Last updated: 2020-10-16
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Knit directory: NRCRI_2020GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 977f389 | wolfemd | 2020-10-16 | Publish NRCRI imputations for 2020 (DCas20_5510 and DCas20_5440) plus a |
Follow outlined GenomicPredictionChecklist and previous pipeline to process cassavabase data for ultimate genomic prediction.
Below we will clean and format training data.
Downloaded all NRCRI field trials.
DatabaseDownload_2020Oct13/ uploaded to Cassavabase FTP server.Read DB data directly from the Cassavabase FTP server.
Make TrialType Variable
Looking at the studyName’s of trials getting NA for TrialType, which can’t be classified at present.
Here is the list of trials I am not including.
dbdata %>% filter(is.na(TrialType)) %$% unique(studyName) %>%
write.csv(.,file = here::here("output","NRCRI_trials_NOT_identifiable.csv"), row.names = F)Wrote to disk a CSV in the output/ sub-directory.
Should any of these trials have been included?
Especially the following new trials (post 2018)?
[1] "19C3SeedlingNursery_umu" "19cetcftumu_1"
[3] "19cetcftumu_2" "19cetcftumu_3"
[5] "19crossingblockCETubiaja" "19introLatAmeumu"
[7] "19S1CETumu" "19UYTumudike"
[9] "2020regionaltrial12Otobi" "2020regionaltrial16Otobi"
[11] "2020regionaltrial17Otobi" "2020regionaltrial21Otobi"
[13] "2020regionaltrial23Umudike" "2020regionaltrial24Otobi"
[15] "2020regionaltrial29Otobi" "2020regionaltrial36Otobi"
[17] "2020regionaltrial3Otobi" "2020regionaltrial46Umudike"
[19] "2020regionaltrial50Otobi" "2020regionaltrial9Otobi"
[21] "20AYTintroLatAmeAgo-Owu" "20AYTintroLatAmekano"
[23] "20AYTintroLatAmeOnne" "20AYTintroLatAmeUmudike"
[25] "20C4crossingblockCETubiaja" "20CFT1AYTumu"
[27] "20CFT2AYTumu" "20CFT3AYTmum"
[29] "20event2D8083CFTumu" "20event3D9001CFTumu"
[31] "20eventD5001CFTumu" "20NRS1pytubiaja"
[33] "20NRS1pytumu" "20NRS1pytumudike"
[35] "20pyt_GPR_ikenne_set1" "20pyt_GPR_ikenne_set2"
[37] "20pyt_GPR_ikenne_set3" "20pyt_GPR_ikenne_set4"
[39] "20pyt_GPR_umu_Set1" "20pyt_GPR_umu_Set2"
[41] "20pyt_GPR_umu_Set3" "20pyt_GPR_umu_Set4"
[43] "20pytNUEigbariam_1" "20pytNUEigbariam_2"
[45] "20pytNUEotobi_1" "20pytNUEotobi_2"
[47] "20pytNUEset1umudike" "20pytNUEset2umudike"
[49] "20pytNUEumu_2" "20pytNUset1igbariam"
[51] "20pytNUset2igbariam" "20uytnrcri_iita_reg_oto"
[53] "20uytnrcri_iita_reg_umu" "CETCrossingblock19_ubiaja"
[55] "Ikenne2019RootPhenotyping" "Umudike2019RootPhenotyping"dbdata %<>%
filter(!is.na(TrialType))
dbdata %>%
group_by(programName) %>%
summarize(N=n()) %>% rmarkdown::paged_table()Making a table of abbreviations for renaming
traitabbrevs<-tribble(~TraitAbbrev,~TraitName,
"CMD1S","cassava.mosaic.disease.severity.1.month.evaluation.CO_334.0000191",
"CMD3S","cassava.mosaic.disease.severity.3.month.evaluation.CO_334.0000192",
"CMD6S","cassava.mosaic.disease.severity.6.month.evaluation.CO_334.0000194",
