... | ... |
@@ -11,34 +11,64 @@ |
11 | 11 |
#' @name StarBioTrek |
12 | 12 |
NULL |
13 | 13 |
|
14 |
-#' Pathway data from KEGG |
|
14 |
+ |
|
15 |
+#' pathway data list |
|
15 | 16 |
#' @docType data |
16 | 17 |
#' @keywords internal |
17 | 18 |
#' @name path |
18 |
-#' @format A data frame with rows and variables |
|
19 |
+#' @format A list of dataframe |
|
19 | 20 |
NULL |
20 | 21 |
|
21 |
-#' network data |
|
22 |
+#' pathway data list |
|
22 | 23 |
#' @docType data |
23 | 24 |
#' @keywords internal |
24 |
-#' @name netw |
|
25 |
+#' @name Data_CANCER_normUQ_fil |
|
26 |
+#' @format A dataframe with gene expression profiles |
|
27 |
+NULL |
|
28 |
+ |
|
29 |
+ |
|
30 |
+#' pathway data |
|
31 |
+#' @docType data |
|
32 |
+#' @keywords internal |
|
33 |
+#' @name pathway |
|
25 | 34 |
#' @format A data frame with rows and variables |
26 | 35 |
NULL |
27 | 36 |
|
37 |
+#' network data for IPPI fucntion |
|
38 |
+#' @docType data |
|
39 |
+#' @keywords internal |
|
40 |
+#' @name netw_IPPI |
|
41 |
+#' @format A list |
|
42 |
+NULL |
|
43 |
+ |
|
28 | 44 |
|
45 |
+#' network data |
|
46 |
+#' @docType data |
|
47 |
+#' @keywords internal |
|
48 |
+#' @name pathway_matrix |
|
49 |
+#' @format A data frame with rows and variables |
|
50 |
+NULL |
|
29 | 51 |
|
52 |
+#' network data |
|
53 |
+#' @docType data |
|
54 |
+#' @keywords internal |
|
55 |
+#' @name netw |
|
56 |
+#' @format A data frame with rows and variables |
|
57 |
+NULL |
|
30 | 58 |
|
31 |
-#' TCGA data |
|
59 |
+#' All pathways data from KEGG |
|
32 | 60 |
#' @docType data |
33 | 61 |
#' @keywords internal |
34 |
-#' @name Data_CANCER_normUQ_filt |
|
35 |
-#' @format A data frame with rows and variables |
|
62 |
+#' @name path_KEGG |
|
63 |
+#' @format A list of pathways with the involved genes |
|
36 | 64 |
NULL |
37 | 65 |
|
66 |
+ |
|
67 |
+ |
|
38 | 68 |
#' Score Matrix of pairwise pathway using euclidean distance |
39 | 69 |
#' @docType data |
40 | 70 |
#' @keywords internal |
41 |
-#' @name score_euc_dist |
|
71 |
+#' @name score_euc_dista |
|
42 | 72 |
#' @format A data frame with rows and variables |
43 | 73 |
NULL |
44 | 74 |
|
... | ... |
@@ -55,10 +85,3 @@ NULL |
55 | 85 |
#' @name tumo |
56 | 86 |
#' @format A data frame with rows and variables |
57 | 87 |
NULL |
58 |
- |
|
59 |
-#' A matrix of gene expression for pathways given by the user. |
|
60 |
-#' @docType data |
|
61 |
-#' @keywords internal |
|
62 |
-#' @name list_path_plot |
|
63 |
-#' @format A data frame with rows and variables |
|
64 |
-NULL |
|
65 | 88 |
\ No newline at end of file |
... | ... |
@@ -1,225 +1,133 @@ |
1 |
-#' @title Get human KEGG pathway data. |
|
2 |
-#' @description getKEGGdata creates a data frame with human KEGG pathway. Columns are the pathways and rows the genes inside those pathway |
|
3 |
-#' @param KEGG_path variable |
|
1 |
+#' @title Get general information inside pathways. |
|
2 |
+#' @description GetData creates a list with genes inside the pathways. |
|
3 |
+#' @param species variable. The user can select the species of interest from SELECT_path_species(path_spec) |
|
4 |
+#' @param pathwaydb variable. The user can select the pathway database of interest from SELECT_path_graphite(path_spec) |
|
4 | 5 |
#' @export |
5 |
-#' @importFrom KEGGREST keggList keggGet |
|
6 |
-#' @importFrom org.Hs.eg.db org.Hs.egSYMBOL2EG |
|
7 |
-#' @importFrom AnnotationDbi mappedkeys as.list |
|
8 |
-#' @return dataframe with human pathway data |
|
6 |
+#' @importFrom graphite pathways pathwayTitle |
|
7 |
+#' @return a list of pathways |
|
9 | 8 |
#' @examples |
10 |
-#' path<-getKEGGdata(KEGG_path="Transcript") |
|
11 |
-getKEGGdata<-function(KEGG_path){ |
|
12 |
-if (KEGG_path=="Carb_met") { |
|
13 |
- mer<-select_path_carb(Carbohydrate) |
|
14 |
- c<-proc_path(mer) |
|
15 |
- a<-c[[2]] |
|
9 |
+#' \dontrun{ |
|
10 |
+#' species="hsapiens" |
|
11 |
+#' pathwaydb="pharmgkb" |
|
12 |
+#' path<-GetData(species,pathwaydb)} |
|
13 |
+GetData<-function(species,pathwaydb){ |
|
14 |
+ humanpath <- pathways(species, pathwaydb) |
|
15 |
+ humanReactome<-humanpath |
|
16 |
+ le<-list() |
|
17 |
+ for (j in 1:length(humanReactome)){ |
|
18 |
+ e<-humanReactome[[j]] |
|
19 |
+ print(paste0("Querying............. ",pathwayTitle(e)," ", j, " of ",length(humanReactome)," pathways")) |
|
20 |
+ le[[j]]<-e |
|
21 |
+ } |
|
22 |
+ names(le)<- names(humanReactome) |
|
23 |
+ return(le) |
|
16 | 24 |
} |
17 |
- if (KEGG_path=="Ener_met") { |
|
18 |
- mer<-select_path_en(Energy) |
|
19 |
- c<-proc_path(mer) |
|
20 |
- a<-c[[2]] |
|
21 |
- } |
|
22 |
- if (KEGG_path=="Lip_met") { |
|
23 |
- mer<-select_path_lip(Lipid) |
|
24 |
- c<-proc_path(mer) |
|
25 |
- a<-c[[2]] |
|
26 |
- } |
|
27 |
- if (KEGG_path=="Amn_met") { |
|
28 |
- mer<-select_path_amn(Aminoacid) |
|
29 |
- c<-proc_path(mer) |
|
30 |
- a<-c[[2]] |
|
31 |
- } |
|
32 |
- if (KEGG_path=="Gly_bio_met") { |
|
33 |
- mer<-select_path_gly(Glybio_met) |
|
34 |
- c<-proc_path(mer) |
|
35 |
- a<-c[[2]] |
|
36 |
- } |
|
37 |
- if (KEGG_path=="Cof_vit_met") { |
|
38 |
- mer<-select_path_cofa(Cofa_vita_met) |
|
39 |
- c<-proc_path(mer) |
|
40 |
- a<-c[[2]] |
|
41 |
- } |
|
42 |
- if (KEGG_path=="Transcript") { |
|
43 |
- mer<-select_path_transc(Transcription) |
|
44 |
- c<-proc_path(mer) |
|
45 |
- a<-c[[2]] |
|
46 |
- } |
|
47 |
- if (KEGG_path=="Transl") { |
|
48 |
- mer<-select_path_transl(Translation) |
|
49 |
- c<-proc_path(mer) |
|
50 |
- a<-c[[2]] |
|
51 |
- } |
|
52 |
- if (KEGG_path=="Fold_degr") { |
|
53 |
- mer<-select_path_fold(Folding_sorting_and_degradation) |
|
54 |
- c<-proc_path(mer) |
|
55 |
- a<-c[[2]] |
|
56 |
- } |
|
57 |
- if (KEGG_path=="Repl_repair") { |
|
58 |
- mer<-select_path_repl(Replication_and_repair) |
|
59 |
- c<-proc_path(mer) |
|
60 |
- a<-c[[2]] |
|
61 |
- } |
|
62 |
- if (KEGG_path=="sign_transd") { |
|
63 |
- mer<-select_path_sign(Signal_transduction) |
|
64 |
- c<-proc_path(mer) |
|
65 |
- a<-c[[2]] |
|
66 |
- } |
|
67 |
- if (KEGG_path=="sign_mol_int") { |
|
68 |
- mer<-select_path_sign_mol(Signaling_molecules_and_interaction) |
|
69 |
- c<-proc_path(mer) |
|
70 |
- a<-c[[2]] |
|
71 |
- } |
|
72 |
- if (KEGG_path=="Transp_cat") { |
|
73 |
- mer<-select_path_transp_ca(Transport_and_catabolism) |
|
74 |
- c<-proc_path(mer) |
|
75 |
- a<-c[[2]] |
|
76 |
- } |
|
77 |
- if (KEGG_path=="cell_grow_d") { |
|
78 |
- mer<-select_path_cell_grow(Cell_growth_and_death) |
|
79 |
- c<-proc_path(mer) |
|
80 |
- a<-c[[2]] |
|
81 |
- } |
|
82 |
- if (KEGG_path=="cell_comm") { |
|
83 |
- mer<-select_path_cell_comm(Cellular_community) |
|
84 |
- c<-proc_path(mer) |
|
85 |
- a<-c[[2]] |
|
86 |
- } |
|
87 |
- if (KEGG_path=="imm_syst") { |
|
88 |
- mer<-select_path_imm_syst(Immune_system) |
|
89 |
- c<-proc_path(mer) |
|
90 |
- a<-c[[2]] |
|
91 |
- } |
|
92 |
- if (KEGG_path=="end_syst") { |
|
93 |
- mer<-select_path_end_syst(Endocrine_system) |
|
94 |
- c<-proc_path(mer) |
|
95 |
- a<-c[[2]] |
|
96 |
- } |
|
97 |
- if (KEGG_path=="circ_syst") { |
|
98 |
- mer<-select_path_circ_syst(Circulatory_system) |
|
99 |
- c<-proc_path(mer) |
|
100 |
- a<-c[[2]] |
|
101 |
- } |
|
102 |
- if (KEGG_path=="dig_syst") { |
|
103 |
- mer<-select_path_dig_syst(Digestive_system) |
|
104 |
- c<-proc_path(mer) |
|
105 |
- a<-c[[2]] |
|
106 |
- } |
|
107 |
- if (KEGG_path=="exc_syst") { |
|
108 |
- mer<-select_path_exc_syst(Excretory_system) |
|
109 |
- c<-proc_path(mer) |
|
110 |
- a<-c[[2]] |
|
111 |
- } |
|
112 |
- if (KEGG_path=="nerv_syst") { |
|
113 |
- mer<-select_path_ner_syst(Nervous_system) |
|
114 |
- c<-proc_path(mer) |
|
115 |
- a<-c[[2]] |
|
116 |
- } |
|
117 |
- if (KEGG_path=="sens_syst") { |
|
118 |
- mer<-select_path_sens_syst(Sensory_system) |
|
119 |
- c<-proc_path(mer) |
|
120 |
- a<-c[[2]] |
|
121 |
- } |
|
122 |
-if (KEGG_path=="KEGG_path") { |
|
123 |
- pathways.list <- keggList("pathway", "hsa")## returns the list of human pathways |
|
124 |
-pathway.codes <- sub("path:", "", names(pathways.list)) |
|
125 |
