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.

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”).

The package allows the various steps required for GSSTDA to be performed separately or together in one function.

OPTION #1 (the three blocks of the G-SS-TDA process are in separate function):

First step of the process: DGSA.

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_object <- DGSA(full_data, survival_time, survival_event, case_tag)
#> 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

Second step of the process: Select the genes within the DGSA object created in the previous step and calcute the values of the filtering functions.

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_object <- geneSelection(DGSA_object, gen_select_type, percent_gen_select)
#> [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
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
geneSelection_object <- geneSelection(data_object, gen_select_type, percent_gen_select)
#> 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

Third step of the process: Create the mapper object with disease component matrix

with only the selected genes and the filter function obtained in the gene selection step.

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_object <- mapper(full_data = geneSelection_object[["genes_disease_component"]], 
                        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)
#> Choose one of the following optimal cluster number method: standard/silhouette:
#> The optimal clustering mode is 'silhouette' by default

Obtain information from the DGSA block created in the previous step.

This function returns the 100 genes with the highest variability within the dataset and builds a heat map with them.

DGSA_information <- results_DGSA(DGSA_object[["matrix_disease_component"]], case_tag)

print(DGSA_information)
#>   [1] "CPB1"     "AGR3"     "BMPR1B"   "ANKRD30A" "CP"       "CEACAM6" 
#>   [7] "ADH1B"    "CXCL13"   "COL11A1"  "CHI3L1"   "CYP4Z1"   "AGR2"    
#>  [13] "CD36"     "CLIC6"    "CYP4X1"   "C19orf33" "AREG"     "APOD"    
#>  [19] "ADIPOQ"   "BEX1"     "AGTR1"    "CALML5"   "CLDN8"    "CYP4B1"  
#>  [25] "CRISP3"   "DACH1"    "CXCL14"   "AFF3"     "CYP2B7P"  "CAPN8"   
#>  [31] "COMP"     "CLSTN2"   "CCL19"    "CFB"      "CYP2T1P"  "CSTA"    
#>  [37] "AZGP1"    "CRISPLD1" "CLGN"     "ANXA3"    "CGA"      "CHGB"    
#>  [43] "CXCL9"    "AKR1C2"   "ACTG2"    "ALOX15B"  "AQP3"     "CLCA2"   
#>  [49] "COL2A1"   "CXCL11"   "CA12"     "C15orf48" "CA2"      "CYP4Z2P" 
#>  [55] "ASPN"     "AR"       "CHRDL1"   "AKR1C1"   "BBOX1"    "ABCA8"   
#>  [61] "CYP4F8"   "CT83"     "CXCL10"   "ABAT"     "CEACAM5"  "ADAMTS15"
#>  [67] "ANLN"     "CLDN1"    "CPE"      "DCLK1"    "CELSR1"   "COL10A1" 
#>  [73] "CFD"      "CNTNAP2"  "CLDN11"   "APOBEC3B" "ALDH3B2"  "C16orf54"
#>  [79] "CDO1"     "ANKRD30B" "COL14A1"  "ARHGAP36" "CECR2"    "BAMBI"   
#>  [85] "CCND1"    "ADGRG6"   "AKR1C3"   "ABCC13"   "C16orf89" "CCL8"    
#>  [91] "CNKSR3"   "DCD"      "CCL5"     "CEP55"    "CST6"     "ARNT2"   
#>  [97] "BEX5"     "COL4A5"   "CLEC3A"   "ARMT1"

Obtain information from the mapper object created in the G-SS-TDA process.

