README.md
In liukf10/TEST: Disease-Drived Differential Proteins and Proteome-Wide Co-Expression Network Associated Analysis
DDPNA
DDPNA is a package for Disease-Drived Differential Proteins(DFP) and Proteome-Wide Co-expression Network Associated Analysis. The goal of DDPNA is offered a better methods to analyze omic data. The package is designed for proteomic data, but it is also fit for expression data in RNA-seq and metabolome. It is associated DFP and co-expression network module, and constructed a Mod-DFP network to remove lower connectivity DFP. The lower connectivity DFP is hard to get the key function in PPI and is more likely a false postive protein. The Mod-DFP network can also get DFP related proteins which is more likely a false negative protein. It provides the essential statisic analysis included t.test, ANOVA analysis to extract differential proteins. The package also provide some module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis. The co-expression network should constructed by other package or software.(WGCNA package or others)
Installation
You can install the developed version of DDPNA from github with:
library(devtools)
install_github("liukf10/DDPNA")
Example
This is a basic example which shows you how to solve a common problem:
library(DDPNA)
#>
data(Dforimpute)
#outlier sample remove and miss value impute
data <- Data_impute(Dforimpute, miss.value = 0, distmethod = "manhattan", plot = FALSE)
#> Warning in .NAnum.proteomic_data(data, miss.value = miss.value, verbose =
#> verbose): The sample ad059 have been removed
logD <- data$log2_value
rownames(logD) <- data$inf$ori.ID
#net is constructed by WGCNA blockwiseModules function.
##the parameter:datExpr = t(logD), TOMType = "unsigned", deepSplit = 4, minModuleSize = 17, reassignThreshold = 0.05, mergeCutHeight = 0.07
data(net)
Module <- Module_inf(net, data$inf)
oriData <- Dforimpute$LFQ
colnames(oriData) <- gsub("LFQ.intensity.","", colnames(oriData))
oriData <- oriData[,colnames(logD)]
rownames(oriData) <- Dforimpute$inf$ori.ID
group <- gsub("[0-9]+","", colnames(oriData))
up <- changedID(oriData, group, vs.set2 = "ad",vs.set1 = "ctl",
rank = "foldchange",anova = FALSE, Padj = "none",cutoff = 1,
datatype = "none",fctype = "up")
FCSenrich <- Module_Enrich(Module, up, datainf = rownames(oriData), coln="ori.ID")
FCSenrich <- FCSenrichplot(FCSenrich)
pos <- which(net$colors == 4)
Mod4_PCA <- modpcomp(logD[pos,], net$colors[pos], plot = TRUE, group = group)
Mod4 <- getmoduleHub(logD, Module, 4, coln = "ori.ID",adjustp = FALSE)
#> ####### PFN Calculation commences ########
#> permutation no.:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,
if (requireNamespace("MEGENA", quietly = TRUE)) {
try(library(MEGENA), silent = TRUE)
PMFG <- plot_subgraph(module = Mod4$degreeStat$gene,
hub = Mod4$hub, PFN = Mod4$PMFG,
node.default.color = "black",
gene.set = NULL, color.code = c("grey"),
show.legend = TRUE, label.hubs.only = TRUE,
hubLabel.col = "red", hubLabel.sizeProp = 0.5,
show.topn.hubs = 10, node.sizeProp = 13,
label.sizeProp = 13, label.scaleFactor = 10,
layout = "kamada.kawai")
print(PMFG)
}
#> Warning: package 'MEGENA' was built under R version 3.5.3
#> Loading required package: doParallel
#> Warning: package 'doParallel' was built under R version 3.5.3
#> Loading required package: foreach
#> Warning: package 'foreach' was built under R version 3.5.2
#> Loading required package: iterators
#> Warning: package 'iterators' was built under R version 3.5.2
#> Loading required package: parallel
#> Loading required package: igraph
#> Warning: package 'igraph' was built under R version 3.5.2
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
#> - # of genes: 57
#> - # of hubs: 2
#> - generating module subnetwork figure...
