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Dyson09Dyson09
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Results Exploration update
minor fix in the Results Exploration vignette
1 parent 5c0b483 commit 4c2e2fc

22 files changed

+531
-329
lines changed

‎.Rhistory‎

Lines changed: 86 additions & 86 deletions
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@@ -1,89 +1,3 @@
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Entrez_gene_ids_sign <- res$ENTREZID[(res$FDR < 0.01) & (res$logFC > 4)]
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# Downregulated:
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#Entrez_gene_ids_sign <- res$ENTREZID[(res$FDR < 0.01) & (res$logFC < -4)]
4-
# General disruption:
5-
#Entrez_gene_ids_sign <- res$ENTREZID[(res$FDR < 0.01) & (abs(res$logFC) > 4)]
6-
Entrez_gene_ids_sign <- Entrez_gene_ids_sign[!is.na(Entrez_gene_ids_sign)]
7-
Entrez_gene_ids_sign <- sort(as.character(Entrez_gene_ids_sign))
8-
length(Entrez_gene_ids_sign)
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# Write IDs to file
10-
write.table(Entrez_gene_ids_background, file="Entrez_gene_ids_background.txt",
11-
col.names=F, quote=F, row.names=F)
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write.table(Entrez_gene_ids_sign, file="Entrez_gene_ids_sign.txt",
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col.names=F, quote=F, row.names=F)
14-
# Load data for one individual sample
15-
dat_lengths <- read.table("GSM1545545_JMS9-P8c.txt", header=T, sep="\t")
16-
head(dat_lengths)
17-
# Sort based on Entrez IDs
18-
dat_lengths_sorted <- dat_lengths[sort(as.character(dat_lengths$EntrezID),index.return=T)$ix, ]
19-
# Sort the results based on Entrez IDs
20-
res_sorted <- res[sort(as.character(res$ENTREZID),index.return=T)$ix, ]
21-
# Select the ones that are in the results table
22-
dat_lengths_sorted <- dat_lengths_sorted[dat_lengths_sorted$EntrezID %in% res$ENTREZID, ]
23-
# Check if order and amount of Entrez IDs is correct
24-
all(dat_lengths_sorted$EntrezID == res_sorted$ENTREZID)
25-
# Add gene lengths to results
26-
res_sorted$Lengths <- dat_lengths_sorted$GeneLength
27-
head(res_sorted)
28-
# GO analysis
29-
gsa <- goana(sort(Entrez_gene_ids_sign),
30-
species = "Mm",
31-
universe = sort(Entrez_gene_ids_background),
32-
trend = res_sorted$Lengths)
33-
head(gsa)
34-
# Sort results and calculate FDR
35-
gsa <- gsa[sort(gsa$P.DE,index.return=T)$ix,]
36-
gsa$P.DE.adj <- p.adjust(gsa$P.DE, n=nrow(gsa), method="BH")
37-
head(gsa)
38-
# Write results
39-
write.table(gsa, file="GSA_analysis_all.txt", col.names = T,
40-
row.names = F, quote = F, sep="\t")
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View(gsa)
42-
?parlapply
43-
??parlapply
44-
?mblogit
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??mblogit
46-
library(mclogit)
47-
install.packages("mclogit")
48-
library(mclogit)
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?mblogit
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load("~/MultiData.RData")
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# Fit the model:
52-
MultiData.mblogit <- mblogit(formula = Bin ~ Genotype,
53-
random = ~1|BC,
54-
weights = count,
55-
data = MultiData)
56-
?mclogit
57-
?VGAM::zibinomial
58-
install.packages(VGAM)
59-
install.packages("VGAM")
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?VGAM::zibinomial
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?VGAM::zimultinomial
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??zimultinomial
63-
MAGE::LOItest_logreg(c(5, 8, 10, 3, 5, 6, 23), c(8, 8, 6, 4, 4, 10, 0),
64-
c(5, 0, 0, 3, 5, 1, 23), c(1, 8, 6, 2, 0, 10, 0))
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?mclogit
66-
?mblogit
67-
load("~/SCRIPT7_DI_results_genes.RData")
68-
View(DI_results_genes)
69-
load("~/SCRIPT7_DI_results_SNP.RData")
70-
View(DI_results_SNP)
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load("~/SCRIPT7_DI_results_genes.RData")
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View(DI_results_genes)
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load("~/SCRIPT7_DI_results_genes.RData")
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View(DI_results_genes)
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load("~/SCRIPT7_DI_results_SNP.RData")
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View(DI_results_SNP)
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View(DI_results_SNP)
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load("~/SignTab2.RData")
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View(SignTab)
80-
SignTabClone <- SignTab
81-
load("~/SignTab2_New.RData")
82-
View(SignTabClone)
83-
View(SignTab)
84-
sum(SignTab$p_DI_FDR < 0.05)
85-
sum(SignTabClone$p_DI_FDR < 0.05)
86-
SignTab$Gene[SignTab$p_DI_FDR < 0.05]
871
SignTabClone$Gene[SignTabClone$p_DI_FDR < 0.05]