print(mapper_object)
#> $interval_data
#> $interval_data$Level_1
#> [1] 612.7152 639.7351
#> 
#> $interval_data$Level_2
#> [1] 628.9671 655.8870
#> 
#> $interval_data$Level_3
#> [1] 645.1191 672.0390
#> 
#> $interval_data$Level_4
#> [1] 661.2710 688.1909
#> 
#> $interval_data$Level_5
#> [1] 677.4230 704.4429
#> 
#> 
#> $sample_in_level
#> $sample_in_level$Level_1
#> [1] "GSM1045192" "GSM1045193" "GSM1045217" "GSM1045221" "GSM1045288"
#> [6] "GSM1045302"
#> 
#> $sample_in_level$Level_2
#> [1] "GSM1045192" "GSM1045194" "GSM1045211" "GSM1045221" "GSM1045225"
#> [6] "GSM1045243" "GSM1045250" "GSM1045282" "GSM1045288"
#> 
#> $sample_in_level$Level_3
#>  [1] "GSM1045203" "GSM1045206" "GSM1045209" "GSM1045211" "GSM1045212"
#>  [6] "GSM1045213" "GSM1045214" "GSM1045224" "GSM1045225" "GSM1045227"
#> [11] "GSM1045229" "GSM1045237" "GSM1045241" "GSM1045242" "GSM1045243"
#> [16] "GSM1045244" "GSM1045248" "GSM1045250" "GSM1045255" "GSM1045264"
#> [21] "GSM1045266" "GSM1045274" "GSM1045276" "GSM1045278" "GSM1045282"
#> [26] "GSM1045285" "GSM1045293" "GSM1045294" "GSM1045301" "GSM1045305"
#> 
#> $sample_in_level$Level_4
#>  [1] "GSM1045191" "GSM1045195" "GSM1045196" "GSM1045197" "GSM1045198"
#>  [6] "GSM1045199" "GSM1045200" "GSM1045201" "GSM1045203" "GSM1045204"
#> [11] "GSM1045205" "GSM1045206" "GSM1045207" "GSM1045209" "GSM1045210"
#> [16] "GSM1045212" "GSM1045214" "GSM1045216" "GSM1045218" "GSM1045219"
#> [21] "GSM1045222" "GSM1045224" "GSM1045227" "GSM1045228" "GSM1045229"
#> [26] "GSM1045231" "GSM1045232" "GSM1045233" "GSM1045237" "GSM1045238"
#> [31] "GSM1045239" "GSM1045240" "GSM1045241" "GSM1045244" "GSM1045245"
#> [36] "GSM1045246" "GSM1045248" "GSM1045252" "GSM1045254" "GSM1045255"
#> [41] "GSM1045257" "GSM1045258" "GSM1045259" "GSM1045260" "GSM1045261"
#> [46] "GSM1045263" "GSM1045264" "GSM1045266" "GSM1045267" "GSM1045269"
#> [51] "GSM1045270" "GSM1045271" "GSM1045274" "GSM1045275" "GSM1045276"
#> [56] "GSM1045278" "GSM1045280" "GSM1045281" "GSM1045283" "GSM1045284"
#> [61] "GSM1045287" "GSM1045294" "GSM1045296" "GSM1045298" "GSM1045299"
#> [66] "GSM1045300" "GSM1045303" "GSM1045305" "GSM1045306" "GSM1045309"
#> 
#> $sample_in_level$Level_5
#>  [1] "GSM1045191" "GSM1045195" "GSM1045196" "GSM1045197" "GSM1045198"
#>  [6] "GSM1045199" "GSM1045202" "GSM1045204" "GSM1045205" "GSM1045207"
#> [11] "GSM1045208" "GSM1045210" "GSM1045215" "GSM1045216" "GSM1045219"
#> [16] "GSM1045220" "GSM1045222" "GSM1045223" "GSM1045226" "GSM1045228"
#> [21] "GSM1045230" "GSM1045231" "GSM1045234" "GSM1045235" "GSM1045236"
#> [26] "GSM1045238" "GSM1045245" "GSM1045246" "GSM1045247" "GSM1045249"
#> [31] "GSM1045251" "GSM1045253" "GSM1045254" "GSM1045256" "GSM1045258"
#> [36] "GSM1045260" "GSM1045261" "GSM1045262" "GSM1045263" "GSM1045265"
#> [41] "GSM1045267" "GSM1045268" "GSM1045271" "GSM1045272" "GSM1045273"
#> [46] "GSM1045275" "GSM1045277" "GSM1045279" "GSM1045280" "GSM1045281"
#> [51] "GSM1045283" "GSM1045284" "GSM1045286" "GSM1045287" "GSM1045289"
#> [56] "GSM1045290" "GSM1045291" "GSM1045292" "GSM1045295" "GSM1045296"
#> [61] "GSM1045297" "GSM1045299" "GSM1045300" "GSM1045303" "GSM1045304"
#> [66] "GSM1045307" "GSM1045308" "GSM1045309" "GSM1045310" "GSM1045311"