-pathways.list<-list(pathways.list) |
|
126 |
-pathways.list<-pathways.list[lapply(pathways.list,length)!=0] |
|
127 |
-list_pathkeg<-do.call("cbind", pathways.list) |
|
128 |
-c<-list(pathway.codes,list_pathkeg) |
|
129 |
-a<-c[[2]] |
|
130 | 25 |
|
131 |
-} |
|
132 |
-pathway.codes<-c[[1]] |
|
133 |
-genes.by.pathway <- sapply(pathway.codes, |
|
134 |
- function(pwid){ |
|
135 |
- pw <- keggGet(pwid) |
|
136 |
- pw[[1]]$GENE[c(TRUE, FALSE)] |
|
137 |
- }) |
|
138 |
-x <- org.Hs.egSYMBOL2EG |
|
139 |
-mapped_genes <- mappedkeys(x) |
|
140 |
-xx <- as.list(x[mapped_genes]) |
|
141 |
-top3 <- matrix(0, length(xx), length(genes.by.pathway)) |
|
142 |
-rownames(top3) <- names(xx) |
|
143 |
-colnames(top3)<- names(genes.by.pathway) |
|
144 |
- |
|
145 |
- |
|
146 |
- |
|
147 |
-for (j in 1:length(xx)){ |
|
148 |
- for (k in 1:length(genes.by.pathway)){ |
|
149 |
- if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){ |
|
150 |
- |
|
151 |
- |
|
152 |
- |
|
153 |
- top3[j,k]<-names(xx[j]) |
|
154 |
- } |
|
155 |
- } |
|
156 |
-} |
|
157 | 26 |
|
27 |
+#' @title Get genes inside pathways. |
|
28 |
+#' @description GetPathData creates a list of genes inside the pathways. |
|
29 |
+#' @param path_ALL variable. The user can select the variable as obtained by GetData function |
|
30 |
+#' @export |
|
31 |
+#' @importFrom graphite nodes pathwayTitle |
|
32 |
+#' @return a list of pathways |
|
33 |
+#' @examples |
|
34 |
+#' pathway_ALL_GENE<-GetPathData(path_ALL=path[1:3]) |
|
35 |
+GetPathData<-function(path_ALL){ |
|
36 |
+ le<-list() |
|
37 |
+ for (j in 1:length(path_ALL)){ |
|
38 |
+ e<-path_ALL[[j]] |
|
39 |
+ genes<-nodes(e,which = "proteins") |
|
40 |
+ print(paste0("Downloading............. ",pathwayTitle(e)," ", j, " of ",length(path_ALL)," pathways")) |
|
41 |
+ le[[j]]<-genes |
|
42 |
+ } |
|
43 |
+ names(le)<- names(path_ALL) |
|
44 |
+ return(le) |
|
45 |
+} |
|
158 | 46 |
|
159 | 47 |
|
160 |
-for (j in 1:length(xx)){ |
|
161 |
- for (k in 1:length(genes.by.pathway)){ |
|
162 |
- if (length(intersect(xx[[j]],genes.by.pathway[[k]])!=0)){ |
|
163 |
- |
|
164 | 48 |
|
165 | 49 |
|
166 |
- # top3[j,k]<-names(xx[j]) |
|
167 |
- } |
|
168 |
- } |
|
169 |
-} |
|
170 |
-top3[top3 == 0] <- " " |
|
171 |
-#a<-data.frame(pathways.list) |
|
172 |
-#i <- sapply(a, is.factor) |
|
173 |
-#a[i] <- lapply(a[i], as.character) |
|
174 |
-rownames(a)<-sub("path:","",rownames(a)) |
|
175 |
-PROVA<-top3 |
|
176 |
-for( i in 1:ncol(PROVA)) { |
|
177 |
- if (colnames(PROVA)[i]==rownames(a)[i]){ |
|
178 |
- colnames(PROVA)[i]<-a[i] |
|
179 |
-} |
|
180 |
-} |
|
181 |
-return(PROVA) |
|
50 |
+#' @title Get interacting genes inside pathways. |
|
51 |
+#' @description GetPathNet creates a list of genes inside the pathways. |
|
52 |
+#' @param path_ALL variable. The user can select the variable as obtained by GetData function |
|
53 |
+#' @export |
|
54 |
+#' @importFrom graphite edges pathwayTitle |
|
55 |
+#' @return a list of pathways |
|
56 |
+#' @examples |
|
57 |
+#' pathway_net<-GetPathNet(path_ALL=path[1:3]) |
|
58 |
+GetPathNet<-function(path_ALL){ |
|
59 |
+ le<-list() |
|
60 |
+ for (j in 1:length(path_ALL)){ |
|
61 |
+ e<-path_ALL[[j]] |
|
62 |
+ genes<-edges(e,which = "proteins") |
|
63 |
+ print(paste0("Downloading............. ",pathwayTitle(e)," ", j, " of ",length(path_ALL)," pathways")) |
|
64 |
+ le[[j]]<-genes |
|
65 |
+ } |
|
66 |
+ names(le)<- names(path_ALL) |
|
67 |
+ return(le) |
|
182 | 68 |
} |
183 | 69 |
|
184 | 70 |
|
185 |
-#' @title Get network data. |
|
71 |
+ |
|
72 |
+#' @title Get interacting genes inside pathways. |
|
73 |
+#' @description GetPathNet creates a list of genes inside the pathways. |
|
74 |
+#' @param path_ALL variable. The user can select the variable as obtained by GetData function |
|
75 |
+#' @export |
|
76 |
+#' @importFrom graphite nodes pathwayTitle convertIdentifiers |
|
77 |
+#' @return a list of pathways |
|
78 |
+#' @examples |
|
79 |
+#' pathway<-ConvertedIDgenes(path_ALL=path[1:3]) |
|
80 |
+ConvertedIDgenes<-function(path_ALL){ |
|
81 |
+ le<-list() |
|
82 |
+ for (j in 1:length(path_ALL)){ |
|
83 |
+ e<-path_ALL[[j]] |
|
84 |
+ s1<-convertIdentifiers(e, "symbol") |
|
85 |
+ genes<-nodes(s1,which = "proteins") |
|
86 |
+ er <- sapply(strsplit(genes, split=':', fixed=TRUE), function(x) (x[2])) |
|
87 |
+ print(paste0("Mapping Uniprot ID to Gene Symbol, using convertIdentifiers of graphite package.......... ",pathwayTitle(e)," ", j, " of ",length(path_ALL)," pathways")) |
|
88 |
+ #attr(mm, "names")<-NULL |
|
89 |
+ le[[j]]<-er |
|
90 |
+ } |
|
91 |
+ names(le)<- names(path_ALL) |
|
92 |
+ return(le) |
|
93 |
+} |
|
94 |
+ |
|
95 |
+ |
|
96 |
+ |
|
97 |
+ |
|
98 |
+#' @title Get network data from GeneMania. |
|
186 | 99 |
#' @description getNETdata creates a data frame with network data. |
187 | 100 |
#' Network category can be filtered among: physical interactions, co-localization, genetic interactions and shared protein domain. |
188 | 101 |
#' @param network variable. The user can use the following parameters |
189 | 102 |
#' based on the network types to be used. PHint for Physical_interactions, |
190 | 103 |
#' COloc for Co-localization, GENint for Genetic_interactions and |
191 | 104 |
#' SHpd for Shared_protein_domains |
192 |
-#' @param organism organism==NULL default value is homo sapiens |
|
105 |
+#' @param organismID organism==NULL default value is homo sapiens. |
|
193 | 106 |
#' @export |
194 |
-#' @importFrom SpidermiR SpidermiRquery_species SpidermiRquery_spec_networks SpidermiRdownload_net SpidermiRprepare_NET |
|
195 |
-#' @return dataframe with gene-gene (or protein-protein interactions) |
|
107 |
+#' @importFrom SpidermiR SpidermiRquery_spec_networks SpidermiRdownload_net SpidermiRprepare_NET |
|
108 |
+#' @return list with gene-gene (or protein-protein interactions) |
|
196 | 109 |
#' @examples |
197 |
-#' organism="Saccharomyces_cerevisiae" |
|
198 |
-#' netw<-getNETdata(network="SHpd",organism) |
|
199 |
-getNETdata<-function(network,organism=NULL){ |
|
200 |
- org_shar_pro<-SpidermiRquery_species(species) |
|
201 |
- if (is.null(organism)) { |
|
202 |
- net_shar_prot<-SpidermiRquery_spec_networks(organismID = org_shar_pro[6,],network) |
|
203 |
- out_net_shar_pro<-SpidermiRdownload_net(net_shar_prot) |
|
204 |
- geneSymb_net_shar_pro<-SpidermiRprepare_NET(organismID = org_shar_pro[6,],data = out_net_shar_pro) |
|
205 |
- } |
|
206 |
- if( !is.null(organism) ){ |
|
207 |
- net_shar_prot<-SpidermiRquery_spec_networks(organismID = org_shar_pro[9,],network) |
|
208 |
- out_net_shar_pro<-SpidermiRdownload_net(net_shar_prot) |
|
209 |
- geneSymb_net_shar_pro<-SpidermiRprepare_NET(organismID = org_shar_pro[9,],data = out_net_shar_pro) |
|
210 |
-} |
|
211 |
- ds_shar_pro<-do.call("rbind", geneSymb_net_shar_pro) |
|
212 |
- data_shar_pro<-as.data.frame(ds_shar_pro[!duplicated(ds_shar_pro), ]) |
|
213 |
- sdc_shar_pro<-unlist(data_shar_pro$gene_symbolA,data_shar_pro$gene_symbolB) |
|
214 |
- m_shar_pro<-c(data_shar_pro$gene_symbolA) |
|
215 |
- m2_shar_pro<-c(data_shar_pro$gene_symbolB) |
|
216 |
- ss_shar_pro<-cbind(m_shar_pro,m2_shar_pro) |
|
217 |
- data_pr_shar_pro<-as.data.frame(ss_shar_pro[!duplicated(ss_shar_pro), ]) |
|
218 |
- colnames(data_pr_shar_pro) <- c("m_shar_pro", "m2_shar_pro") |
|
219 |
-return(data_pr_shar_pro) |
|
110 |
+#' \dontrun{ |
|
111 |
+#' organismID="Saccharomyces_cerevisiae" |
|
112 |
+#' netw<-getNETdata(network="SHpd",organismID)} |
|
113 |
+getNETdata<-function(network,organismID=NULL){ |
|
114 |
+ if( is.null(organismID) ){ |
|
115 |
+ prr<-SpidermiRprepare_NET(organismID = 'Homo_sapiens', |
|
116 |
+ data = SpidermiRdownload_net(data = SpidermiRquery_spec_networks(organismID = 'Homo_sapiens',network |
|
117 |
+ ))) |
|
118 |
+ } |
|
119 |
+ if( !is.null(organismID) ){ |
|
120 |
+ prr<-SpidermiRprepare_NET(organismID, |
|
121 |
+ data = SpidermiRdownload_net(data = SpidermiRquery_spec_networks(organismID , |
|
122 |
+ network))) |
|
123 |
+ } |
|
124 |