#> $pnet
#>
#> $node.features
#> node.lab id node.size node.shape X1
#> A8YXX5 A8YXX5 A8YXX5 20.718004 gene -0.18712467
#> C9J6D1 C9J6D1 C9J6D1 14.472676 gene 1.53982219
#> Q549N0 Q549N0 Q549N0 15.717805 gene -1.51355990
#> F6U1T9 F6U1T9 F6U1T9 11.197591 gene -0.27010470
#> Q9BRX8 Q9BRX8 Q9BRX8 17.747761 gene 0.04681198
#> P63241 P63241 P63241 20.071471 gene -1.41979759
#> F4ZW64 F4ZW64 F4ZW64 33.592772 hub -0.75209972
#> Q5TE61 Q5TE61 Q5TE61 17.747761 gene 0.74573368
#> A0A024R5K1 A0A024R5K1 A0A024R5K1 13.000000 gene 2.47463646
#> A0A0A0MQW0 A0A0A0MQW0 A0A0A0MQW0 11.197591 gene 0.36651549
#> J3QL05 J3QL05 J3QL05 15.717805 gene 0.21711202
#> B4DJS7 B4DJS7 B4DJS7 11.197591 gene -2.98445253
#> D0PNI1 D0PNI1 D0PNI1 35.019834 hub 0.67298444
#> Q9UII2 Q9UII2 Q9UII2 20.718004 gene -0.56872342
#> Q5T6H7 Q5T6H7 Q5T6H7 8.873881 gene -0.72650792
#> Q9H1E3 Q9H1E3 Q9H1E3 8.873881 gene -0.63453631
#> Q9UPR5 Q9UPR5 Q9UPR5 14.472676 gene 0.80125535
#> Q2M2I8 Q2M2I8 Q2M2I8 14.472676 gene -1.42631056
#> B4DMV0 B4DMV0 B4DMV0 11.197591 gene 1.51265134
#> B4DXL9 B4DXL9 B4DXL9 11.197591 gene -2.95384514
#> B2R6X6 B2R6X6 B2R6X6 14.472676 gene -0.52056360
#> X5DP03 X5DP03 X5DP03 8.873881 gene 1.42260445
#> B4DG89 B4DG89 B4DG89 8.873881 gene -1.24838797
#> B4DRD7 B4DRD7 B4DRD7 11.197591 gene -0.86065616
#> Q6IBS0 Q6IBS0 Q6IBS0 11.197591 gene -1.96268709
#> B4DYL8 B4DYL8 B4DYL8 11.197591 gene -1.17234731
#> Q6IBG8 Q6IBG8 Q6IBG8 15.717805 gene 1.64645826
#> B7Z879 B7Z879 B7Z879 13.000000 gene 1.80133504
#> B5MCX3 B5MCX3 B5MCX3 8.873881 gene -1.80168220
#> Q14289 Q14289 Q14289 17.747761 gene -1.30884082
#> Q03252 Q03252 Q03252 16.796386 gene -1.69982688
#> K7EM56 K7EM56 K7EM56 13.000000 gene -0.37398999
#> H7BZH9 H7BZH9 H7BZH9 8.873881 gene 2.63269815
#> A4D177 A4D177 A4D177 8.873881 gene 1.42638878
#> A0A024RD97 A0A024RD97 A0A024RD97 8.873881 gene -2.49367198
#> B7ZB67 B7ZB67 B7ZB67 11.197591 gene 0.93743802
#> A0A024R5C4 A0A024R5C4 A0A024R5C4 5.598795 gene -0.57320377
#> A0A140VKC4 A0A140VKC4 A0A140VKC4 11.197591 gene 0.49181581
#> K7EJM5 K7EJM5 K7EJM5 11.197591 gene -0.01778775
#> Q13636 Q13636 Q13636 5.598795 gene 0.88193523
#> B5MCT8 B5MCT8 B5MCT8 15.717805 gene -2.01167037
#> Q5U077 Q5U077 Q5U077 11.197591 gene -0.41881130
#> B1AKK2 B1AKK2 B1AKK2 8.873881 gene 1.10535902
#> A8K3C3 A8K3C3 A8K3C3 11.197591 gene 1.97980407
#> V9HW71 V9HW71 V9HW71 13.000000 gene -2.47192823
#> B3KSG3 B3KSG3 B3KSG3 19.368649 gene -0.06696900
#> Q16778 Q16778 Q16778 11.197591 gene -1.08630819
#> O95721 O95721 O95721 15.717805 gene -0.88958071
#> Q6IBP2 Q6IBP2 Q6IBP2 0.000000 gene 3.17979205
#> E7ERH2 E7ERH2 E7ERH2 0.000000 gene 0.33261079
#> P07814 P07814 P07814 13.000000 gene 2.50614142
#> F5H5G1 F5H5G1 F5H5G1 0.000000 gene -2.20549359
#> O14617 O14617 O14617 8.873881 gene -2.83743501
#> B4DY16 B4DY16 B4DY16 14.472676 gene 0.87117234
#> V9HW90 V9HW90 V9HW90 8.873881 gene -2.34971521
#> A0A024R3W7 A0A024R3W7 A0A024R3W7 8.873881 gene -1.89151846