"CMD9S","cassava.mosaic.disease.severity.9.month.evaluation.CO_334.0000193",
"CGM","Cassava.green.mite.severity.CO_334.0000033",
"CGMS1","cassava.green.mite.severity.first.evaluation.CO_334.0000189",
"CGMS2","cassava.green.mite.severity.second.evaluation.CO_334.0000190",
"DM","dry.matter.content.percentage.CO_334.0000092",
"PLTHT","plant.height.measurement.in.cm.CO_334.0000018",
"BRNHT1","first.apical.branch.height.measurement.in.cm.CO_334.0000106",
"SHTWT","fresh.shoot.weight.measurement.in.kg.per.plot.CO_334.0000016",
"RTWT","fresh.storage.root.weight.per.plot.CO_334.0000012",
"RTNO","root.number.counting.CO_334.0000011",
"TCHART","total.carotenoid.by.chart.1.8.CO_334.0000161",
"NOHAV","plant.stands.harvested.counting.CO_334.0000010")
traitabbrevs %>% rmarkdown::paged_table()Run function renameAndSelectCols() to rename columns and remove everything unecessary
[1] "studyYear"
[2] "programDbId"
[3] "programName"
[4] "programDescription"
[5] "studyDbId"
[6] "studyName"
[7] "studyDescription"
[8] "studyDesign"
[9] "plotWidth"
[10] "plotLength"
[11] "fieldSize"
[12] "fieldTrialIsPlannedToBeGenotyped"
[13] "fieldTrialIsPlannedToCross"
[14] "plantingDate"
[15] "harvestDate"
[16] "locationDbId"
[17] "locationName"
[18] "germplasmDbId"
[19] "germplasmName"
[20] "germplasmSynonyms"
[21] "observationLevel"
[22] "observationUnitDbId"
[23] "observationUnitName"
[24] "replicate"
[25] "blockNumber"
[26] "plotNumber"
[27] "rowNumber"
[28] "colNumber"
[29] "entryType"
[30] "plantNumber"
[31] "plantedSeedlotStockDbId"
[32] "plantedSeedlotStockUniquename"
[33] "plantedSeedlotCurrentCount"
[34] "plantedSeedlotCurrentWeightGram"
[35] "plantedSeedlotBoxName"
[36] "plantedSeedlotTransactionCount"
[37] "plantedSeedlotTransactionWeight"
[38] "plantedSeedlotTransactionDescription"
[39] "availableGermplasmSeedlotUniquenames"
[40] "Cassava.anthracnose.disease.incidence.CO_334.0000038"
[41] "Cassava.anthracnose.disease.severity.CO_334.0000032"
[42] "Cassava.bacterial.blight.incidence.CO_334.0000037"
[43] "Cassava.bacterial.blight.severity.CO_334.0000031"
[44] "Cassava.green.mite.incidence.CO_334.0000122"
[45] "Cassava.green.mite.severity.CO_334.0000033"
[46] "Cassava.mosaic.disease.incidence.CO_334.0000039"
[47] "Cassava.mosaic.disease.severity.CO_334.0000035"
[48] "apical.pubescence.visual.rating.0.7.CO_334.0000026"
[49] "boiled.storage.root.color.visual.1.3.CO_334.0000114"
[50] "branching.level.counting.CO_334.0000079"
[51] "branching.visual.rating.0.3.CO_334.0000139"
[52] "cassava.anthractnose.disease.incidence.in.3.month.CO_334.0000219"
[53] "cassava.anthractnose.disease.incidence.in.6.month.CO_334.0000181"
[54] "cassava.anthractnose.disease.incidence.in.9.month.CO_334.0000182"
[55] "cassava.anthractnose.disease.incidence.in12.month.CO_334.0000183"
[56] "cassava.anthractnose.disease.severity.in.3.month.CO_334.0000218"
[57] "cassava.anthractnose.disease.severity.in.6.month.CO_334.0000184"