882
View(SignTab)
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View(SignTabClone)
@@ -510,3 +424,89 @@ View(GENEdata_Table3SS)
510424
GENEdata_Table3SS <- GENEdata_Table3[, c(-30:-20) ]
511425
?glm
512426
?lm
427+
?merge
428+
CurDF <- (data_allchr_control[[chr]][[HelpTab$SNPname[j]]])
429+
SnpsTillNow <- 0
430+
+
431+
1
432+
SnpsTillNow
433+
load("~/MAGEgenotyper/ARAS.RData")
434+
load("~/MAGEgenotyper/BRBS.RData")
435+
View(ara1)
436+
SelectedSamps <- ara1$TCGA_sample_ID[1:5]
437+
SelectedSamps <- ara1$TCGA_sample_ID[1:5]
438+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "chr", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
439+
View(ara1)
440+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "chr", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
441+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "chr", "TCGA_sample_ID", "A", "T", "C")]
442+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("C", "G", "ref", "var", "total")]
443+
load("~/MAGEgenotyper/ARAS.RData")
444+
load("~/MAGEgenotyper/ARAS.RData")
445+
load("~/MAGEgenotyper/BRBS.RData")
446+
SelectedSamps <- ara1$TCGA_sample_ID[1:5]
447+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
448+
brb1 <- brb1[brb1$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
449+
colnames(brb1) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
450+
ara2 <- ara2[ara2$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
451+
brb2 <- brb2[brb2$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
452+
colnames(brb2) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
453+
ara3 <- ara3[ara3$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
454+
brb3 <- brb3[brb3$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
455+
colnames(brb3) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
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View(ara1)
457+
View(ara2)
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View(ara3)
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View(brb1)
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View(brb2)
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View(brb3)
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load("~/MAGEgenotyper/ARAS.RData")
463+
load("~/MAGEgenotyper/ARAS.RData")
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load("~/MAGEgenotyper/BRBS.RData")
465+
SelectedSamps <- ara1$TCGA_sample_ID[1:5]
466+
ara1 <- ara1[ara1$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
467+
brb1 <- brb1[brb1$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
468+
colnames(brb1) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
469+
ara2 <- ara2[ara2$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
470+
brb2 <- brb2[brb2$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
471+
colnames(brb2) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
472+
ara3 <- ara3[ara3$TCGA_sample_ID %in% SelectedSamps, c("dbSNP_ref", "gene", "TCGA_sample_ID", "A", "T", "C", "G", "ref", "var", "total")]
473+
brb3 <- brb3[brb3$TCGA_sample_ID %in% SelectedSamps, c("SNPname", "gene", "NEW", "ref_count", "var_count", "total")]
474+
colnames(brb3) <- c("SNP_ID", "gene", "Sample_ID", "ref_count", "var_count", "total")
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View(ara1)
476+
View(ara2)
477+
View(ara3)
478+
View(brb1)
479+
View(brb2)
480+
View(brb3)
481+
HelpTab_ReConstruct <- data.frame("dbSNP_ref"=character(), "gene"=character())
482+
View(ara2)
483+
?read.csv
484+
21:21
485+
NC <- 10
486+
NS <- 50
487+
NS <- length(AllLocs)
488+
NS <- 50
489+
spl <- c(0, cumsum(rep(floor(NS/NC),NC)+c(rep(1,NS-floor(NS/NC)*NC),
490+
rep(0,NC-NS+floor(NS/NC)*NC)))) # Helps in splitting input data
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spl
492+
NS <- 51
493+
spl <- c(0, cumsum(rep(floor(NS/NC),NC)+c(rep(1,NS-floor(NS/NC)*NC),
494+
rep(0,NC-NS+floor(NS/NC)*NC)))) # Helps in splitting input data
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spl
496+
NS <- 49
497+
spl <- c(0, cumsum(rep(floor(NS/NC),NC)+c(rep(1,NS-floor(NS/NC)*NC),
498+
rep(0,NC-NS+floor(NS/NC)*NC)))) # Helps in splitting input data
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spl
500+
NC <- 40
501+
NS <- 8592
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spl
503+
spl <- c(0, cumsum(rep(floor(NS/NC),NC)+c(rep(1,NS-floor(NS/NC)*NC),
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rep(0,NC-NS+floor(NS/NC)*NC)))) # Helps in splitting input data
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spl
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for(bla in 1:1){
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next
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print("bla")
509+
}
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for(bla in 1:1){
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print("bla")
512+
}