#> B4DJT9 B4DJT9 B4DJT9 0.000000 gene -2.44359715
#> X2
#> A8YXX5 -1.08635317
#> C9J6D1 0.52779930
#> Q549N0 0.92776157
#> F6U1T9 -2.04660948
#> Q9BRX8 -0.30950627
#> P63241 -0.66711376
#> F4ZW64 0.77677929
#> Q5TE61 0.96541133
#> A0A024R5K1 0.91565218
#> A0A0A0MQW0 2.75472410
#> J3QL05 1.70786276
#> B4DJS7 -0.92748681
#> D0PNI1 0.24754478
#> Q9UII2 0.26011422
#> Q5T6H7 -0.17034334
#> Q9H1E3 1.44150804
#> Q9UPR5 -1.30979928
#> Q2M2I8 -0.16927065
#> B4DMV0 -1.05186603
#> B4DXL9 1.23859689
#> B2R6X6 -1.02711993
#> X5DP03 -0.13561015
#> B4DG89 1.89363149
#> B4DRD7 -2.26626875
#> Q6IBS0 1.45334422
#> B4DYL8 1.29844017
#> Q6IBG8 0.99255434
#> B7Z879 1.68146574
#> B5MCX3 -2.63148809
#> Q14289 -1.55457329
#> Q03252 0.47777578
#> K7EM56 2.35389232
#> H7BZH9 2.17999959
#> A4D177 -0.56451887
#> A0A024RD97 -1.09807576
#> B7ZB67 1.44040412
#> A0A024R5C4 1.82940463
#> A0A140VKC4 -0.52123752
#> K7EJM5 -2.10872968
#> Q13636 -1.91424337
#> B5MCT8 -1.55556843
#> Q5U077 -1.65081811
#> B1AKK2 0.46386711
#> A8K3C3 -0.55528674
#> V9HW71 -1.78328201
#> B3KSG3 1.10717494
#> Q16778 2.37304288
#> O95721 -0.89908789
#> Q6IBP2 0.47497015
#> E7ERH2 4.02546319
#> P07814 1.38159138
#> F5H5G1 -3.78931307
#> O14617 0.66649894
#> B4DY16 -0.91352899
#> V9HW90 -0.81186041
#> A0A024R3W7 -0.02101455
#> B4DJT9 2.05666008
liukf10/TEST documentation built on May 20, 2019, 12:59 a.m.
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DDPNA
DDPNA is a package for Disease-Drived Differential Proteins(DFP) and Proteome-Wide Co-expression Network Associated Analysis. The goal of DDPNA is offered a better methods to analyze omic data. The package is designed for proteomic data, but it is also fit for expression data in RNA-seq and metabolome. It is associated DFP and co-expression network module, and constructed a Mod-DFP network to remove lower connectivity DFP. The lower connectivity DFP is hard to get the key function in PPI and is more likely a false postive protein. The Mod-DFP network can also get DFP related proteins which is more likely a false negative protein. It provides the essential statisic analysis included t.test, ANOVA analysis to extract differential proteins. The package also provide some module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis. The co-expression network should constructed by other package or software.(WGCNA package or others)
Installation
You can install the developed version of DDPNA from github with:
library(devtools)
install_github("liukf10/DDPNA")
Example
This is a basic example which shows you how to solve a common problem:
library(DDPNA)
#>
data(Dforimpute)
#outlier sample remove and miss value impute
data <- Data_impute(Dforimpute, miss.value = 0, distmethod = "manhattan", plot = FALSE)
#> Warning in .NAnum.proteomic_data(data, miss.value = miss.value, verbose =
#> verbose): The sample ad059 have been removed
logD <- data$log2_value
rownames(logD) <- data$inf$ori.ID
#net is constructed by WGCNA blockwiseModules function.