[58] "cassava.anthractnose.disease.severity.in.9.month.CO_334.0000185"
[59] "cassava.anthractnose.disease.severity.in12.month.CO_334.0000186"
[60] "cassava.bacterial.blight.incidence.12.month.evaluation.CO_334.0000211"
[61] "cassava.bacterial.blight.incidence.3.month.evaluation.CO_334.0000178"
[62] "cassava.bacterial.blight.incidence.6.month.evaluation.CO_334.0000179"
[63] "cassava.bacterial.blight.incidence.9.month.evaluation.CO_334.0000180"
[64] "cassava.bacterial.blight.severity.12.month.evaluation.CO_334.0000212"
[65] "cassava.bacterial.blight.severity.3.month.evaluation.CO_334.0000175"
[66] "cassava.bacterial.blight.severity.6.month.evaluation.CO_334.0000176"
[67] "cassava.bacterial.blight.severity.9.month.evaluation.CO_334.0000177"
[68] "cassava.green.mite.incidence.first.evaluation.CO_334.0000187"
[69] "cassava.green.mite.incidence.second.evaluation.CO_334.0000188"
[70] "cassava.green.mite.severity.first.evaluation.CO_334.0000189"
[71] "cassava.green.mite.severity.second.evaluation.CO_334.0000190"
[72] "cassava.mealy.bug.incidence.by.ratio.CO_334.0000041"
[73] "cassava.mealy.bug.severity.by.visual.rating.1.5.CO_334.0000034"
[74] "cassava.mosaic.disease.incidence.1.month.evaluation.CO_334.0000195"
[75] "cassava.mosaic.disease.incidence.12.month.evaluation.CO_334.0000200"
[76] "cassava.mosaic.disease.incidence.3.month.evaluation.CO_334.0000196"
[77] "cassava.mosaic.disease.incidence.6.month.evaluation.CO_334.0000198"
[78] "cassava.mosaic.disease.incidence.9.month.evaluation.CO_334.0000197"
[79] "cassava.mosaic.disease.severity.1.month.evaluation.CO_334.0000191"
[80] "cassava.mosaic.disease.severity.12.month.evaluation.CO_334.0000199"
[81] "cassava.mosaic.disease.severity.3.month.evaluation.CO_334.0000192"
[82] "cassava.mosaic.disease.severity.6.month.evaluation.CO_334.0000194"
[83] "cassava.mosaic.disease.severity.9.month.evaluation.CO_334.0000193"
[84] "compactness.of.shoot.apices.visual.1.3.CO_334.0000270"
[85] "dry.matter.content.by.specific.gravity.method.CO_334.0000160"
[86] "dry.matter.content.percentage.CO_334.0000092"
[87] "dry.yield.CO_334.0000014"
[88] "first.apical.branch.height.measurement.in.cm.CO_334.0000106"
[89] "first.fully.expanded.leaf.color.visual.rating.1.9.CO_334.0000102"
[90] "flower.visual.rating.0.1.CO_334.0000111"
[91] "fresh.root.yield.CO_334.0000013"
[92] "fresh.shoot.weight.measurement.in.kg.per.plot.CO_334.0000016"
[93] "fresh.storage.root.weight.per.plot.CO_334.0000012"
[94] "fruit.set.visual.rating.0.1.CO_334.0000110"
[95] "harvest.index.variable.CO_334.0000015"
[96] "initial.plant.vigor.assessment.1.5.CO_334.0000220"
[97] "initial.vigor.assessment.1.7.CO_334.0000009"
[98] "leaf.lobe.number.counting.CO_334.0000082"
[99] "leaf.retention.visual.rating.1.5.CO_334.0000048"
[100] "leaf.scar.level.measurement.in.cm.CO_334.0000137"
[101] "leaf.scar.number.counting.CO_334.0000136"
[102] "leaf.scar.prominence.visual.rating.1.7.CO_334.0000105"