#> 
#> 
#> $clustering_all_levels
#> $clustering_all_levels$Level_1
#> GSM1045192 GSM1045193 GSM1045217 GSM1045221 GSM1045288 GSM1045302 
#>          1          1          1          1          1          1 
#> 
#> $clustering_all_levels$Level_2
#> GSM1045192 GSM1045194 GSM1045211 GSM1045221 GSM1045225 GSM1045243 GSM1045250 
#>          1          1          1          1          1          1          1 
#> GSM1045282 GSM1045288 
#>          1          1 
#> 
#> $clustering_all_levels$Level_3
#> GSM1045203 GSM1045206 GSM1045209 GSM1045211 GSM1045212 GSM1045213 GSM1045214 
#>          1          1          1          1          1          1          1 
#> GSM1045224 GSM1045225 GSM1045227 GSM1045229 GSM1045237 GSM1045241 GSM1045242 
#>          1          1          1          1          1          1          1 
#> GSM1045243 GSM1045244 GSM1045248 GSM1045250 GSM1045255 GSM1045264 GSM1045266 
#>          1          1          1          1          1          1          1 
#> GSM1045274 GSM1045276 GSM1045278 GSM1045282 GSM1045285 GSM1045293 GSM1045294 
#>          1          1          1          1          1          1          1 
#> GSM1045301 GSM1045305 
#>          1          1 
#> 
#> $clustering_all_levels$Level_4
#> GSM1045191 GSM1045195 GSM1045196 GSM1045197 GSM1045198 GSM1045199 GSM1045200 
#>          1          1          1          1          1          1          1 
#> GSM1045201 GSM1045203 GSM1045204 GSM1045205 GSM1045206 GSM1045207 GSM1045209 
#>          1          1          1          1          1          1          1 
#> GSM1045210 GSM1045212 GSM1045214 GSM1045216 GSM1045218 GSM1045219 GSM1045222 
#>          1          1          1          1          1          1          1 
#> GSM1045224 GSM1045227 GSM1045228 GSM1045229 GSM1045231 GSM1045232 GSM1045233 
#>          1          1          1          1          1          1          1 
#> GSM1045237 GSM1045238 GSM1045239 GSM1045240 GSM1045241 GSM1045244 GSM1045245 
#>          1          1          1          1          1          1          1 
#> GSM1045246 GSM1045248 GSM1045252 GSM1045254 GSM1045255 GSM1045257 GSM1045258 
#>          1          1          1          1          1          1          1 
#> GSM1045259 GSM1045260 GSM1045261 GSM1045263 GSM1045264 GSM1045266 GSM1045267 
#>          1          1          1          1          1          1          1 
#> GSM1045269 GSM1045270 GSM1045271 GSM1045274 GSM1045275 GSM1045276 GSM1045278 
#>          1          1          1          1          1          1          1 
#> GSM1045280 GSM1045281 GSM1045283 GSM1045284 GSM1045287 GSM1045294 GSM1045296 
#>          1          1          1          1          1          1          1 
#> GSM1045298 GSM1045299 GSM1045300 GSM1045303 GSM1045305 GSM1045306 GSM1045309 
#>          1          1          1          1          1          1          1 
#> 
#> $clustering_all_levels$Level_5
#> GSM1045191 GSM1045195 GSM1045196 GSM1045197 GSM1045198 GSM1045199 GSM1045202 
#>          1          1          1          1          1          1          1 
#> GSM1045204 GSM1045205 GSM1045207 GSM1045208 GSM1045210 GSM1045215 GSM1045216 
