update

ScriptAlleleNetwork.r

@@ -7,20 +7,63 @@ library("igraph")
 
 ##### AlleleNetwork Alternative ###############################################
 
+Net.abr = read_delim("Net.abr.net", col_names = FALSE, delim = "\t")
+Net.bac = read_delim("Net.bac.net", col_names = FALSE, delim = "\t")
+Net.rel = read_delim("Net.rel.net", col_names = FALSE, delim = "\t")
 
+colnames(Net.abr) = c("Source", "Target", "value")
+colnames(Net.bac) = c("Source", "Target", "value")
+colnames(Net.rel) = c("Source", "Target", "value")
 
-Net.abr.graph = graph_from_edgelist(as.matrix(Net.abr), directed = FALSE)
-Net.bac.graph = graph_from_edgelist(as.matrix(Net.bac), directed = FALSE)
-Net.rel.graph = graph_from_edgelist(as.matrix(Net.rel), directed = FALSE)
+Net.abr.matrix = Net.abr %>% filter(!is.na(Target), !is.na(Source)) %>% spread(Source, value, fill = 0)
+Net.bac.matrix = Net.bac %>% filter(!is.na(Target), !is.na(Source)) %>% spread(Source, value, fill = 0)
+Net.rel.matrix = Net.rel %>% filter(!is.na(Target), !is.na(Source)) %>% spread(Source, value, fill = 0)
 
-Net.abr.matrix = as_adjacency_matrix(Net.abr.graph)
-Net.abr.mcl = mcl(Net.abr.matrix, addLoops = TRUE, allow1 = TRUE)
+tmp = Net.abr.matrix$Target
+Net.abr.matrix = Net.abr.matrix[, -1]
+rownames(Net.abr.matrix) = tmp
+
+tmp = Net.bac.matrix$Target
+Net.bac.matrix = Net.bac.matrix[, -1]
+rownames(Net.bac.matrix) = tmp
+
+
+tmp = Net.rel.matrix$Target
+Net.rel.matrix = Net.rel.matrix[, -1]
+rownames(Net.rel.matrix) = tmp
+
+
+Net.abr.graph = graph_from_adjacency_matrix(
+  as.matrix(Net.abr.matrix),
+  mode = "upper",
+  weighted = TRUE,
+  diag = FALSE
+)
+Net.bac.graph = graph_from_adjacency_matrix(
+  as.matrix(Net.abr.matrix),
+  mode = "upper",
+  weighted = TRUE,
+  diag = FALSE
+)
+Net.rel.graph = graph_from_adjacency_matrix(
+  as.matrix(Net.abr.matrix),
+  mode = "upper",
+  weighted = TRUE,
+  diag = FALSE
+)
+
+################  Using MCL algorithm for clustering classification #########################
+Net.abr.mcl = mcl(as_adjacency_matrix(Net.abr.graph),
+                  addLoops = TRUE,
+                  allow1 = TRUE)
+Net.bac.mcl = mcl(as_adjacency_matrix(Net.bac.graph),
+                  addLoops = TRUE,
+                  allow1 = TRUE)
+Net.rel.mcl = mcl(as_adjacency_matrix(Net.rel.graph),
+                  addLoops = TRUE,
+                  allow1 = TRUE)
 
-Net.bac.matrix = as_adjacency_matrix(Net.bac.graph)
-Net.bac.mcl = mcl(Net.bac.matrix, addLoops = TRUE, allow1 = TRUE)
 
-Net.rel.matrix = as_adjacency_matrix(Net.rel.graph)
-Net.rel.mcl = mcl(Net.rel.matrix, addLoops = TRUE, allow1 = TRUE)
 