[103] "leaf.weight.measurement.in.kg.CO_334.0000134"
[104] "marketable.root.number.counting.CO_334.0000169"
[105] "marketable.root.weight.measurement.in.kg.CO_334.0000131"
[106] "non.marketable.root.number.counting.CO_334.0000168"
[107] "non.marketable.root.weight.measurement.in.kg.CO_334.0000132"
[108] "number.of.planted.stakes.per.plot.counting.CO_334.0000159"
[109] "ovary.color.visual.rating.1.5.CO_334.0000058"
[110] "peel.weight.measurement.in.kg.CO_334.0000249"
[111] "petiole.color.visual.rating.1.9.CO_334.0000023"
[112] "plant.architecture.visual.rating.1.5.CO_334.0000099"
[113] "plant.height.measurement.in.cm.CO_334.0000018"
[114] "plant.height.with.leaf.in.cm.CO_334.0000123"
[115] "plant.height.without.leaf.CO_334.0000125"
[116] "plant.stands.harvested.counting.CO_334.0000010"
[117] "post.harvest.physiological.deterioration.variable.0.10.CO_334.0000077"
[118] "root.color.visual.rating.1.3.CO_334.0000221"
[119] "root.flesh.color.visual.rating.1.3.CO_334.0000222"
[120] "root.neck.length.visual.rating.0.7.CO_334.0000022"
[121] "root.number.counting.CO_334.0000011"
[122] "root.surface.color.visual.rating.1.3.CO_334.0000053"
[123] "root.weight.in.air.CO_334.0000157"
[124] "root.weight.in.water.CO_334.0000158"
[125] "rotted.storage.root.counting.CO_334.0000084"
[126] "size.of.shoot.apices.assessment.1.3.CO_334.0000269"
[127] "specific.gravity.CO_334.0000163"
[128] "sprout.count.at.nine.month.CO_334.0000216"
[129] "sprout.count.at.one.month.CO_334.0000213"
[130] "sprout.count.at.six.month.CO_334.0000215"
[131] "sprout.count.at.three.month.CO_334.0000214"
[132] "sprout.count.at.twelve.month.CO_334.0000217"
[133] "sprouting.proportion.CO_334.0000008"
[134] "starch.content.percentage.CO_334.0000071"
[135] "staygreen.visual.scale.1.9.CO_334.0000224"
[136] "stem.weight.measurement.in.kg.CO_334.0000127"
[137] "storage.root.pulp.color.visual.rating.1.3.CO_334.0000021"
[138] "storage.root.shape.visual.rating.1.6.CO_334.0000020"
[139] "stump.weight.measurement.in.kg.CO_334.0000135"
[140] "top.yield.CO_334.0000017"
[141] "total.carotenoid.content.in.ug.g.CO_334.0000073"
[142] "unexpanded.apical.leaf.color.visual.rating.1.9.CO_334.0000101"
[143] "notes"
[144] "TrialType" dbdata<-dbdata %>%
mutate(CMD1S=ifelse(CMD1S<1 | CMD1S>5,NA,CMD1S),
CMD3S=ifelse(CMD3S<1 | CMD3S>5,NA,CMD3S),
CMD6S=ifelse(CMD6S<1 | CMD1S>5,NA,CMD6S),
CMD9S=ifelse(CMD9S<1 | CMD1S>5,NA,CMD9S),
CGM=ifelse(CGM<1 | CGM>5,NA,CGM),
CGMS1=ifelse(CGMS1<1 | CGMS1>5,NA,CGMS1),
CGMS2=ifelse(CGMS2<1 | CGMS2>5,NA,CGMS2),
DM=ifelse(DM>100 | DM<=0,NA,DM),
RTWT=ifelse(RTWT==0 | NOHAV==0 | is.na(NOHAV),NA,RTWT),
SHTWT=ifelse(SHTWT==0 | NOHAV==0 | is.na(NOHAV),NA,SHTWT),
RTNO=ifelse(RTNO==0 | NOHAV==0 | is.na(NOHAV),NA,RTNO),
NOHAV=ifelse(NOHAV==0,NA,NOHAV),
NOHAV=ifelse(NOHAV>42,NA,NOHAV),
RTNO=ifelse(!RTNO %in% 1:10000,NA,RTNO))I anticipate this will not be necessary as it will be computed before or during data upload.