#>          1          1          1          1          1          1          1 
#> GSM1045219 GSM1045220 GSM1045222 GSM1045223 GSM1045226 GSM1045228 GSM1045230 
#>          1          1          1          1          1          1          1 
#> GSM1045231 GSM1045234 GSM1045235 GSM1045236 GSM1045238 GSM1045245 GSM1045246 
#>          1          1          1          1          1          1          1 
#> GSM1045247 GSM1045249 GSM1045251 GSM1045253 GSM1045254 GSM1045256 GSM1045258 
#>          1          1          1          1          1          1          1 
#> GSM1045260 GSM1045261 GSM1045262 GSM1045263 GSM1045265 GSM1045267 GSM1045268 
#>          1          1          1          1          1          1          1 
#> GSM1045271 GSM1045272 GSM1045273 GSM1045275 GSM1045277 GSM1045279 GSM1045280 
#>          1          1          1          1          1          1          1 
#> GSM1045281 GSM1045283 GSM1045284 GSM1045286 GSM1045287 GSM1045289 GSM1045290 
#>          1          1          1          1          1          1          1 
#> GSM1045291 GSM1045292 GSM1045295 GSM1045296 GSM1045297 GSM1045299 GSM1045300 
#>          1          1          1          1          1          1          1 
#> GSM1045303 GSM1045304 GSM1045307 GSM1045308 GSM1045309 GSM1045310 GSM1045311 
#>          1          1          1          1          1          1          1 
#> 
#> 
#> $node_samples
#> $node_samples$Node_1
#> [1] "GSM1045192" "GSM1045193" "GSM1045217" "GSM1045221" "GSM1045288"
#> [6] "GSM1045302"
#> 
#> $node_samples$Node_2
#> [1] "GSM1045192" "GSM1045194" "GSM1045211" "GSM1045221" "GSM1045225"
#> [6] "GSM1045243" "GSM1045250" "GSM1045282" "GSM1045288"
#> 
#> $node_samples$Node_3
#>  [1] "GSM1045203" "GSM1045206" "GSM1045209" "GSM1045211" "GSM1045212"
#>  [6] "GSM1045213" "GSM1045214" "GSM1045224" "GSM1045225" "GSM1045227"
#> [11] "GSM1045229" "GSM1045237" "GSM1045241" "GSM1045242" "GSM1045243"
#> [16] "GSM1045244" "GSM1045248" "GSM1045250" "GSM1045255" "GSM1045264"
#> [21] "GSM1045266" "GSM1045274" "GSM1045276" "GSM1045278" "GSM1045282"
#> [26] "GSM1045285" "GSM1045293" "GSM1045294" "GSM1045301" "GSM1045305"
#> 
#> $node_samples$Node_4
#>  [1] "GSM1045191" "GSM1045195" "GSM1045196" "GSM1045197" "GSM1045198"
#>  [6] "GSM1045199" "GSM1045200" "GSM1045201" "GSM1045203" "GSM1045204"
#> [11] "GSM1045205" "GSM1045206" "GSM1045207" "GSM1045209" "GSM1045210"
#> [16] "GSM1045212" "GSM1045214" "GSM1045216" "GSM1045218" "GSM1045219"
#> [21] "GSM1045222" "GSM1045224" "GSM1045227" "GSM1045228" "GSM1045229"
#> [26] "GSM1045231" "GSM1045232" "GSM1045233" "GSM1045237" "GSM1045238"
#> [31] "GSM1045239" "GSM1045240" "GSM1045241" "GSM1045244" "GSM1045245"
#> [36] "GSM1045246" "GSM1045248" "GSM1045252" "GSM1045254" "GSM1045255"
#> [41] "GSM1045257" "GSM1045258" "GSM1045259" "GSM1045260" "GSM1045261"
#> [46] "GSM1045263" "GSM1045264" "GSM1045266" "GSM1045267" "GSM1045269"
#> [51] "GSM1045270" "GSM1045271" "GSM1045274" "GSM1045275" "GSM1045276"
#> [56] "GSM1045278" "GSM1045280" "GSM1045281" "GSM1045283" "GSM1045284"
#> [61] "GSM1045287" "GSM1045294" "GSM1045296" "GSM1045298" "GSM1045299"
#> [66] "GSM1045300" "GSM1045303" "GSM1045305" "GSM1045306" "GSM1045309"