##the parameter:datExpr = t(logD), TOMType = "unsigned", deepSplit = 4, minModuleSize = 17, reassignThreshold = 0.05, mergeCutHeight = 0.07
data(net)
Module <- Module_inf(net, data$inf)
oriData <- Dforimpute$LFQ
colnames(oriData) <- gsub("LFQ.intensity.","", colnames(oriData))
oriData <- oriData[,colnames(logD)]
rownames(oriData) <- Dforimpute$inf$ori.ID
group <- gsub("[0-9]+","", colnames(oriData))
up <- changedID(oriData, group, vs.set2 = "ad",vs.set1 = "ctl",
rank = "foldchange",anova = FALSE, Padj = "none",cutoff = 1,
datatype = "none",fctype = "up")
FCSenrich <- Module_Enrich(Module, up, datainf = rownames(oriData), coln="ori.ID")
FCSenrich <- FCSenrichplot(FCSenrich)
pos <- which(net$colors == 4)
Mod4_PCA <- modpcomp(logD[pos,], net$colors[pos], plot = TRUE, group = group)
Mod4 <- getmoduleHub(logD, Module, 4, coln = "ori.ID",adjustp = FALSE)
#> ####### PFN Calculation commences ########
#> permutation no.:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,
if (requireNamespace("MEGENA", quietly = TRUE)) {
try(library(MEGENA), silent = TRUE)
PMFG <- plot_subgraph(module = Mod4$degreeStat$gene,
hub = Mod4$hub, PFN = Mod4$PMFG,
node.default.color = "black",
gene.set = NULL, color.code = c("grey"),
show.legend = TRUE, label.hubs.only = TRUE,
hubLabel.col = "red", hubLabel.sizeProp = 0.5,
show.topn.hubs = 10, node.sizeProp = 13,
label.sizeProp = 13, label.scaleFactor = 10,
layout = "kamada.kawai")
print(PMFG)
}
#> Warning: package 'MEGENA' was built under R version 3.5.3
#> Loading required package: doParallel
#> Warning: package 'doParallel' was built under R version 3.5.3
#> Loading required package: foreach
#> Warning: package 'foreach' was built under R version 3.5.2
#> Loading required package: iterators
#> Warning: package 'iterators' was built under R version 3.5.2
#> Loading required package: parallel
#> Loading required package: igraph
#> Warning: package 'igraph' was built under R version 3.5.2
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
#> - # of genes: 57
#> - # of hubs: 2
#> - generating module subnetwork figure...
#> $pnet
#>
#> $node.features
#> node.lab id node.size node.shape X1
#> A8YXX5 A8YXX5 A8YXX5 20.718004 gene -0.18712467
#> C9J6D1 C9J6D1 C9J6D1 14.472676 gene 1.53982219
#> Q549N0 Q549N0 Q549N0 15.717805 gene -1.51355990
#> F6U1T9 F6U1T9 F6U1T9 11.197591 gene -0.27010470
#> Q9BRX8 Q9BRX8 Q9BRX8 17.747761 gene 0.04681198
#> P63241 P63241 P63241 20.071471 gene -1.41979759
#> F4ZW64 F4ZW64 F4ZW64 33.592772 hub -0.75209972
#> Q5TE61 Q5TE61 Q5TE61 17.747761 gene 0.74573368
#> A0A024R5K1 A0A024R5K1 A0A024R5K1 13.000000 gene 2.47463646
#> A0A0A0MQW0 A0A0A0MQW0 A0A0A0MQW0 11.197591 gene 0.36651549
#> J3QL05 J3QL05 J3QL05 15.717805 gene 0.21711202
#> B4DJS7 B4DJS7 B4DJS7 11.197591 gene -2.98445253
#> D0PNI1 D0PNI1 D0PNI1 35.019834 hub 0.67298444
#> Q9UII2 Q9UII2 Q9UII2 20.718004 gene -0.56872342
#> Q5T6H7 Q5T6H7 Q5T6H7 8.873881 gene -0.72650792
#> Q9H1E3 Q9H1E3 Q9H1E3 8.873881 gene -0.63453631
#> Q9UPR5 Q9UPR5 Q9UPR5 14.472676 gene 0.80125535
#> Q2M2I8 Q2M2I8 Q2M2I8 14.472676 gene -1.42631056
#> B4DMV0 B4DMV0 B4DMV0 11.197591 gene 1.51265134
#> B4DXL9 B4DXL9 B4DXL9 11.197591 gene -2.95384514