+ return(prr) |
|
220 | 125 |
} |
221 | 126 |
|
222 | 127 |
|
223 | 128 |
|
224 | 129 |
|
225 | 130 |
|
131 |
+ |
|
132 |
+ |
|
133 |
+ |
... | ... |
@@ -1,405 +1,48 @@ |
1 |
- |
|
2 |
- |
|
3 |
- |
|
4 |
-select_path_carb<-function(Carbohydrate){ |
|
5 |
-species<-c("- Homo sapiens (human)") |
|
6 |
-a<-paste("Glycolysis / Gluconeogenesis", species) |
|
7 |
-b<-paste("Citrate cycle (TCA cycle)", species) |
|
8 |
-c<-paste("Pentose phosphate pathway", species) |
|
9 |
-d<-paste("Pentose and glucuronate interconversions", species) |
|
10 |
-e<-paste("Fructose and mannose metabolism", species) |
|
11 |
-f<-paste("Galactose metabolism", species) |
|
12 |
-g<-paste("Ascorbate and aldarate metabolism", species) |
|
13 |
-h<-paste("Starch and sucrose metabolism", species) |
|
14 |
-i<-paste("Amino sugar and nucleotide sugar metabolism", species) |
|
15 |
-l<-paste("Pyruvate metabolism", species) |
|
16 |
-m<-paste("Glyoxylate and dicarboxylate metabolism", species) |
|
17 |
-n<-paste("Propanoate metabolism", species) |
|
18 |
-o<-paste("Butanoate metabolism", species) |
|
19 |
-p<-paste("C5-Branched dibasic acid metabolism", species) |
|
20 |
-q<-paste("Inositol phosphate metabolism", species) |
|
21 |
-r<-paste("Enzymes", species) |
|
22 |
-s<-paste("Compounds with biological roles",species) |
|
23 |
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s) |
|
24 |
-return(mer) |
|
25 |
-} |
|
26 |
- |
|
27 |
-select_path_en<-function(Energy){ |
|
28 |
- species<-c("- Homo sapiens (human)") |
|
29 |
- r<-paste("Oxidative phosphorylation", species) |
|
30 |
- s<-paste("Photosynthesis", species) |
|
31 |
- t<-paste("Photosynthesis - antenna proteins", species) |
|
32 |
- v<-paste("Carbon fixation in photosynthetic organisms", species) |
|
33 |
- u<-paste("Carbon fixation pathways in prokaryotes", species) |
|
34 |
- z<-paste("Methane metabolism", species) |
|
35 |
- aa<-paste("Nitrogen metabolism", species) |
|
36 |
- ab<-paste("Sulfur metabolism", species) |
|
37 |
- mer<-c(r,s,t,v,u,z,aa,ab) |
|
38 |
- return(mer) |
|
39 |
-} |
|
40 |
- |
|
41 |
- |
|
42 |
-select_path_lip<-function(Lipid){ |
|
43 |
- species<-c("- Homo sapiens (human)") |
|
44 |
-ac<-paste("Fatty acid biosynthesis", species) |
|
45 |
-ad<-paste("Fatty acid elongation", species) |
|
46 |
-ae<-paste("Fatty acid degradation", species) |
|
47 |
-af<-paste("Synthesis and degradation of ketone bodies", species) |
|
48 |
-ag<-paste("Cutin, suberine and wax biosynthesis", species) |
|
49 |
-ah<-paste("Steroid biosynthesis", species) |
|
50 |
-ai<-paste("Primary bile acid biosynthesis", species) |
|
51 |
-al<-paste("Secondary bile acid biosynthesis", species) |
|
52 |
-am<-paste("Steroid hormone biosynthesis", species) |
|
53 |
-an<-paste("Glycerolipid metabolism", species) |
|
54 |
-ao<-paste("Glycerophospholipid metabolism", species) |
|
55 |
-ap<-paste("Ether lipid metabolism", species) |
|
56 |
-aq<-paste("Sphingolipid metabolism", species) |
|
57 |
-ar<-paste("Arachidonic acid metabolism", species) |
|
58 |
-as<-paste("Linoleic acid metabolism", species) |
|
59 |
-at<-paste("alpha-Linolenic acid metabolism", species) |
|
60 |
-av<-paste("Biosynthesis of unsaturated fatty acids", species) |
|
61 |
- |
|
62 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as,at,av) |
|
63 |
-return(mer) |
|
64 |
-} |
|
65 |
- |
|
66 |
- |
|
67 |
- |
|
68 |
- |
|
69 |
-select_path_amn<-function(Aminoacid){ |
|
70 |
- species<-c("- Homo sapiens (human)") |
|
71 |
-ac<-paste("Alanine, aspartate and glutamate metabolism", species) |
|
72 |
-ad<-paste("Glycine, serine and threonine metabolism", species) |
|
73 |
-ae<-paste("Cysteine and methionine metabolism", species) |
|
74 |
-af<-paste("Valine, leucine and isoleucine degradation", species) |
|
75 |
-ag<-paste("Valine, leucine and isoleucine biosynthesis", species) |
|
76 |
-ah<-paste("Lysine biosynthesis", species) |
|
77 |
-ai<-paste("Lysine degradation", species) |
|
78 |
-al<-paste("Arginine biosynthesis", species) |
|
79 |
-am<-paste("Arginine and proline metabolism", species) |
|
80 |
-an<-paste("Histidine metabolism", species) |
|
81 |
-ao<-paste("Tyrosine metabolism", species) |
|
82 |
-ap<-paste("Phenylalanine metabolism", species) |
|
83 |
-aq<-paste("Tryptophan metabolism", species) |
|
84 |
-ar<-paste("Phenylalanine, tyrosine and tryptophan biosynthesis", species) |
|
85 |
-as<-paste("beta-Alanine metabolism", species) |
|
86 |
-at<-paste("Taurine and hypotaurine metabolism", species) |
|
87 |
-av<-paste("Phosphonate and phosphinate metabolism", species) |
|
88 |
-au<-paste("Selenocompound metabolism", species) |
|
89 |
-az<-paste("Cyanoamino acid metabolism", species) |
|
90 |
-a<-paste("D-Glutamine and D-glutamate metabolism", species) |
|
91 |
-b<-paste("D-Arginine and D-ornithine metabolism", species) |
|
92 |
-c<-paste("D-Alanine metabolism", species) |
|
93 |
-d<-paste("Glutathione metabolism", species) |
|
94 |
- |
|
95 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as,at,av,au,az,a,b,c,d) |
|
96 |
-return(mer) |
|
97 |
-} |
|
98 |
- |
|
99 |
-select_path_gly<-function(Glybio_met){ |
|
100 |
- species<-c("- Homo sapiens (human)") |
|
101 |
-ac<-paste("N-Glycan biosynthesis", species) |
|
102 |
-ad<-paste("Various types of N-glycan biosynthesis", species) |
|
103 |
-ae<-paste("Mucin type O-Glycan biosynthesis", species) |
|
104 |
-af<-paste("Other types of O-glycan biosynthesis", species) |
|
105 |
-ag<-paste("Glycosaminoglycan biosynthesis - CS/DS", species) |
|
106 |
-ah<-paste("Glycosaminoglycan biosynthesis - HS/Hep", species) |
|
107 |
-ai<-paste("Glycosaminoglycan biosynthesis - KS", species) |
|
108 |
-al<-paste("Glycosaminoglycan degradation", species) |
|
109 |
-am<-paste("Glycosylphosphatidylinositol(GPI)-anchor biosynthesis", species) |
|
110 |
-an<-paste("Glycosphingolipid biosynthesis - lacto and neolacto series", species) |
|
111 |
-ao<-paste("Glycosphingolipid biosynthesis - globo series", species) |
|
112 |
-ap<-paste("Glycosphingolipid biosynthesis - ganglio series", species) |
|
113 |
-aq<-paste("Lipopolysaccharide biosynthesis", species) |
|
114 |
-ar<-paste("Peptidoglycan biosynthesis", species) |
|
115 |
-as<-paste("Other glycan degradation", species) |
|
116 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap,aq,ar,as) |
|
117 |
-return(mer) |
|
118 |
-} |
|
119 |
- |
|
120 |
- |
|
121 |
- |
|
122 |
-select_path_cofa<-function(Cofa_vita_met){ |
|
123 |
- species<-c("- Homo sapiens (human)") |
|
124 |
-ac<-paste("Thiamine metabolism", species) |
|
125 |
-ad<-paste("Riboflavin metabolism", species) |
|
126 |
-ae<-paste("Vitamin B6 metabolism", species) |
|
127 |
-af<-paste("Nicotinate and nicotinamide metabolism", species) |
|
128 |
-ag<-paste("Pantothenate and CoA biosynthesis", species) |
|
129 |
-ah<-paste("Biotin metabolism", species) |
|
130 |
-ai<-paste("Lipoic acid metabolism", species) |
|
131 |
-al<-paste("Folate biosynthesis", species) |
|
132 |
-am<-paste("One carbon pool by folate", species) |
|
133 |
-an<-paste("Retinol metabolism", species) |
|
134 |
-ao<-paste("Porphyrin and chlorophyll metabolism", species) |
|
135 |
-ap<-paste("Ubiquinone and other terpenoid-quinone biosynthesis", species) |
|
136 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao,ap) |
|
137 |
-return(mer) |
|
138 |
-} |
|
139 |
- |
|
140 |
-select_path_transc<-function(Transcription){ |
|
141 |
- species<-c("- Homo sapiens (human)") |
|
142 |
-ac<-paste("RNA polymerase", species) |
|
143 |
-ad<-paste("Basal transcription factors", species) |
|
144 |
-ae<-paste("Spliceosome", species) |
|
145 |
-af<-paste("Transcription factors", species) |
|
146 |
-ag<-paste("Transcription machinery", species) |
|
147 |
-mer<-c(ac,ad,ae,af,ag) |
|
148 |
-return(mer) |
|
149 |
-} |
|
150 |
- |
|
151 |
- |
|
152 |
- |
|
153 |
-select_path_transl<-function(Translation){ |
|
154 |
- species<-c("- Homo sapiens (human)") |
|
155 |
-ac<-paste("Ribosome", species) |
|
156 |
-ad<-paste("Aminoacyl-tRNA biosynthesis", species) |
|