#> 
#> $node_samples$Node_5
#>  [1] "GSM1045191" "GSM1045195" "GSM1045196" "GSM1045197" "GSM1045198"
#>  [6] "GSM1045199" "GSM1045202" "GSM1045204" "GSM1045205" "GSM1045207"
#> [11] "GSM1045208" "GSM1045210" "GSM1045215" "GSM1045216" "GSM1045219"
#> [16] "GSM1045220" "GSM1045222" "GSM1045223" "GSM1045226" "GSM1045228"
#> [21] "GSM1045230" "GSM1045231" "GSM1045234" "GSM1045235" "GSM1045236"
#> [26] "GSM1045238" "GSM1045245" "GSM1045246" "GSM1045247" "GSM1045249"
#> [31] "GSM1045251" "GSM1045253" "GSM1045254" "GSM1045256" "GSM1045258"
#> [36] "GSM1045260" "GSM1045261" "GSM1045262" "GSM1045263" "GSM1045265"
#> [41] "GSM1045267" "GSM1045268" "GSM1045271" "GSM1045272" "GSM1045273"
#> [46] "GSM1045275" "GSM1045277" "GSM1045279" "GSM1045280" "GSM1045281"
#> [51] "GSM1045283" "GSM1045284" "GSM1045286" "GSM1045287" "GSM1045289"
#> [56] "GSM1045290" "GSM1045291" "GSM1045292" "GSM1045295" "GSM1045296"
#> [61] "GSM1045297" "GSM1045299" "GSM1045300" "GSM1045303" "GSM1045304"
#> [66] "GSM1045307" "GSM1045308" "GSM1045309" "GSM1045310" "GSM1045311"
#> 
#> 
#> $node_sizes
#> Node_1 Node_2 Node_3 Node_4 Node_5 
#>      6      9     30     70     70 
#> 
#> $node_average_filt
#> $node_average_filt$Node_1
#> [1] 624.6244
#> 
#> $node_average_filt$Node_2
#> [1] 643.6715
#> 
#> $node_average_filt$Node_3
#> [1] 663.1964
#> 
#> $node_average_filt$Node_4
#> [1] 676.6767
#> 
#> $node_average_filt$Node_5
#> [1] 687.8436
#> 
#> 
#> $adj_matrix
#>        Node_1 Node_2 Node_3 Node_4 Node_5
#> Node_1      1      1      0      0      0
#> Node_2      0      1      1      0      0
#> Node_3      0      0      1      1      0
#> Node_4      0      0      0      1      1
#> Node_5      0      0      0      0      1
#> 
#> $n_sizes
#> [1] 5
#> 
#> $average_nodes
#> [1] 37
#> 
#> $standard_desviation_nodes
#> [1] 31.5119
#> 
#> $number_connections
#> [1] 4
#> 
#> $proportion_connections
#> [1] 0.4
#> 
#> $number_ramifications
#> [1] 0
#> 
#> attr(,"class")
#> [1] "mapper_object"

Plot the mapper graph.

plot_mapper(mapper_object)

OPTION #2 (all process integrate in the same function):

It creates the GSSTDA object with full data set, internally pre-process using the DGSA technique, and the mapper information.

GSSTDA_obj <- GSSTDA(full_data = full_data, survival_time = survival_time, 
                     survival_event = survival_event, case_tag = case_tag, 
                     gen_select_type = gen_select_type, 
                     percent_gen_select = percent_gen_select, 
                     num_intervals = num_intervals, 
                     percent_overlap = percent_overlap, 
                     distance_type = distance_type, 
                     clustering_type = clustering_type, 
                     linkage_type = linkage_type)
#> 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)
#> Choose one of the following optimal cluster number method: standard/silhouette:
#> The optimal clustering mode is 'silhouette' by default
#> 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
#> [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
#> 
#> BLOCK III: The mapper process is started
#> 
#> BLOCK III: The mapper process is finished