#> B2R6X6 B2R6X6 B2R6X6 14.472676 gene -0.52056360
#> X5DP03 X5DP03 X5DP03 8.873881 gene 1.42260445
#> B4DG89 B4DG89 B4DG89 8.873881 gene -1.24838797
#> B4DRD7 B4DRD7 B4DRD7 11.197591 gene -0.86065616
#> Q6IBS0 Q6IBS0 Q6IBS0 11.197591 gene -1.96268709
#> B4DYL8 B4DYL8 B4DYL8 11.197591 gene -1.17234731
#> Q6IBG8 Q6IBG8 Q6IBG8 15.717805 gene 1.64645826
#> B7Z879 B7Z879 B7Z879 13.000000 gene 1.80133504
#> B5MCX3 B5MCX3 B5MCX3 8.873881 gene -1.80168220
#> Q14289 Q14289 Q14289 17.747761 gene -1.30884082
#> Q03252 Q03252 Q03252 16.796386 gene -1.69982688
#> K7EM56 K7EM56 K7EM56 13.000000 gene -0.37398999
#> H7BZH9 H7BZH9 H7BZH9 8.873881 gene 2.63269815
#> A4D177 A4D177 A4D177 8.873881 gene 1.42638878
#> A0A024RD97 A0A024RD97 A0A024RD97 8.873881 gene -2.49367198
#> B7ZB67 B7ZB67 B7ZB67 11.197591 gene 0.93743802
#> A0A024R5C4 A0A024R5C4 A0A024R5C4 5.598795 gene -0.57320377
#> A0A140VKC4 A0A140VKC4 A0A140VKC4 11.197591 gene 0.49181581
#> K7EJM5 K7EJM5 K7EJM5 11.197591 gene -0.01778775
#> Q13636 Q13636 Q13636 5.598795 gene 0.88193523
#> B5MCT8 B5MCT8 B5MCT8 15.717805 gene -2.01167037
#> Q5U077 Q5U077 Q5U077 11.197591 gene -0.41881130
#> B1AKK2 B1AKK2 B1AKK2 8.873881 gene 1.10535902
#> A8K3C3 A8K3C3 A8K3C3 11.197591 gene 1.97980407
#> V9HW71 V9HW71 V9HW71 13.000000 gene -2.47192823
#> B3KSG3 B3KSG3 B3KSG3 19.368649 gene -0.06696900
#> Q16778 Q16778 Q16778 11.197591 gene -1.08630819
#> O95721 O95721 O95721 15.717805 gene -0.88958071
#> Q6IBP2 Q6IBP2 Q6IBP2 0.000000 gene 3.17979205
#> E7ERH2 E7ERH2 E7ERH2 0.000000 gene 0.33261079
#> P07814 P07814 P07814 13.000000 gene 2.50614142
#> F5H5G1 F5H5G1 F5H5G1 0.000000 gene -2.20549359
#> O14617 O14617 O14617 8.873881 gene -2.83743501
#> B4DY16 B4DY16 B4DY16 14.472676 gene 0.87117234
#> V9HW90 V9HW90 V9HW90 8.873881 gene -2.34971521
#> A0A024R3W7 A0A024R3W7 A0A024R3W7 8.873881 gene -1.89151846
#> B4DJT9 B4DJT9 B4DJT9 0.000000 gene -2.44359715
#> X2
#> A8YXX5 -1.08635317
#> C9J6D1 0.52779930
#> Q549N0 0.92776157
#> F6U1T9 -2.04660948
#> Q9BRX8 -0.30950627
#> P63241 -0.66711376
#> F4ZW64 0.77677929
#> Q5TE61 0.96541133
#> A0A024R5K1 0.91565218
#> A0A0A0MQW0 2.75472410
#> J3QL05 1.70786276
#> B4DJS7 -0.92748681
#> D0PNI1 0.24754478
#> Q9UII2 0.26011422
#> Q5T6H7 -0.17034334
#> Q9H1E3 1.44150804
#> Q9UPR5 -1.30979928
#> Q2M2I8 -0.16927065
#> B4DMV0 -1.05186603
#> B4DXL9 1.23859689
#> B2R6X6 -1.02711993
#> X5DP03 -0.13561015
#> B4DG89 1.89363149
#> B4DRD7 -2.26626875
#> Q6IBS0 1.45334422
#> B4DYL8 1.29844017
#> Q6IBG8 0.99255434
#> B7Z879 1.68146574
#> B5MCX3 -2.63148809
#> Q14289 -1.55457329
#> Q03252 0.47777578
#> K7EM56 2.35389232
#> H7BZH9 2.17999959
#> A4D177 -0.56451887
#> A0A024RD97 -1.09807576
#> B7ZB67 1.44040412
#> A0A024R5C4 1.82940463
#> A0A140VKC4 -0.52123752
#> K7EJM5 -2.10872968
#> Q13636 -1.91424337
#> B5MCT8 -1.55556843
#> Q5U077 -1.65081811
#> B1AKK2 0.46386711
#> A8K3C3 -0.55528674
#> V9HW71 -1.78328201
#> B3KSG3 1.10717494
#> Q16778 2.37304288
#> O95721 -0.89908789
#> Q6IBP2 0.47497015
#> E7ERH2 4.02546319
#> P07814 1.38159138
#> F5H5G1 -3.78931307
#> O14617 0.66649894
#> B4DY16 -0.91352899
#> V9HW90 -0.81186041
#> A0A024R3W7 -0.02101455
#> B4DJT9 2.05666008
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