157 |
-ae<-paste("RNA transport", species) |
|
158 |
-af<-paste("mRNA surveillance pathway", species) |
|
159 |
-ag<-paste("Ribosome biogenesis in eukaryotes", species) |
|
160 |
-ah<-paste("Ribosomal proteins", species) |
|
161 |
-ai<-paste("Ribosome biogenesis", species) |
|
162 |
-al<-paste("Transfer RNA biogenesis", species) |
|
163 |
-am<-paste("Translation factors", species) |
|
164 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am) |
|
165 |
-return(mer) |
|
166 |
-} |
|
167 |
- |
|
168 |
-select_path_fold<-function(Folding_sorting_and_degradation){ |
|
169 |
- species<-c("- Homo sapiens (human)") |
|
170 |
-ac<-paste("Protein export", species) |
|
171 |
-ad<-paste("Protein processing in endoplasmic reticulum", species) |
|
172 |
-ae<-paste("SNARE interactions in vesicular transport", species) |
|
173 |
-af<-paste("Ubiquitin mediated proteolysis", species) |
|
174 |
-ag<-paste("Sulfur relay system", species) |
|
175 |
-ah<-paste("RNA degradation", species) |
|
176 |
-ai<-paste("Chaperones and folding catalysts", species) |
|
177 |
-al<-paste("SNAREs", species) |
|
178 |
-am<-paste("Ubiquitin system", species) |
|
179 |
-an<-paste("Proteasome", species) |
|
180 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an) |
|
181 |
-return(mer) |
|
182 |
-} |
|
183 |
- |
|
184 |
- |
|
185 |
- |
|
186 |
- |
|
187 |
-select_path_repl<-function(Replication_and_repair){ |
|
188 |
- species<-c("- Homo sapiens (human)") |
|
189 |
-ac<-paste("DNA replication", species) |
|
190 |
-ad<-paste("Base excision repair", species) |
|
191 |
-ae<-paste("Nucleotide excision repair", species) |
|
192 |
-af<-paste("Mismatch repair", species) |
|
193 |
-ag<-paste("Homologous recombination", species) |
|
194 |
-ah<-paste("Non-homologous end-joining", species) |
|
195 |
-ai<-paste("Fanconi anemia pathway", species) |
|
196 |
-al<-paste("DNA replication proteins", species) |
|
197 |
-am<-paste("Chromosome", species) |
|
198 |
-an<-paste("DNA repair and recombination", species) |
|
199 |
-ao<-paste("proteins", species) |
|
200 |
-mer<-c(ac,ad,ae,af,ag,ah,ai,al,am,an,ao) |
|
201 |
-return(mer) |
|
202 |
-} |
|
203 |
- |
|
204 |
- |
|
205 |
- |
|
206 |
-select_path_sign<-function(Signal_transduction){ |
|
207 |
- species<-c("- Homo sapiens (human)") |
|
208 |
-a<-paste("Ras signaling pathway", species) |
|
209 |
-b<-paste("Rap1 signaling pathway", species) |
|
210 |
-c<-paste("MAPK signaling pathway", species) |
|
211 |
-d<-paste("ErbB signaling pathway", species) |
|
212 |
-e<-paste("Wnt signaling pathway", species) |
|
213 |
-f<-paste("Notch signaling pathway", species) |
|
214 |
-g<-paste("Hedgehog signaling pathway", species) |
|
215 |
-h<-paste("TGF-beta signaling pathway", species) |
|
216 |
-i<-paste("Hippo signaling pathway", species) |
|
217 |
-l<-paste("VEGF signaling pathway", species) |
|
218 |
-m<-paste("Jak-STAT signaling pathway", species) |
|
219 |
-n<-paste("NF-kappa B signaling pathway", species) |
|
220 |
-o<-paste("TNF signaling pathway", species) |
|
221 |
-p<-paste("HIF-1 signaling pathway", species) |
|
222 |
-q<-paste("FoxO signaling pathway", species) |
|
223 |
-r<-paste("Calcium signaling pathway", species) |
|
224 |
-s<-paste("Phosphatidylinositol signaling system", species) |
|
225 |
-t<-paste("Phospholipase D signaling pathway", species) |
|
226 |
-v<-paste("Sphingolipid signaling pathway", species) |
|
227 |
-u<-paste("cAMP signaling pathway", species) |
|
228 |
-z<-paste("cGMP-PKG signaling pathway", species) |
|
229 |
-ab<-paste("PI3K-Akt signaling pathway", species) |
|
230 |
-ac<-paste("AMPK signaling pathway", species) |
|
231 |
-ad<-paste("mTOR signaling pathway", species) |
|
232 |
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s,t,v,u,z,ab,ac,ad) |
|
233 |
-return(mer) |
|
234 |
-} |
|
235 |
- |
|
236 |
- |
|
237 |
-select_path_sign_mol<-function(Signaling_molecules_and_interaction){ |
|
238 |
- species<-c("- Homo sapiens (human)") |
|
239 |
-a<-paste("Neuroactive ligand-receptor interaction", species) |
|
240 |
-b<-paste("Cytokine-cytokine receptor interaction", species) |
|
241 |
-c<-paste("ECM-receptor interaction", species) |
|
242 |
-d<-paste("Cell adhesion molecules (CAMs)", species) |
|
243 |
-mer<-c(a,b,c,d) |
|
244 |
-return(mer) |
|
245 |
-} |
|
246 |
- |
|
247 |
- |
|
248 |
-select_path_transp_ca<-function(Transport_and_catabolism){ |
|
249 |
- species<-c("- Homo sapiens (human)") |
|
250 |
-a<-paste("Endocytosis", species) |
|
251 |
-b<-paste("Phagosome", species) |
|
252 |
-c<-paste("Lysosome", species) |
|
253 |
-d<-paste("Peroxisome", species) |
|
254 |
-e<-paste("Regulation of autophagy", species) |
|
255 |
-mer<-c(a,b,c,d,e) |
|
256 |
-return(mer) |
|
257 |
-} |
|
258 |
- |
|
259 |
-select_path_cell_grow<-function(Cell_growth_and_death){ |
|
260 |
- species<-c("- Homo sapiens (human)") |
|
261 |
- a<-paste("Cell cycle", species) |
|
262 |
-b<-paste("Apoptosis", species) |
|
263 |
-c<-paste("p53 signaling pathway", species) |
|
264 |
-mer<-c(a,b,c) |
|
265 |
-return(mer) |
|
266 |
-} |
|
267 |
- |
|
268 |
- |
|
269 |
-select_path_cell_comm<-function(Cellular_community){ |
|
270 |
- species<-c("- Homo sapiens (human)") |
|
271 |
- a<-paste("Focal adhesion", species) |
|
272 |
-b<-paste("Adherens junction", species) |
|
273 |
-c<-paste("Tight junction", species) |
|
274 |
-d<-paste("Gap junction", species) |
|
275 |
-e<-paste("Signaling pathways regulating pluripotency of stem cells ", species) |
|
276 |
-mer<-c(a,b,c,d,e) |
|
277 |
-return(mer) |
|
278 |
-} |
|
279 |
- |
|
280 |
- |
|
281 |
-select_path_imm_syst<-function(Immune_system){ |
|
282 |
- species<-c("- Homo sapiens (human)") |
|
283 |
-a<-paste("Hematopoietic cell lineage", species) |
|
284 |
-b<-paste("Complement and coagulation cascades", species) |
|
285 |
-c<-paste("Platelet activation", species) |
|
286 |
-d<-paste("Toll-like receptor signaling pathway", species) |
|
287 |
-e<-paste("Toll and Imd signaling pathway", species) |
|
288 |
-f<-paste("NOD-like receptor signaling pathway", species) |
|
289 |
-g<-paste("RIG-I-like receptor signaling pathway", species) |
|
290 |
-h<-paste("Cytosolic DNA-sensing pathway", species) |
|
291 |
-i<-paste("Natural killer cell mediated cytotoxicity", species) |
|
292 |
-l<-paste("Antigen processing and presentation", species) |
|
293 |
-m<-paste("T cell receptor signaling pathway", species) |
|
294 |
-n<-paste("B cell receptor signaling pathway", species) |
|
295 |
-o<-paste("Fc epsilon RI signaling pathway", species) |
|
296 |
-p<-paste("Fc gamma R-mediated phagocytosis", species) |
|
297 |
-q<-paste("Leukocyte transendothelial migration", species) |
|
298 |
-r<-paste("Intestinal immune network for IgA production", species) |
|
299 |
-s<-paste("Chemokine signaling pathway", species) |
|
300 |
- |
|
301 |
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s) |
|
302 |
-return(mer) |
|
1 |
+#' @title List of species |
|
2 |
+#' @description List of species for network |
|
3 |
+#' @param path_spec variable |
|
4 |
+#' @importFrom graphite pathwayDatabases |
|
5 |
+#' @examples |
|
6 |
+#' \dontrun{m<-SELECTpathspecies(path_spec) |
|
7 |
+#' } |
|
8 |
+SELECTpathspecies<-function(path_spec){ |
|
9 |
+ e<-pathwayDatabases() |
|
10 |
+ return(e) |
|
303 | 11 |
} |
304 | 12 |
|
13 |
+Uniform<-function(pathwayfrom){ |
|
14 |
+ mapped_genes<-pathwayfrom |
|
15 |
+ xx <- unique(unlist(mapped_genes)) |
|
16 |
+ top3 <- matrix(0, length(xx), length(pathwayfrom)) |
|
17 |
+ rownames(top3) <- xx |
|
18 |
+ colnames(top3)<- names(pathwayfrom) |
|
19 |
+ for (j in 1:length(xx)){ |
|
20 |
+ for (k in 1:length(pathwayfrom)){ |
|
21 |
+ if (length(intersect(xx[[j]],pathwayfrom[[k]])!=0)){ |
|
22 |
+ |
|
23 |
+ top3[j,k]<-xx[j] |
|
24 |
+ } |
|
25 |
+ } |
|
26 |
+ } |
|
27 |
+ top3[top3 == 0] <- " " |
|
28 |
+return(top3) |
|
29 |
+ } |
|
305 | 30 |
|
306 | 31 |
|
307 | 32 |
|
308 |
-select_path_end_syst<-function(Endocrine_system){ |
|
309 |
- species<-c("- Homo sapiens (human)") |
|
310 |