‎.Rproj.user/67A8F655/pcs/files-pane.pper‎

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{
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"sortOrder": [
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{
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"columnIndex": 2,
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"ascending": true
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},
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{
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"columnIndex": 4,
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},
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{
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"columnIndex": 2,
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"ascending": true
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},
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{
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"columnIndex": 3,
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"ascending": false
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}
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],
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"path": "~/MAGEgenotyper/maelstRom/R"
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"path": "~/MAGEgenotyper/maelstRom"
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}

‎.Rproj.user/67A8F655/pcs/source-pane.pper‎

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{
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"activeTab": -1,
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"activeTab": 2,
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"activeTabSourceWindow0": 0
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}

‎.Rproj.user/67A8F655/pcs/windowlayoutstate.pper‎

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{
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"topwindowstate": "NORMAL",
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"splitterpos": 681,
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"topwindowstate": "NORMAL",
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‎.Rproj.user/67A8F655/persistent-state‎

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‎.Rproj.user/67A8F655/sources/prop/INDEX‎

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~%2FData%20(2)%2FQC%20Report%20EColi%20lrp%20experiment%2Findex.html="B398ADFB"
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~%2FData_P9%2FData%2FEdgeR_output.txt="7BEE640D"
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~%2FData_P9%2FData%2FGene_Set_Analysis_X.Rmd="8D9432DA"
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~%2FGS_scripts%2FPermutatedAnonymizedAnalysis.R="533AD841"
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~%2FGS_scripts%2FScramblerScript1.R="8A0AAA97"
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~%2FGS_scripts%2FScramblerScript1_X.R="16746948"
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~%2FGS_scripts%2FScramblerScript2.R="618B7A95"
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~%2FGS_scripts%2FScramblerScript2_X.R="7D8A4EB3"
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~%2FGS_scripts%2FScramblerScriptSupport_SNPidCOUNTER.R="65184716"
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~%2FGS_scripts%2FScriptScrambleAnalysis.R="EBC2144F"
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~%2FGS_scripts%2FXCHROM_PermutatedAnonymizedAnalysis.R="BF0DAB54"
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~%2FGerben%2FGerbVignette.Rmd="E713191A"
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~%2FGerben%2FGerbenScript.R="3149ABAF"
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~%2FGerben%2FModel_Sloppy.Rmd="108370A5"
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~%2FMAGEgenotyper%2FMAGESCRIPT2_ControlGenotyping.R="86C7E1EA"
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~%2FMAGEgenotyper%2FMAGESCRIPT5_dAD.R="71773C79"
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~%2FMAGEgenotyper%2FMAGE_DP_Temp_Copy.R="2BA0B1B5"
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~%2FMAGEgenotyper%2FMethylatieChecker_SNP.R="72163D0F"
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~%2FMAGEgenotyper%2FMyBetaBinom%2FR%2FRcppExports.R="A5E12766"
