Nicolau et al. is independent of the rest of the process and can be used with the data for further analysis other than mapper. It allows the calculation of the “disease component” 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.
<- DGSA(full_data, survival_time, survival_event, case_tag)
DGSA_object #> Are the columns of the data set the patient and the rows the genes?: yes/no
#> 0 missing values and NaN's are omitted in the genes (rows)
#> What is the tag of the healthy patient (value in the case_tag)? (NT or T):
#> [1] "The case tag is ' NT ' by default"
#>
#> BLOCK I: The pre-process DGSA is started
#>
#> BLOCK I: The pre-process DGSA is finished
After performing a survival analysis of each gene, this function selects the genes to be used in the mapper 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.
<- geneSelection(DGSA_object, gen_select_type, percent_gen_select)
geneSelection_object #> [1] "DGSA_object"
#>
#> BLOCK II: The gene selection is started
#> Calculating the matrix of Zcox
#>
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#>
#> BLOCK II: The gene selection is finished
Another option to execute the second step of the process. Create a object “data_object” with the require information. This could be used when you do not want to apply DGSA (RBR, duda).
# Create data object
<- list("full_data" = full_data, "survival_time" = survival_time,
data_object "survival_event" = survival_event, "case_tag" = case_tag)
class(data_object) <- "data_object"
#Select gene from data object
<- geneSelection(data_object, gen_select_type, percent_gen_select)
geneSelection_object #> Are the columns of the data set the patient and the rows the genes?: yes/no
#> 0 missing values and NaN's are omitted in the genes (rows)
#> What is the tag of the healthy patient (value in the case_tag)? (NT or T):
#> [1] "The case tag is ' NT ' by default"
#>
#> BLOCK II: The gene selection is started
#> Calculating the matrix of Zcox
#>
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#>
#> BLOCK II: The gene selection is finished
Mapper condenses the information of high-dimensional datasets into a combinatory graph that is referred to as the skeleton of the dataset. To do so, it divides the dataset into different levels according to its value of the filtering function. These levels overlap each other. Within each level, an independent clustering is performed using the input matrix and the indicated distance type. Subsequently, clusters from different levels that share patients with each other are joined by a vertex.
This function is independent from the rest and could be used without having done DGSA and gene selection
<- mapper(full_data = geneSelection_object[["genes_disease_component"]],
mapper_object filter_values = geneSelection_object[["filter_values"]],
num_intervals = num_intervals,
percent_overlap = percent_overlap, distance_type = distance_type,
clustering_type = clustering_type,
linkage_type = linkage_type,
optimal_clustering_mode = optimal_clustering_mode)
#> Are the columns of the data set the patient and the rows the genes?: yes/no
#> 0 missing values and NaN's are omitted in the genes (rows)