-a<-paste("Insulin secretion", species) |
|
311 |
-b<-paste("Insulin signaling pathway", species) |
|
312 |
-c<-paste("Glucagon signaling pathway", species) |
|
313 |
-d<-paste("Regulation of lipolysis in adipocytes", species) |
|
314 |
-e<-paste("Adipocytokine signaling pathway", species) |
|
315 |
-f<-paste("PPAR signaling pathway", species) |
|
316 |
-g<-paste("GnRH signaling pathway", species) |
|
317 |
-h<-paste("Ovarian steroidogenesis", species) |
|
318 |
-i<-paste("Estrogen signaling pathway", species) |
|
319 |
-l<-paste("Progesterone-mediated oocyte maturation", species) |
|
320 |
-m<-paste("Prolactin signaling pathway", species) |
|
321 |
-n<-paste("Oxytocin signaling pathway", species) |
|
322 |
-o<-paste("Thyroid hormone synthesis", species) |
|
323 |
-p<-paste("Thyroid hormone signaling pathway", species) |
|
324 |
-q<-paste("Melanogenesis", species) |
|
325 |
-r<-paste("Renin secretion", species) |
|
326 |
-s<-paste("Renin-angiotensin system", species) |
|
327 |
-t<-paste("Aldosterone synthesis and secretion", species) |
|
328 |
- |
|
329 |
- |
|
330 |
-mer<-c(a,b,c,d,e,f,g,h,i,l,m,n,o,p,q,r,s,t) |
|
331 |
-return(mer) |
|
332 |
-} |
|
333 | 33 |
|
334 | 34 |
|
335 |
-select_path_circ_syst<-function(Circulatory_system){ |
|
336 |
- species<-c("- Homo sapiens (human)") |
|
337 |
- a<-paste("Cardiac muscle contraction", species) |
|
338 |
-b<-paste("Adrenergic signaling in cardiomyocytes", species) |
|
339 |
-c<-paste("Vascular smooth muscle contraction", species) |
|
340 |
-mer<-c(a,b,c) |
|
341 |
-return(mer) |
|
342 |
-} |
|
343 | 35 |
|
344 | 36 |
|
345 |
-select_path_dig_syst<-function(Digestive_system){ |
|
346 |
- species<-c("- Homo sapiens (human)") |
|
347 |
- a<-paste("Salivary secretion", species) |
|
348 |
-b<-paste("Gastric acid secretion", species) |
|
349 |
-c<-paste("Pancreatic secretion", species) |
|
350 |
-d<-paste("Bile secretion", species) |
|
351 |
-e<-paste("Carbohydrate digestion and absorption", species) |
|
352 |
-f<-paste("Protein digestion and absorption", species) |
|
353 |
-g<-paste("Fat digestion and absorption", species) |
|
354 |
-h<-paste("Vitamin digestion and absorption", species) |
|
355 |
-i<-paste("Mineral absorption", species) |
|
356 |
- |
|
357 |
-mer<-c(a,b,c,d,e,f,g,h,i) |
|
358 |
-return(mer) |
|
359 |
-} |
|
360 | 37 |
|
361 | 38 |
|
362 | 39 |
|
363 |
-select_path_exc_syst<-function(Excretory_system){ |
|
364 |
- species<-c("- Homo sapiens (human)") |
|
365 |
- a<-paste("Vasopressin-regulated water reabsorption", species) |
|
366 |
-b<-paste("Aldosterone-regulated sodium reabsorption", species) |
|
367 |
-c<-paste("Endocrine and other factor-regulated calcium reabsorption", species) |
|
368 |
-d<-paste("Proximal tubule bicarbonate reclamation", species) |
|
369 |
-e<-paste("Collecting duct acid secretion", species) |
|
370 | 40 |
|
371 | 41 |
|
372 |
-mer<-c(a,b,c,d,e) |
|
373 |
-return(mer) |
|
374 |
-} |
|
375 | 42 |
|
376 | 43 |
|
377 |
-select_path_ner_syst<-function(Nervous_system){ |
|
378 |
- species<-c("- Homo sapiens (human)") |
|
379 |
-a<-paste("Glutamatergic synapse", species) |
|
380 |
-b<-paste("GABAergic synapse", species) |
|
381 |
-c<-paste("Cholinergic synapse", species) |
|
382 |
-d<-paste("Dopaminergic synapse", species) |
|
383 |
-e<-paste("Serotonergic synapse", species) |
|
384 |
-f<-paste("Long-term potentiation", species) |
|
385 |
-g<-paste("Long-term depression", species) |
|
386 |
-h<-paste("Retrograde endocannabinoid signaling", species) |
|
387 |
-i<-paste("Synaptic vesicle cycle", species) |
|
388 |
-l<-paste("Neurotrophin signaling pathway", species) |
|
389 |
- |
|
390 |
-mer<-c(a,b,c,d,e,f,g,h,i,l) |
|
391 |
-return(mer) |
|
392 |
-} |
|
393 |
- |
|
394 |
- |
|
395 |
-select_path_sens_syst<-function(Sensory_system){ |
|
396 |
- species<-c("- Homo sapiens (human)") |
|
397 |
- a<-paste("Phototransduction", species) |
|
398 |
-b<-paste("Olfactory transduction", species) |
|
399 |
-c<-paste("Taste transduction", species) |
|
400 |
-d<-paste("Inflammatory mediator regulation of TRP channels", species) |
|
401 |
-mer<-c(a,b,c,d) |
|
402 |
-return(mer) |
|
44 |
+delete.NULLs <- function(xlist){ # delele null/empty entries in a list |
|
45 |
+ xlist[unlist(lapply(xlist, nrow) != 0)] |
|
403 | 46 |
} |
404 | 47 |
|
405 | 48 |
|
... | ... |
@@ -411,9 +54,9 @@ return(mer) |
411 | 54 |
#' @export |
412 | 55 |
#' @return a gene expression matrix of the samples with specified label |
413 | 56 |
#' @examples |
414 |
-#' tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_filt,typesample="tumor")[,2] |
|
57 |
+#' tumo<-SelectedSample(Dataset=Data_CANCER_normUQ_fil,typesample="tumour")[,2] |
|
415 | 58 |
SelectedSample <- function(Dataset,typesample){ |
416 |
- if( typesample =="tumor"){ |
|
59 |
+ if( typesample =="tumour"){ |
|
417 | 60 |
Dataset <- Dataset[,which( as.numeric(substr(colnames(Dataset), 14, 15)) == 01) ] |
418 | 61 |
} |
419 | 62 |
|
... | ... |
@@ -427,115 +70,284 @@ SelectedSample <- function(Dataset,typesample){ |
427 | 70 |
|
428 | 71 |
|
429 | 72 |
#' @title Select the class of TCGA data |
430 |
-#' @description select two labels from ID barcode |
|
73 |
+#' @description select best performance |
|
74 |
+#' @param performance_matrix list of AUC value |
|
431 | 75 |
#' @param cutoff cut-off for AUC value |
432 |
-#' @param auc.df list of AUC value |
|
433 | 76 |
#' @return a gene expression matrix with only pairwise pathway with a particular cut-off |
434 |
-select_class<-function(auc.df,cutoff){ |
|
435 |
-ds<-do.call("rbind", auc.df) |
|
436 |
-tmp_ordered <- as.data.frame(ds[order(ds,decreasing=TRUE),]) |
|
437 |
-colnames(tmp_ordered)<-'pathway' |
|
438 |
-er<-as.data.frame(tmp_ordered$pathway>cutoff) |
|
439 |
-ase<-tmp_ordered[tmp_ordered$pathway>cutoff,] |
|
440 |
-rownames(er)<-rownames(tmp_ordered) |
|
441 |
-er[,2]<-tmp_ordered$pathway |
|
442 |
-lipid_metabolism<-er[1:length(ase),] |
|
443 |
-return(lipid_metabolism) |
|
77 |
+select_class<-function(performance_matrix,cutoff){ |
|
78 |
+ tmp_ordered <- as.data.frame(performance_matrix[order(performance_matrix[,1],decreasing=TRUE),]) |
|
79 |
+ |
|
80 |
+ er<-tmp_ordered[tmp_ordered[,1]>cutoff,] |
|
81 |
+ #ase<-tmp_ordered[tmp_ordered$pathway>cutoff,] |
|
82 |
+ #rownames(er)<-rownames(tmp_ordered) |
|
83 |
+ #er[,2]<-tmp_ordered$pathway |
|
84 |
+ #lipid_metabolism<-er[1:length(ase),] |
|
85 |
+ return(er) |
|
444 | 86 |
} |
445 | 87 |
|
446 | 88 |
|
447 | 89 |
|
448 | 90 |
|
449 |
-#' @title Process matrix TCGA data after the selection of pairwise pathway |
|
450 |
-#' @description processing gene expression matrix |
|
451 |
-#' @param measure matrix with measure of cross-talk among pathways |
|
452 |
-#' @param list_perf output of the function select_class |
|
453 |
-#' @return a gene expression matrix for case study 1 |
|
454 |
-process_matrix<-function(measure,list_perf){ |
|
455 |
-scoreMatrix <- as.data.frame(measure[,3:ncol(measure)]) |
|
456 |
-for( i in 1: ncol(scoreMatrix)){ |
|
457 |
- scoreMatrix[,i] <- as.numeric(as.character(scoreMatrix[,i])) |
|
458 |
-} |
|
459 |
-measure[,1] <- gsub(" ", "_", measure[,1]) |
|
460 |
-d<-sub('_-_Homo_sapiens_*', '', measure[,1]) |
|
461 |
-d_pr<- gsub("(human)", "", d, fixed="TRUE") |
|
462 |
-d_pr <- gsub("_", "", d_pr) |
|
463 |
-d_pr <- gsub("-", "", d_pr) |
|
464 |
-measure[,2] <- gsub(" ", "_", measure[,2]) |
|
465 |
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure[,2]) |
|
466 |
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE") |
|
467 |
-d_pr2 <- gsub("_", "", d_pr2) |
|
468 |
-d_pr2 <- gsub("-", "", d_pr2) |
|
469 |
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" ) |
|
470 |
-rownames(scoreMatrix) <-PathwaysPair |
|
471 |
-intera<-intersect(rownames(scoreMatrix),rownames(list_perf)) |
|
472 |
-path_bestlipd<-scoreMatrix[intera,] |
|
473 |
-return(path_bestlipd) |
|
91 |
+IPPIpath_net<-function(pathway,data){ |
|
92 |
+ lista_int<-list() |
|
93 |
+ for (k in 1:ncol(pathway)){ |
|
94 |
+ print(colnames(pathway)[k]) |