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~%2FMAGEgenotyper%2FMyReAnnotter.R="D95BCDB3"
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~%2FMAGEgenotyper%2FNewGenoAndAnnotation.R="11BD2F2F"
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~%2FMAGEgenotyper%2FmaelstRom%2FEMfit_betabinom_popcombXX.R="243A96FF"
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~%2FMAGEgenotyper%2FmaelstRom%2FLicense="1233750C"
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~%2FMAGEgenotyper%2FmaelstRom%2FNAMESPACE="8FFAE7E4"
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~%2FMAGEgenotyper%2FmaelstRom%2FR%2FAD_correlator.R="E819CD21"
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~%2FMAGEgenotyper%2FmaelstRom%2FR%2FAdvancedFitter.R="C4CC033A"
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~%2FMAGEgenotyper%2FmaelstRom%2FR%2FAdvancedPvalcomb.R="C81C353A"
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~%2FMAGEgenotyper%2FmaelstRom%2FR%2FAllelicMeta_est.R="48F82AD8"

‎.Rproj.user/shared/notebooks/paths‎

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C:/Users/Cedric/Desktop/PhD archief/GerbenAnalyse/GerbVignette.Rmd="3AEA347B"
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C:/Users/Cedric/Documents/BayesianCourse/TweedeOefeningen.R="18BFF170"
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C:/Users/Cedric/Documents/Brol.Rmd="3EC6765D"
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C:/Users/Cedric/Documents/GS_scripts/PermutatedAnonymizedAnalysis.R="62AE7069"
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C:/Users/Cedric/Documents/GS_scripts/ScramblerScript1.R="74E47F11"
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C:/Users/Cedric/Documents/GS_scripts/ScramblerScript1_X.R="164FFF97"
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C:/Users/Cedric/Documents/GS_scripts/ScramblerScript2.R="718F7B14"
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C:/Users/Cedric/Documents/GS_scripts/ScramblerScript2_X.R="68930790"
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C:/Users/Cedric/Documents/GS_scripts/ScramblerScriptSupport_SNPidCOUNTER.R="BC4D94F6"
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C:/Users/Cedric/Documents/GS_scripts/ScriptScrambleAnalysis.R="591AE073"
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C:/Users/Cedric/Documents/GS_scripts/XCHROM_PermutatedAnonymizedAnalysis.R="23994782"
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C:/Users/Cedric/Documents/Gerben/GerbVignette.Rmd="E81330B0"
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C:/Users/Cedric/Documents/Gerben/GerbenScript.R="F742FF24"
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C:/Users/Cedric/Documents/Gerben/Model_Sloppy.Rmd="89C18C53"
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C:/Users/Cedric/Documents/MAGEgenotyper/MAGESCRIPT2_ControlGenotyping.R="0D03A2CA"
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C:/Users/Cedric/Documents/MAGEgenotyper/MAGESCRIPT5_dAD.R="DB83340B"
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C:/Users/Cedric/Documents/MAGEgenotyper/MAGE_DP_Temp_Copy.R="8D7F287A"
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C:/Users/Cedric/Documents/MAGEgenotyper/MethylatieChecker_SNP.R="09CB3C73"
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C:/Users/Cedric/Documents/MAGEgenotyper/MyBetaBinom/R/RcppExports.R="B8CF69D0"
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C:/Users/Cedric/Documents/MAGEgenotyper/MyReAnnotter.R="0749454C"
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C:/Users/Cedric/Documents/MAGEgenotyper/NewGenoAndAnnotation.R="3C32841A"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/DESCRIPTION="C2CA8763"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/License="4A82D458"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/NAMESPACE="191AFE80"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/R/AD_correlator.R="AFE585E2"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/R/AdvancedFitter.R="6FBA5501"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/R/AdvancedPvalcomb.R="8D226AE6"
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C:/Users/Cedric/Documents/MAGEgenotyper/maelstRom/R/AllelicMeta_est.R="362DEE6D"