 
 Net.abr.cl = data_frame(
@@ -42,10 +85,44 @@ Net.rel.cl = data_frame(
 Net.all.cl = bind_rows(Net.abr.cl,
                        Net.bac.cl,
                        Net.rel.cl)
+############ END MCL algorithm for clustering classification   ################################
+
+############ Clustering by Components (Calculate the maximal (weakly or strongly) connected components of a graph) #############
+############ This is an alternative if MCL fails because the size of the AlleleNetwork ####################
+
+Net.abr.comp = components(Net.abr.graph)
+Net.bac.comp = components(Net.bac.graph)
+Net.rel.comp = components(Net.rel.graph)
+
+Net.abr.cl = data_frame(
+  Gene = rownames(as.data.frame(Net.abr.comp$membership)),
+  Cluster = Net.abr.comp$membership,
+  DataSet = "abr"
+)
+Net.bac.cl = data_frame(
+  Gene = rownames(as.data.frame(Net.bac.comp$membership)),
+  Cluster = Net.bac.comp$membership,
+  DataSet = "bac"
+)
+Net.rel.cl = data_frame(
+  Gene = rownames(as.data.frame(Net.rel.comp$membership)),
+  Cluster = Net.rel.comp$membership,
+  DataSet = "rel"
+)
+Net.all.cl = bind_rows(Net.abr.cl,
+                       Net.bac.cl,
+                       Net.rel.cl)
+
+########### END of Clustering by components ################################################
+
 
-Connections.abr = bind_rows(Net.abr %>% select(X = Gen, N), Net.abr %>% select(X = siguienteG, N)) %>% group_by(X) %>% summarise(N = sum(N))
-Connections.bac = bind_rows(Net.bac %>% select(X = Gen, N), Net.bac %>% select(X = siguienteG, N)) %>% group_by(X) %>% summarise(N = sum(N))
-Connections.rel = bind_rows(Net.rel %>% select(X = Gen, N), Net.rel %>% select(X = siguienteG, N)) %>% group_by(X) %>% summarise(N = sum(N))
+
+Connections.abr = bind_rows(Net.abr %>% select(X = Target, value),
+                            Net.abr %>% select(X = Source, value)) %>% group_by(X) %>% summarise(N = sum(value))
+Connections.bac = bind_rows(Net.bac %>% select(X = Target, value),
+                            Net.bac %>% select(X = Source, value)) %>% group_by(X) %>% summarise(N = sum(value))
+Connections.rel = bind_rows(Net.rel %>% select(X = Target, value),
+                            Net.rel %>% select(X = Source, value)) %>% group_by(X) %>% summarise(N = sum(value))
 Connections.all = bind_rows(Connections.abr, Connections.bac, Connections.rel)
 colnames(Connections.all) = c("Gene", "Connections")
 
@@ -83,13 +160,19 @@ Full.table = Full.table %>% separate(Sample, c("Sample", "DataSet", "kk"), sep =
                                        "\\.") %>% select(-kk) %>% full_join(., Net.all.cl)
 Full.table = Full.table %>% left_join(., Connections.all)
 Full.table$Connections[is.na(Full.table$Connections)] = 0
-reads = read_table("../count.reads",col_names = FALSE)
-colnames(reads) = c("TotalReads","Sample")
-reads$Sample = gsub(".R1.fastq","",reads$Sample)
-reads$TotalReads = reads$TotalReads/4
-Full.table = left_join(Full.table, Nreads)
+reads = read_delim("../Nreads.txt", col_names = TRUE, delim = ",")   #### File with the reads count per Sample
+
+Full.table = left_join(Full.table, reads)
 Full.table = Full.table %>% mutate(FinalClust = ifelse(is.na(Cluster), Gene, paste(DataSet, Cluster, sep =
                                                                                      "")))
 RepresentativesOfCluster = Full.table %>% group_by(FinalClust, Gene) %>% summarise(conn = max(Connections)) %>% group_by(FinalClust) %>% mutate(top = max(conn)) %>% filter(conn == top) %>% group_by(FinalClust) %>% summarise(Representative = first(Gene))
 Full.table = full_join(Full.table, RepresentativesOfCluster)
+
 ##### END Join AlleleNetwork and Data abundance ############
+
+
+
+Full.table = Full.table %>% mutate(RPKM = RPK * 1e6 / TotalReads,
+                                   UpM = Uniq * 1e6 / TotalReads)
+
+