|
95 |
+ currentPathway_genes<-pathway[,k] |
|
96 |
+ colnames(data) <- c("gene_symbolA", "gene_symbolB") |
|
97 |
+ i <- sapply(data, is.factor) |
|
98 |
+ data[i] <- lapply(data[i], as.character) |
|
99 |
+ ver<-unlist(data) |
|
100 |
+ n<-unique(ver) |
|
101 |
+ s<-intersect(n,currentPathway_genes) |
|
102 |
+ g <- graph.data.frame(data,directed=FALSE) |
|
103 |
+ g2 <- induced.subgraph(graph=g,vids=s) |
|
104 |
+ aaa<-get.data.frame(g2) |
|
105 |
+ colnames(aaa)[1] <- 'V1' |
|
106 |
+ colnames(aaa)[2] <- 'V2' |
|
107 |
+ lista_int[[k]]<-aaa |
|
108 |
+ names(lista_int)[k]<-colnames(pathway)[k] |
|
109 |
+ } |
|
110 |
+ return(lista_int) |
|
474 | 111 |
} |
475 | 112 |
|
476 | 113 |
|
477 | 114 |
|
478 |
-process_matrix_cell_process<-function(measure_cell_process){ |
|
479 |
-score__cell_grow_d <- as.data.frame(measure_cell_process[,3:ncol(measure_cell_process)]) |
|
480 |
-for( i in 1: ncol(score__cell_grow_d)){ |
|
481 |
- score__cell_grow_d[,i] <- as.numeric(as.character(score__cell_grow_d[,i])) |
|
482 |
-} |
|
483 |
- |
|
484 |
-measure_cell_process[,1] <- gsub(" ", "_", measure_cell_process[,1]) |
|
485 |
-d<-sub('_-_Homo_sapiens_*', '', measure_cell_process[,1]) |
|
486 |
- |
|
487 |
-d_pr<- gsub("(human)", "", d, fixed="TRUE") |
|
488 |
-d_pr <- gsub("_", "", d_pr) |
|
489 |
-d_pr <- gsub("-", "", d_pr) |
|
490 | 115 |
|
491 |
-measure_cell_process[,2] <- gsub(" ", "_", measure_cell_process[,2]) |
|
492 |
-d2<-sub('_-_Homo_sapiens_(human)*', '', measure_cell_process[,2]) |
|
493 |
-d_pr2<- gsub("(human)", "", d2, fixed="TRUE") |
|
494 |
-d_pr2 <- gsub("_", "", d_pr2) |
|
495 |
-d_pr2 <- gsub("-", "", d_pr2) |
|
496 | 116 |
|
497 |
-PathwaysPair <- paste( as.matrix(d_pr), as.matrix(d_pr2),sep="_" ) |
|
498 |
-rownames(score__cell_grow_d) <-PathwaysPair |
|
499 |
-return(score__cell_grow_d) |
|
500 |
-} |
|
117 |
+IPPIlist_path_net<-function(lista_net,pathway){ |
|
118 |
+ v=list() |
|
119 |
+ bn=list() |
|
120 |
+ for (j in 1:length(lista_net)){ |
|
121 |
+ cf<-lista_net[[j]] |
|
122 |
+ i <- sapply(cf, is.factor) |
|
123 |
+ cf[i] <- lapply(cf[i], as.character) |
|
124 |
+ colnames(cf) <- c("m_shar_pro", "m2_shar_pro") |
|
125 |
+ m<-c(cf$m_shar_pro) |
|
126 |
+ m2<-c(cf$m2_shar_pro) |
|
127 |
+ s<-c(m,m2) |
|
128 |
+ fr<- unique(s) |
|
129 |
+ n<-as.data.frame(fr) |
|
130 |
+ if(length(n)==0){ |
|
131 |
+ v[[j]]<-NULL |
|
132 |
+ |
|
133 |
+ } |
|
134 |
+ if(length(n)!=0){ |
|
135 |
+ i <- sapply(n, is.factor) |
|
136 |
+ n[i] <- lapply(n[i], as.character) |
|
137 |
+ #for (k in 1:ncol(pathway)){ |
|
138 |
+ if (length(intersect(n$fr,pathway[,j]))==nrow(n)){ |
|
139 |
+ print(paste("List of genes interacting in the same pathway:",colnames(pathway)[j])) |
|
140 |
+ aa<-intersect(n$fr,pathway[,j]) |
|
141 |
+ v[[j]]<-aa |
|
142 |
+ names(v)[j]<-colnames(pathway)[j] |
|
143 |
+ } |
|
144 |
+ }} |
|
145 |
+ return(v)} |
|
501 | 146 |
|
502 | 147 |
|
503 |
-#' @title Get human KEGG pathway data. |
|
504 |
-#' @description getKEGGdata creates a data frame with human KEGG pathway. Columns are the pathways and rows the genes inside those pathway |
|
505 |
-#' @param mer output for example of select_path_carb |
|
506 |
-#' @export |
|
507 |
-#' @importFrom KEGGREST keggList |
|
508 |
-#' @return dataframe with human pathway data |
|
509 |
-proc_path<-function(mer){ |
|
510 |
-pathways.list <- keggList("pathway", "hsa")## returns the list of human pathways |
|
511 |
-common<-intersect(pathways.list,mer) |
|
512 |
-lo<-list() |
|
513 |
-for (i in 1:length(pathways.list)){ |
|
514 |
- if (length(intersect(pathways.list[[i]],common)!=0)){ |
|
515 |
- lo[[i]]<-pathways.list[[i]] |
|
516 |
- names(lo)[[i]]<-names(pathways.list)[[i]] |
|
517 |
- } |
|
518 |
-} |
|
519 |
-pathways.list<-lo[lapply(lo,length)!=0] |
|
520 |
-pathway.codes <- sub("path:", "", names(pathways.list)) |
|
521 |
-b<-do.call("rbind", pathways.list) |
|
522 |
-list_pathkegg<-list(pathway.codes,b) |
|
523 |
-return(list_pathkegg) |
|
524 |
-} |
|
525 | 148 |
|
526 | 149 |
|
527 | 150 |
|
528 |
-delete.NULLs <- function(xlist){ # delele null/empty entries in a list |
|
529 |
- xlist[unlist(lapply(xlist, nrow) != 0)] |
|
151 |
+check_chord <- function(mat, limit){ |
|
152 |
+ |
|
153 |
+ if(all(colSums(mat) >= limit[2]) & all(rowSums(mat) >= limit[1])) return(mat) |
|
154 |
+ |
|
155 |
+ tmp <- mat[(rowSums(mat) >= limit[1]),] |
|
156 |
+ mat <- tmp[,(colSums(tmp) >= limit[2])] |
|
157 |
+ |
|
158 |
+ mat <- check_chord(mat, limit) |
|
159 |
+ return(mat) |
|
530 | 160 |
} |
531 | 161 |
|
162 |
+bezier <- function(data, process.col){ |
|
163 |
+ x <- c() |
|
164 |
+ y <- c() |
|
165 |
+ Id <- c() |
|
166 |
+ sequ <- seq(0, 1, by = 0.01) |
|
167 |
+ N <- dim(data)[1] |
|
168 |
+ sN <- seq(1, N, by = 2) |
|
169 |
+ if (process.col[1] == '') col_rain <- grDevices::rainbow(N) else col_rain <- process.col |
|
170 |
+ for (n in sN){ |
|
171 |
+ xval <- c(); xval2 <- c(); yval <- c(); yval2 <- c() |
|
172 |
+ for (t in sequ){ |
|
173 |
+ xva <- (1 - t) * (1 - t) * data$x.start[n] + t * t * data$x.end[n] |
|
174 |
+ xval <- c(xval, xva) |
|
175 |
+ xva2 <- (1 - t) * (1 - t) * data$x.start[n + 1] + t * t * data$x.end[n + 1] |
|
176 |
+ xval2 <- c(xval2, xva2) |
|
177 |
+ yva <- (1 - t) * (1 - t) * data$y.start[n] + t * t * data$y.end[n] |
|
178 |
+ yval <- c(yval, yva) |
|
179 |
+ yva2 <- (1 - t) * (1 - t) * data$y.start[n + 1] + t * t * data$y.end[n + 1] |
|
180 |
+ yval2 <- c(yval2, yva2) |
|
181 |
+ } |
|
182 |
+ x <- c(x, xval, rev(xval2)) |
|
183 |
+ y <- c(y, yval, rev(yval2)) |
|
184 |
+ Id <- c(Id, rep(n, 2 * length(sequ))) |
|
185 |
+ } |
|
186 |
+ df <- data.frame(lx = x, ly = y, ID = Id) |
|
187 |
+ return(df) |
|
188 |
+} |
|
532 | 189 |
|
533 | 190 |
|
534 |
- |
|
535 |
- |
|
536 |
- |
|
537 |
- |
|
538 |
- |
|
539 |
- |
|
191 |
+#' @name GOChord |
|
192 |
+#' @title Displays the relationship between genes and terms. |
|
193 |
+#' @description The GOChord function generates a circularly composited overview |
|
194 |
+#' of selected/specific genes and their assigned processes or terms. More |
|
195 |
+#' generally, it joins genes and processes via ribbons in an intersection-like |
|
196 |
+#' graph. |
|
197 |
+#' @param data The matrix represents the binary relation (1= is related to, 0= |
|
198 |
+#' is not related to) between a set of genes (rows) and processes (columns); a |
|
199 |
+#' column for the logFC of the genes is optional |
|
200 |
+#' @param title The title (on top) of the plot |
|
201 |
+#' @param space The space between the chord segments of the plot |
|
202 |
+#' @param gene.order A character vector defining the order of the displayed gene |
|
203 |
+#' labels |
|
204 |
+#' @param gene.size The size of the gene labels |
|
205 |
+#' @param gene.space The space between the gene labels and the segement of the |
|
206 |
+#' logFC |
|
207 |
+#' @param nlfc Defines the number of logFC columns (default=1) |
|
208 |
+#' @param lfc.col The fill color for the logFC specified in the following form: |
|
209 |
+#' c(color for low values, color for the mid point, color for the high values) |
|
210 |
+#' @param lfc.min Specifies the minimium value of the logFC scale (default = -3) |
|
211 |
+#' @param lfc.max Specifies the maximum value of the logFC scale (default = 3) |
|
212 |
+#' @param ribbon.col The background color of the ribbons |
|
213 |
+#' @param border.size Defines the size of the ribbon borders |
|
214 |
+#' @param process.label The size of the legend entries |
|
215 |
+#' @param limit A vector with two cutoff values (default= c(0,0)). |
|
216 |
+#' @import ggplot2 |
|
217 |
+#' @import grDevices |
|
218 |
+GOChord <- function(data, title, space, gene.order, gene.size, gene.space, nlfc = 1, lfc.col, lfc.min, lfc.max, ribbon.col, border.size, process.label, limit){ |
|
219 |
+ y <- id <- xpro <- ypro <- xgen <- ygen <- lx <- ly <- ID <- logFC <- NULL |
|
220 |
+ Ncol <- dim(data)[2] |
|
221 |
+ |
|
222 |
+ if (missing(title)) title <- '' |