For calculating fresh root yield:
dbdata<-dbdata %>%
mutate(PlotSpacing=ifelse(programName!="IITA",1,
ifelse(studyYear<2013,1,
ifelse(TrialType %in% c("CET","GeneticGain","ExpCET"),1,0.8))))
maxNOHAV_byStudy<-dbdata %>%
group_by(programName,locationName,studyYear,studyName,studyDesign) %>%
summarize(MaxNOHAV=max(NOHAV, na.rm=T)) %>%
ungroup() %>%
mutate(MaxNOHAV=ifelse(MaxNOHAV=="-Inf",NA,MaxNOHAV))
write.csv(maxNOHAV_byStudy %>% arrange(studyYear),file=here::here("output","maxNOHAV_byStudy.csv"), row.names = F)This bit is from April 2019: Previously, I took these values as is. I am unsatisfied with that. The trial number is small enough I’m going to curate manually below. I hope this gives better yield results.
maxNOHAV_byStudy %<>%
mutate(MaxNOHAV=ifelse(studyName=="18C2acrossingblockCETubiaja",8,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="13TP1CET518kano",5,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="17C1aAYTGSkano",10,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="18C1bAYTGSOtobi",10,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="16C1aCETnonGSOtobi",5,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="17C1bCETkano",5,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="16C1aCETnonGSOtobi",5,MaxNOHAV),
MaxNOHAV=ifelse(studyName=="18C1bAYTGSset2umu",10,MaxNOHAV))
# maxNOHAV_byStudy %>%
# filter(!is.na(MaxNOHAV),
# MaxNOHAV>=10)
# maxNOHAV_byStudy %>%
# filter(grepl("CET",studyName))
# dbdata %>% filter(studyName=="13TP1CET518kano") %$% table(NOHAV)
# dbdata %>% filter(studyName=="18C2acrossingblockCETubiaja") %$% table(NOHAV)
# dbdata %>% filter(studyName=="17C1aAYTGSkano") %$% table(NOHAV)
# dbdata %>% filter(studyName=="18C1bAYTGSOtobi") %$% table(NOHAV)
# dbdata %>% filter(studyName=="16C1aCETnonGSOtobi") %$% table(NOHAV)
# dbdata %>% filter(studyName=="17C1aAYTGSumu") %$% table(NOHAV)
# dbdata %>% filter(studyName=="17C1bCETkano") %$% table(NOHAV)
# dbdata %>% filter(studyName=="18C1bAYTGSset2umu") %$% table(NOHAV)
# 18C2acrossingblockCETubiaja ...10... Lydia says 8.
# 13TP1CET518kano... 5
# 18C1bAYTGSOtobi... looks like it should be 5, but DB says 4 x 4 m plots.
# 16C1aCETnonGSOtobi... 10 (says plot length 8)
# 18C1bAYTGSset2umu... 10# I log transform yield traits
# to satisfy homoskedastic residuals assumption
# of linear mixed models
dbdata<-left_join(dbdata,maxNOHAV_byStudy) %>%
mutate(RTWT=ifelse(NOHAV>MaxNOHAV,NA,RTWT),
SHTWT=ifelse(NOHAV>MaxNOHAV,NA,SHTWT),
RTNO=ifelse(NOHAV>MaxNOHAV,NA,RTNO),
HI=ifelse(NOHAV>MaxNOHAV,NA,HI),
logFYLD=log(RTWT/(MaxNOHAV*PlotSpacing)*10),
logTOPYLD=log(SHTWT/(MaxNOHAV*PlotSpacing)*10),
logRTNO=log(RTNO),
PropNOHAV=NOHAV/MaxNOHAV)
# remove non transformed / per-plot (instead of per area) traits
dbdata %<>% select(-RTWT,-SHTWT,-RTNO)This step is mostly copy-pasted from previous processing of IITA- and NRCRI-specific data.