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(AD_correlator)
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export(AdvancedFitter)
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export(AdvancedPvalcomb)
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export(AllelicMeta_est)

‎R/AD_correlator.R‎

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#' AnnotateLater
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#' @export
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AD_correlator <- function(CountDF, dAD_fitres, CustomCor = NULL, MinCount = 0, method = "spearman"){
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CountDF$GT<- "none"
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CountDF$EX <- NA
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for(AAi in 1:nrow(CountDF)){
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EXTR <- min(maelstRom::pBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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dAD_fitres$Pi, dAD_fitres$ThetaHetT),
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maelstRom::pBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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dAD_fitres$Pi, dAD_fitres$ThetaHetT, lower.tail = FALSE) +
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maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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dAD_fitres$Pi, dAD_fitres$ThetaHetT))
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CountDF$EX[AAi] <- EXTR
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24+
GT <- which(c(dAD_fitres$phi_rr*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
25+
CountDF$ref_count[AAi] + CountDF$var_count[AAi],
26+
1-SEmedian, dAD_fitres$ThetaHom),
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dAD_fitres$phi_rv*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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dAD_fitres$Pi, dAD_fitres$ThetaHetT),
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dAD_fitres$phi_vv*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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SEmedian, dAD_fitres$ThetaHom)) ==
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max(c(dAD_fitres$phi_rr*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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1-SEmedian, dAD_fitres$ThetaHom),
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dAD_fitres$phi_rv*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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dAD_fitres$Pi, dAD_fitres$ThetaHetT),
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dAD_fitres$phi_vv*maelstRom::dBetaBinom(CountDF$ref_count[AAi],
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CountDF$ref_count[AAi] + CountDF$var_count[AAi],
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SEmedian, dAD_fitres$ThetaHom))))
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if(any(GT == 2)){
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CountDF$GT[AAi]<- "rv"
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}else if(any(GT == 1)){
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CountDF$GT[AAi]<- "rr"
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}else{
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CountDF$GT[AAi]<- "vv"
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}
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}
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CountDF$EX <- -log(CountDF$EX)
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CountDF <- CountDF[!is.na(CountDF$EX),]
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CountDF <- CountDF[CountDF$GT == "rv" &
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(CountDF$ref_count + CountDF$var_count > MinCount) &
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CountDF$Outlier != 1,]
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if(is.null(CustomCor)){
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CountDF$ToCorr <- CountDF$ref_count + CountDF$var_count
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}else{
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CountDF$ToCorr <- CustomCor
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}
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CountDF <- CountDF[!is.na(CountDF$ToCorr),]
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if(nrow(CountDF) <= 1){
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ReturnObj <- c(NA, NA)
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names(ReturnObj) <- c("correlation", "p-value")
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return(ReturnObj)
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}else{
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ReturnObj <- c()
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ReturnObj <- c(ReturnObj, cor(CountDF$EX, CountDF$ToCorr, method = method))
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ReturnObj <- c(ReturnObj, cor.test(CountDF$EX, CountDF$ToCorr, method = method)$p.value)
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names(ReturnObj) <- c("correlation", "p-value")
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return(ReturnObj)
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}
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}

‎data/VignetteImage.RData‎

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