|
223 |
+ if (missing(space)) space = 0 |
|
224 |
+ if (missing(gene.order)) gene.order <- 'none' |
|
225 |
+ if (missing(gene.size)) gene.size <- 3 |
|
226 |
+ if (missing(gene.space)) gene.space <- 0.2 |
|
227 |
+ if (missing(lfc.col)) lfc.col <- c('brown1', 'azure', 'cornflowerblue') |
|
228 |
+ if (missing(lfc.min)) lfc.min <- -10 |
|
229 |
+ if (missing(lfc.max)) lfc.max <- 10 |
|
230 |
+ if (missing(border.size)) border.size <- 0.5 |
|
231 |
+ if (missing (process.label)) process.label <- 11 |
|
232 |
+ if (missing(limit)) limit <- c(0, 0) |
|
233 |
+ |
|
234 |
+ if (gene.order == 'logFC') data <- data[order(data[, Ncol], decreasing = T), ] |
|
235 |
+ if (gene.order == 'alphabetical') data <- data[order(rownames(data)), ] |
|
236 |
+ if (sum(!is.na(match(colnames(data), 'logFC'))) > 0){ |
|
237 |
+ if (nlfc == 1){ |
|
238 |
+ cdata <- check_chord(data[, 1:(Ncol - 1)], limit) |
|
239 |
+ lfc <- sapply(rownames(cdata), function(x) data[match(x,rownames(data)), Ncol]) |
|
240 |
+ }else{ |
|
241 |
+ cdata <- check_chord(data[, 1:(Ncol - nlfc)], limit) |
|
242 |
+ lfc <- sapply(rownames(cdata), function(x) data[, (Ncol - nlfc + 1)]) |
|
243 |
+ } |
|
244 |
+ }else{ |
|
245 |
+ cdata <- check_chord(data, limit) |
|
246 |
+ lfc <- 0 |
|
247 |
+ } |
|
248 |
+ if (missing(ribbon.col)) colRib <- grDevices::rainbow(dim(cdata)[2]) else colRib <- ribbon.col |
|
249 |
+ nrib <- colSums(cdata) |
|
250 |
+ ngen <- rowSums(cdata) |
|
251 |
+ Ncol <- dim(cdata)[2] |
|
252 |
+ Nrow <- dim(cdata)[1] |
|
253 |
+ colRibb <- c() |
|
254 |
+ for (b in 1:length(nrib)) colRibb <- c(colRibb, rep(colRib[b], 202 * nrib[b])) |
|
255 |
+ r1 <- 1; r2 <- r1 + 0.1 |
|
256 |
+ xmax <- c(); x <- 0 |
|
257 |
+ for (r in 1:length(nrib)){ |
|
258 |
+ perc <- nrib[r] / sum(nrib) |
|
259 |
+ xmax <- c(xmax, (pi * perc) - space) |
|
260 |
+ if (length(x) <= Ncol - 1) x <- c(x, x[r] + pi * perc) |
|
261 |
+ } |
|
262 |
+ xp <- c(); yp <- c() |
|
263 |
+ l <- 50 |
|
264 |
+ for (s in 1:Ncol){ |
|
265 |
+ xh <- seq(x[s], x[s] + xmax[s], length = l) |
|
266 |
+ xp <- c(xp, r1 * sin(x[s]), r1 * sin(xh), r1 * sin(x[s] + xmax[s]), r2 * sin(x[s] + xmax[s]), r2 * sin(rev(xh)), r2 * sin(x[s])) |
|
267 |
+ yp <- c(yp, r1 * cos(x[s]), r1 * cos(xh), r1 * cos(x[s] + xmax[s]), r2 * cos(x[s] + xmax[s]), r2 * cos(rev(xh)), r2 * cos(x[s])) |
|
268 |
+ } |
|
269 |
+ df_process <- data.frame(x = xp, y = yp, id = rep(c(1:Ncol), each = 4 + 2 * l)) |
|
270 |
+ xp <- c(); yp <- c(); logs <- NULL |
|
271 |
+ x2 <- seq(0 - space, -pi - (-pi / Nrow) - space, length = Nrow) |
|
272 |
+ xmax2 <- rep(-pi / Nrow + space, length = Nrow) |
|
273 |
+ for (s in 1:Nrow){ |
|
274 |
+ xh <- seq(x2[s], x2[s] + xmax2[s], length = l) |
|
275 |
+ if (nlfc <= 1){ |
|
276 |
+ xp <- c(xp, (r1 + 0.05) * sin(x2[s]), (r1 + 0.05) * sin(xh), (r1 + 0.05) * sin(x2[s] + xmax2[s]), r2 * sin(x2[s] + xmax2[s]), r2 * sin(rev(xh)), r2 * sin(x2[s])) |
|
277 |
+ yp <- c(yp, (r1 + 0.05) * cos(x2[s]), (r1 + 0.05) * cos(xh), (r1 + 0.05) * cos(x2[s] + xmax2[s]), r2 * cos(x2[s] + xmax2[s]), r2 * cos(rev(xh)), r2 * cos(x2[s])) |
|
278 |
+ }else{ |
|
279 |
+ tmp <- seq(r1, r2, length = nlfc + 1) |
|
280 |
+ for (t in 1:nlfc){ |
|
281 |
+ logs <- c(logs, data[s, (dim(data)[2] + 1 - t)]) |
|
282 |
+ xp <- c(xp, (tmp[t]) * sin(x2[s]), (tmp[t]) * sin(xh), (tmp[t]) * sin(x2[s] + xmax2[s]), tmp[t + 1] * sin(x2[s] + xmax2[s]), tmp[t + 1] * sin(rev(xh)), tmp[t + 1] * sin(x2[s])) |
|
283 |
+ yp <- c(yp, (tmp[t]) * cos(x2[s]), (tmp[t]) * cos(xh), (tmp[t]) * cos(x2[s] + xmax2[s]), tmp[t + 1] * cos(x2[s] + xmax2[s]), tmp[t + 1] * cos(rev(xh)), tmp[t + 1] * cos(x2[s])) |
|
284 |
+ }}} |
|
285 |
+ if(lfc[1] != 0){ |
|
286 |
+ if (nlfc == 1){ |
|
287 |
+ df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:Nrow), each = 4 + 2 * l), logFC = rep(lfc, each = 4 + 2 * l)) |
|
288 |
+ }else{ |
|
289 |
+ df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:(nlfc*Nrow)), each = 4 + 2 * l), logFC = rep(logs, each = 4 + 2 * l)) |
|
290 |
+ } |
|
291 |
+ }else{ |
|
292 |
+ df_genes <- data.frame(x = xp, y = yp, id = rep(c(1:Nrow), each = 4 + 2 * l)) |
|
293 |
+ } |
|
294 |
+ aseq <- seq(0, 180, length = length(x2)); angle <- c() |
|
295 |
+ for (o in aseq) if((o + 270) <= 360) angle <- c(angle, o + 270) else angle <- c(angle, o - 90) |
|
296 |
+ df_texg <- data.frame(xgen = (r1 + gene.space) * sin(x2 + xmax2/2),ygen = (r1 + gene.space) * cos(x2 + xmax2 / 2),labels = rownames(cdata), angle = angle) |
|
297 |
+ df_texp <- data.frame(xpro = (r1 + 0.15) * sin(x + xmax / 2),ypro = (r1 + 0.15) * cos(x + xmax / 2), labels = colnames(cdata), stringsAsFactors = FALSE) |
|
298 |
+ cols <- rep(colRib, each = 4 + 2 * l) |
|
299 |
+ x.end <- c(); y.end <- c(); processID <- c() |
|
300 |
+ for (gs in 1:length(x2)){ |
|
301 |
+ val <- seq(x2[gs], x2[gs] + xmax2[gs], length = ngen[gs] + 1) |
|
302 |
+ pros <- which((cdata[gs, ] != 0) == T) |
|
303 |
+ for (v in 1:(length(val) - 1)){ |
|
304 |
+ x.end <- c(x.end, sin(val[v]), sin(val[v + 1])) |
|
305 |
+ y.end <- c(y.end, cos(val[v]), cos(val[v + 1])) |
|
306 |
+ processID <- c(processID, rep(pros[v], 2)) |
|
307 |
+ } |
|
308 |
+ } |
|
309 |
+ df_bezier <- data.frame(x.end = x.end, y.end = y.end, processID = processID) |
|
310 |
+ df_bezier <- df_bezier[order(df_bezier$processID,-df_bezier$y.end),] |
|
311 |
+ x.start <- c(); y.start <- c() |
|
312 |
+ for (rs in 1:length(x)){ |
|
313 |
+ val<-seq(x[rs], x[rs] + xmax[rs], length = nrib[rs] + 1) |
|
314 |
+ for (v in 1:(length(val) - 1)){ |
|
315 |
+ x.start <- c(x.start, sin(val[v]), sin(val[v + 1])) |
|
316 |
+ y.start <- c(y.start, cos(val[v]), cos(val[v + 1])) |
|
317 |
+ } |
|
318 |
+ } |
|
319 |
+ df_bezier$x.start <- x.start |
|
320 |
+ df_bezier$y.start <- y.start |
|
321 |
+ df_path <- bezier(df_bezier, colRib) |
|
322 |
+ if(length(df_genes$logFC) != 0){ |
|
323 |
+ tmp <- sapply(df_genes$logFC, function(x) ifelse(x > lfc.max, lfc.max, x)) |
|
324 |
+ logFC <- sapply(tmp, function(x) ifelse(x < lfc.min, lfc.min, x)) |
|
325 |
+ df_genes$logFC <- logFC |
|
326 |
+ } |
|
327 |
+ |
|
328 |
+ g<- ggplot() + |
|
329 |
+ geom_polygon(data = df_process, aes(x, y, group=id), fill='gray70', inherit.aes = F,color='black') + |
|
330 |
+ geom_polygon(data = df_process, aes(x, y, group=id), fill=cols, inherit.aes = F,alpha=0.6,color='black') + |
|
331 |
+ geom_point(aes(x = xpro, y = ypro, size = factor(labels, levels = labels), shape = NA), data = df_texp) + |
|
332 |
+ guides(size = guide_legend("Pathway", ncol = 4, byrow = T, override.aes = list(shape = 22, fill = unique(cols), size = 8))) + |
|
333 |
+ theme(legend.text = element_text(size = process.label)) + |
|
334 |
+ geom_text(aes(xgen, ygen, label = labels, angle = angle), data = df_texg, size = gene.size) + |
|
335 |
+ geom_polygon(aes(x = lx, y = ly, group = ID), data = df_path, fill = colRibb, color = 'black', size = border.size, inherit.aes = F) + |
|
336 |
+ labs(title = title) + theme(axis.line = element_blank(), axis.text.x = element_blank(), |
|
337 |
+ axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), |
|
338 |
+ axis.title.y = element_blank(), panel.background = element_blank(), panel.border = element_blank(), |
|
339 |
+ panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.background = element_blank()) |
|
340 |
+ |
|
341 |
+ |
|
342 |
+ if (nlfc >= 1){ |
|
343 |
+ g + geom_polygon(data = df_genes, aes(x, y, group = id, fill = logFC), inherit.aes = F, color = 'black') + |
|
344 |
+ scale_fill_gradient2('score', space = 'Lab', low = lfc.col[3], mid = lfc.col[2], high = lfc.col[1], guide = guide_colorbar(title.position = "top", title.hjust = 0.5), |
|
345 |
+ breaks = c(min(df_genes$logFC), max(df_genes$logFC)), labels = c(round(min(df_genes$logFC)), round(max(df_genes$logFC)))) + |
|
346 |
+ theme(legend.position = 'bottom', legend.background = element_rect(fill = 'transparent'), legend.box = 'horizontal', legend.direction = 'horizontal') |
|
347 |
+ }else{ |
|
348 |