Uses 3 flat files, which are available e.g. here. Specifically, IITA_GBStoPhenoMaster_33018.csv, GBSdataMasterList_31818.csv and NRCRI_GBStoPhenoMaster_40318.csv. I copy them to the data/ sub-directory for the current analysis.
In addition, DArT-only samples are now expected to also have phenotypes. Therefore, checking for matches in new flatfiles, deposited in the data/ (see code below).
library(tidyverse); library(magrittr)
gbs2phenoMaster<-dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","NRCRI_GBStoPhenoMaster_40318.csv"),
stringsAsFactors = F)) %>%
mutate(FullSampleName=ifelse(grepl("C2a",germplasmName,ignore.case = T) &
is.na(FullSampleName),germplasmName,FullSampleName)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","IITA_GBStoPhenoMaster_33018.csv"),
stringsAsFactors = F)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmName=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^UG",germplasmName,ignore.case = T),
gsub("UG","Ug",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^TZ",germplasmName,
ignore.case = T),
gsub("TZ","",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(FullSampleName,OrigKeyFile,Institute) %>%
rename(OriginOfSample=Institute)) %>%
mutate(OrigKeyFile=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OrigKeyFile),"LavalGBS",OrigKeyFile),
OrigKeyFile),
OriginOfSample=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OriginOfSample),"NRCRI",OriginOfSample),
OriginOfSample))
## NEW: check for germName-DArT name matches
germNamesWithoutGBSgenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(is.na(FullSampleName)) %>%
select(-FullSampleName)
## NEW: check for germName-DArT name matches
germNamesWithoutGBSgenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(is.na(FullSampleName)) %>%
select(-FullSampleName)
germNamesWithDArT<-germNamesWithoutGBSgenos %>%
inner_join(read.table(here::here("data","chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam"),
header = F, stringsAsFactors = F)$V2 %>%
grep("TMS16|TMS17|TMS18|TMS19|TMS20",.,value = T, ignore.case = T) %>%
tibble(dartName=.) %>%
separate(dartName,c("germplasmName","dartID"),"_",extra = 'merge',remove = F)) %>%
group_by(germplasmName) %>%
slice(1) %>%
ungroup() %>%
rename(FullSampleName=dartName) %>%
mutate(OrigKeyFile="DArTseqLD", OriginOfSample="IITA") %>%
select(-dartID)
print(paste0(nrow(germNamesWithDArT)," germNames with DArT-only genos"))[1] "0 germNames with DArT-only genos"# first, filter to just program-DNAorigin matches
germNamesWithGenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(!is.na(FullSampleName))
print(paste0(nrow(germNamesWithGenos)," germNames with GBS genos"))[1] "3235 germNames with GBS genos"# program-germNames with locally sourced GBS samples
germNamesWithGenos_HasLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
semi_join(germNamesWithGenos,.) %>%
group_by(programName,germplasmName) %>% # select one DNA per germplasmName per program
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_HasLocalSourcedGBS)," germNames with local GBS genos"))[1] "2712 germNames with local GBS genos"# the rest (program-germNames) with GBS but coming from a different breeding program
germNamesWithGenos_NoLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
anti_join(germNamesWithGenos,.) %>%
# select one DNA per germplasmName per program
group_by(programName,germplasmName) %>%
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_NoLocalSourcedGBS)," germNames without local GBS genos"))[1] "212 germNames without local GBS genos"genosForPhenos<-bind_rows(germNamesWithGenos_HasLocalSourcedGBS,
germNamesWithGenos_NoLocalSourcedGBS) %>%
bind_rows(germNamesWithDArT)
print(paste0(nrow(genosForPhenos)," total germNames with genos either GBS or DArT"))[1] "2924 total germNames with genos either GBS or DArT"dbdata %<>%
left_join(genosForPhenos)
# Create a new identifier, GID
## Equals the value SNP data name (FullSampleName)
## else germplasmName if no SNP data
dbdata %<>%
mutate(GID=ifelse(is.na(FullSampleName),germplasmName,FullSampleName))# going to check against SNP data
snps<-readRDS(file=url(paste0("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/",
"DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds")))
rownames_snps<-rownames(snps); rm(snps); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 1185946 63.4 2106107 112.5 NA 2106107 112.5
Vcells 3460095 26.4 726800432 5545.1 102400 755697347 5765.6# current matches to SNP data
dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
semi_join(tibble(GID=rownames_snps)) %>% nrow() #1340[1] 1340dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
semi_join(tibble(GID=rownames_snps)) %>%
filter(grepl("c1",GID,ignore.case = F)) # no C1 clones currently match# A tibble: 0 x 3
# … with 3 variables: germplasmName <chr>, FullSampleName <chr>, GID <chr>dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
semi_join(tibble(GID=rownames_snps)) %>%
filter(grepl("c2",GID,ignore.case = F)) # no C2 clones either# A tibble: 0 x 3
# … with 3 variables: germplasmName <chr>, FullSampleName <chr>, GID <chr>dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
anti_join(tibble(GID=rownames_snps)) %>%
filter(grepl("c1|c2",GID,ignore.case = T)) # definitely there are both C1 and C2 phenotypes# A tibble: 2,440 x 3
germplasmName FullSampleName GID
<chr> <chr> <chr>
1 NR16F100C1bP001 <NA> NR16F100C1bP001
2 NR16F100C1bP002 <NA> NR16F100C1bP002
3 NR16F104C1bP001 <NA> NR16F104C1bP001
4 NR16F105C1bP001 <NA> NR16F105C1bP001
5 NR16F105C1bP002 <NA> NR16F105C1bP002
6 NR16F106C1bP001 <NA> NR16F106C1bP001
7 NR16F107C1bP002 <NA> NR16F107C1bP002
8 NR16F107C1bP004 <NA> NR16F107C1bP004
9 NR16F108C1bP001 <NA> NR16F108C1bP001
10 NR16F109C1bP001 <NA> NR16F109C1bP001
# … with 2,430 more rows# and there are C1 and C2 genotypes
rownames_snps %>% grep("c1",.,value = T,ignore.case = T) %>% length # [1] 1762[1] 1762[1] 4291germ2snps<-dbdata %>%
distinct(germplasmName,FullSampleName) %>%
semi_join(tibble(FullSampleName=rownames_snps)) %>%
bind_rows(dbdata %>%
distinct(germplasmName,FullSampleName) %>%
anti_join(tibble(FullSampleName=rownames_snps)) %>%
filter(grepl("c1a",germplasmName,ignore.case = T)) %>%
select(-FullSampleName) %>%
left_join(tibble(FullSampleName=rownames_snps) %>%
filter(grepl("c1a",FullSampleName,ignore.case = T)) %>%
separate(FullSampleName,c("dartID","germplasmName"),"\\.\\.\\.",extra = 'merge',remove = F) %>%
select(-dartID))) %>%
bind_rows(dbdata %>%