+ g + geom_polygon(data = df_genes, aes(x, y, group = id), fill = 'gray50', inherit.aes = F, color = 'black')+ |
|
349 |
+ theme(legend.position = 'bottom', legend.background = element_rect(fill = 'transparent'), legend.box = 'horizontal', legend.direction = 'horizontal') |
|
350 |
+ } |
|
351 |
+} |
|
540 | 352 |
|
541 | 353 |
|
... | ... |
@@ -1,17 +1,28 @@ |
1 |
-#' @title Get human KEGG pathway data and network data in order to define the common gene. |
|
2 |
-#' @description path_net creates a list of network data for each human pathway. The network data will be generated when interacting genes belong to that pathway. |
|
3 |
-#' @param data network data as provided by getNETdata |
|
4 |
-#' @param pathway pathway data as provided by getKEGGdata |
|
1 |
+#' @title Get human KEGG pathway data and creates a network data. |
|
2 |
+#' @description pathnet creates a list of network data for each human pathway. The network data will be generated when interacting genes belong to that pathway. |
|
3 |
+#' @param data a list of network data as provided by getNETdata |
|
4 |
+#' @param genes.by.pathway a list of pathway data as provided by ConvertedIDgenes |
|
5 | 5 |
#' @importFrom igraph graph.data.frame induced.subgraph get.data.frame |
6 | 6 |
#' @export |
7 | 7 |
#' @return a list of network data for each pathway (interacting genes belong to that pathway) |
8 | 8 |
#' @examples |
9 |
-#' lista_net<-path_net(pathway=path,data=netw) |
|
10 |
-path_net<-function(pathway,data){ |
|
9 |
+#' lista_net<-pathnet(genes.by.pathway=pathway[1:5],data=netw) |
|
10 |
+pathnet<-function(genes.by.pathway,data){ |
|
11 |
+ geneSymb_net_shar_pro<-data |
|
12 |
+ ds_shar_pro<-do.call("rbind", geneSymb_net_shar_pro) |
|
13 |
+ data_shar_pro<-as.data.frame(ds_shar_pro[!duplicated(ds_shar_pro), ]) |
|
14 |
+ sdc_shar_pro<-unlist(data_shar_pro$gene_symbolA,data_shar_pro$gene_symbolB) |
|
15 |
+ m_shar_pro<-c(data_shar_pro$gene_symbolA) |
|
16 |
+ m2_shar_pro<-c(data_shar_pro$gene_symbolB) |
|
17 |
+ ss_shar_pro<-cbind(m_shar_pro,m2_shar_pro) |
|
18 |
+ data_pr_shar_pro<-as.data.frame(ss_shar_pro[!duplicated(ss_shar_pro), ]) |
|
19 |
+ pathwayx<- Uniform(genes.by.pathway) |
|
20 |
+ data<-data_pr_shar_pro |
|
21 |
+ |
|
11 | 22 |
lista_int<-list() |
12 |
- for (k in 1:ncol(pathway)){ |
|
13 |
- print(colnames(pathway)[k]) |
|
14 |
- currentPathway_genes<-pathway[,k] |
|
23 |
+ for (k in 1:ncol(pathwayx)){ |
|
24 |
+ print(colnames(pathwayx)[k]) |
|
25 |
+ currentPathway_genes<-pathwayx[,k] |
|
15 | 26 |
colnames(data) <- c("gene_symbolA", "gene_symbolB") |
16 | 27 |
i <- sapply(data, is.factor) |
17 | 28 |
data[i] <- lapply(data[i], as.character) |
... | ... |
@@ -24,7 +35,7 @@ path_net<-function(pathway,data){ |
24 | 35 |
colnames(aaa)[1] <- 'V1' |
25 | 36 |
colnames(aaa)[2] <- 'V2' |
26 | 37 |
lista_int[[k]]<-aaa |
27 |
- names(lista_int)[k]<-colnames(pathway)[k] |
|
38 |
+ names(lista_int)[k]<-colnames(pathwayx)[k] |
|
28 | 39 |
} |
29 | 40 |
return(lista_int) |
30 | 41 |
} |
... | ... |
@@ -32,16 +43,18 @@ path_net<-function(pathway,data){ |
32 | 43 |
|
33 | 44 |
|
34 | 45 |
|
35 |
-#' @title Get human KEGG pathway data and output of path_net in order to define the common genes. |
|
36 |
-#' @description list_path_net creates a list of interacting genes for each human pathway. |
|
46 |
+#' @title Get human KEGG pathway data and the output of list_path_net define the common genes. |
|
47 |
+#' @description listpathnet creates a list of interacting genes for each human pathway. |
|
37 | 48 |
#' @param lista_net output of path_net |
38 |
-#' @param pathway pathway data as provided by getKEGGdata |
|
49 |
+#' @param pathway_exp pathway data as provided by getKEGGdata |
|
39 | 50 |
#' @export |
40 | 51 |
#' @return a list of genes for each pathway (interacting genes belong to that pathway) |
41 | 52 |
#' @examples |
42 |
-#' lista_netw<-path_net(pathway=path,data=netw) |
|
43 |
-#' list_path<-list_path_net(lista_net=lista_netw,pathway=path) |
|
44 |
-list_path_net<-function(lista_net,pathway){ |
|
53 |
+#' lista_network<-pathnet(genes.by.pathway=pathway[1:5],data=netw) |
|
54 |
+#' list_path<-listpathnet(lista_net=lista_network,pathway=pathway[1:5]) |
|
55 |
+listpathnet<-function(lista_net,pathway_exp){ |
|
56 |
+ top3<- Uniform(pathway_exp) |
|
57 |
+ pathway<-top3 |
|
45 | 58 |
v=list() |
46 | 59 |
bn=list() |
47 | 60 |
for (j in 1:length(lista_net)){ |
... | ... |
@@ -56,9 +69,7 @@ for (j in 1:length(lista_net)){ |
56 | 69 |
n<-as.data.frame(fr) |
57 | 70 |
if(length(n)==0){ |
58 | 71 |
v[[j]]<-NULL |
59 |
- |
|
60 | 72 |
} |
61 |
- if(length(n)!=0){ |
|
62 | 73 |
i <- sapply(n, is.factor) |
63 | 74 |
n[i] <- lapply(n[i], as.character) |
64 | 75 |
#for (k in 1:ncol(pathway)){ |
... | ... |
@@ -68,31 +79,30 @@ for (j in 1:length(lista_net)){ |
68 | 79 |
v[[j]]<-aa |
69 | 80 |
names(v)[j]<-colnames(pathway)[j] |
70 | 81 |
} |
71 |
-}} |
|
82 |
+} |
|
72 | 83 |
return(v)} |
73 | 84 |
|
74 | 85 |
|
75 | 86 |
|
76 | 87 |
|
77 |
-#' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a matrix with the gene expression for only pathways given in input . |
|
78 |
-#' @description GE_matrix creates a matrix of gene expression for pathways given by the user. |
|
88 |
+#' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a list with the gene expression for only pathways given in input . |
|
89 |
+#' @description GE_matrix creates a list of gene expression for pathways given by the user. |
|
79 | 90 |
#' @param DataMatrix gene expression matrix (eg.TCGA data) |
80 |
-#' @param pathway pathway data as provided by getKEGGdata |
|
91 |
+#' @param genes.by.pathway a list of pathway data as provided by GetData and ConvertedID_genes |
|
81 | 92 |
#' @export |
82 |
-#' @return a matrix for each pathway ( gene expression level belong to that pathway) |
|
93 |
+#' @return a list for each pathway ( gene expression level belong to that pathway) |
|
83 | 94 |
#' @examples |
84 |
-#' list_path_gene<-GE_matrix(DataMatrix=tumo[,1:2],pathway=path) |
|
85 |
-GE_matrix<-function(DataMatrix,pathway) { |
|
86 |
- path_name<-sub(' ', '_',colnames(pathway)) |
|
87 |
-d_pr<- gsub(" - Homo sapiens (human)", "", path_name, fixed="TRUE") |
|
88 |
-colnames(pathway)<-d_pr |
|
89 |
-#zz<-as.data.frame(rowMeans(DataMatrix)) |
|
95 |
+#' list_path_gene<-GE_matrix(DataMatrix=tumo[,1:2],genes.by.pathway=pathway[1:5]) |
|
96 |
+GE_matrix<-function(DataMatrix,genes.by.pathway) { |
|
97 |
+ pathwayfrom<-genes.by.pathway |
|
98 |
+ top3<- Uniform(pathwayfrom) |
|
99 |
+ pathway<-top3 |
|
90 | 100 |
zz<-as.data.frame(DataMatrix) |
91 | 101 |
v<-list() |
92 | 102 |
for ( k in 1: ncol(pathway)){ |
93 | 103 |
#k=2 |
94 | 104 |
if (length(intersect(rownames(zz),pathway[,k])!=0)){ |
95 |
- print(colnames(path)[k]) |
|
105 |
+ print(colnames(pathway)[k]) |
|
96 | 106 |
currentPathway_genes_list_common <- intersect(rownames(zz), currentPathway_genes<-pathway[,k]) |
97 | 107 |
currentPathway_genes_list_commonMatrix <- as.data.frame(zz[currentPathway_genes_list_common,]) |
98 | 108 |
rownames(currentPathway_genes_list_commonMatrix)<-currentPathway_genes_list_common |
... | ... |
@@ -100,27 +110,23 @@ for ( k in 1: ncol(pathway)){ |
100 | 110 |
names(v)[k]<-colnames(pathway)[k] |
101 | 111 |
} |
102 | 112 |
} |
103 |
-#PEAmatrix <- matrix( 0,nrow(DataMatrix),ncol(pathway)) |
|
104 |
-#rownames(PEAmatrix) <- as.factor(rownames(DataMatrix)) |
|
105 |
-#colnames(PEAmatrix) <- as.factor(colnames(pathway)) |
|
106 |
-#for (i in 1:length(v)){ |
|
107 |
-#PEAmatrix[v[[i]],i]<-zz[v[[i]],] |
|
108 |
-#} |
|
109 |
-#PEAmatrix<-PEAmatrix[which(rowSums(PEAmatrix) > 0),] |
|
110 | 113 |
return(v) |
111 | 114 |
} |
112 | 115 |
|
113 | 116 |
|
114 | 117 |
|
115 | 118 |
#' @title Get human KEGG pathway data and a gene expression matrix in order to obtain a matrix with the mean gene expression for only pathways given in input . |
116 |
-#' @description GE_matrix creates a matrix of mean gene expression for pathways given by the user. |
|