distinct(germplasmName,FullSampleName) %>%
anti_join(tibble(FullSampleName=rownames_snps)) %>%
filter(grepl("C1b",germplasmName,ignore.case = T)) %>%
filter(grepl("NR16C1b",germplasmName,ignore.case = T)) %>%
select(-FullSampleName) %>%
left_join(tibble(FullSampleName=rownames_snps) %>%
filter(grepl("c1b",FullSampleName,ignore.case = T)) %>%
separate(FullSampleName,c("germplasmName","GBS_ID"),":",extra = 'merge',remove = F) %>%
select(-GBS_ID) %>%
mutate(germplasmName=gsub("C1b","",germplasmName),
germplasmName=paste0("NR16C1b",germplasmName)))) %>%
bind_rows(dbdata %>%
distinct(germplasmName,FullSampleName) %>%
anti_join(tibble(FullSampleName=rownames_snps)) %>%
filter(grepl("C1b",germplasmName,ignore.case = T)) %>%
filter(!grepl("NR16C1b",germplasmName,ignore.case = T)) %>%
select(-FullSampleName) %>%
left_join(tibble(FullSampleName=rownames_snps) %>%
filter(grepl("c1b",FullSampleName,ignore.case = T)) %>%
separate(FullSampleName,c("germplasmName","GBS_ID"),":",extra = 'merge',remove = F) %>%
select(-GBS_ID) %>%
mutate(germplasmName=paste0("NR16",germplasmName)))) %>%
bind_rows(dbdata %>%
distinct(germplasmName,FullSampleName) %>%
anti_join(tibble(FullSampleName=rownames_snps)) %>%
filter(grepl("c2",germplasmName,ignore.case = T)) %>%
select(-FullSampleName) %>%
left_join(tibble(FullSampleName=rownames_snps) %>%
filter(grepl("c2",FullSampleName,ignore.case = T),
grepl("\\.\\.\\.",FullSampleName)) %>%
separate(FullSampleName,c("dartID","germplasmName"),"\\.\\.\\.",extra = 'merge',remove = F) %>%
select(-dartID))) %>%
distinct
germ2snps %>%
count(germplasmName) %>% arrange(desc(n))# A tibble: 3,780 x 2
germplasmName n
<chr> <int>
1 NR16C1bF185P001 4
2 NR16F185C1bP001 4
3 NR16C1bF170P019 3
4 NR16C1bF170P020 3
5 NR16C1bF180P002 3
6 NR16F180C1bP002 3
7 NR16F182C1bP001 3
8 NR16C1bF171P002 2
9 NR16C1bF179P001 2
10 NR16C1bF179P002 2
# … with 3,770 more rows# A tibble: 3,304 x 2
FullSampleName n
<chr> <int>
1 <NA> 357
2 F116C1bP006:CA7RRANXX:3:499841 3
3 F123C1bP002:CA7RRANXX:4:499895 3
4 F123C1bP006:CA7RRANXX:4:499899 3
5 F12C1bP018:CABV7ANXX:5:503932 3
6 F135C1bP001:CABJYANXX:5:505238 3
7 F136C1bP002:CABJYANXX:5:505240 3
8 F140C1bP001:CABJYANXX:5:505270 3
9 F152C1bP010:CA7RRANXX:5:499961 3
10 F152C1bP014:CA7RRANXX:5:499965 3
# … with 3,294 more rows[1] 2911
FALSE TRUE
1584 1327 # FALSE TRUE
# 1584 1327
dbdata %>%
select(-GID,-FullSampleName) %>%
left_join(germ2snps) %$%
length(unique(FullSampleName)) # [1] 3304[1] 3304dbdata %>%
select(-GID,-FullSampleName) %>%
left_join(germ2snps) %$%
table(unique(FullSampleName) %in% rownames_snps)
FALSE TRUE
1 3303 # FALSE TRUE
# 1 3303
# Merge updated pheno-to-SNP matches to raw pheno DF
dbdata %<>%
select(-GID,-FullSampleName) %>%
left_join(germ2snps) %>%
# Re-create the GID identifier
## Equals the value SNP data name (FullSampleName)
## else germplasmName if no SNP data
mutate(GID=ifelse(is.na(FullSampleName),germplasmName,FullSampleName))The next step is to check the experimental design of each trial. If you are absolutely certain of the usage of the design variables in your dataset, you might not need this step.
Examples of reasons to do the step below:
One reason it might be important to get this right is that the variance among complete blocks might not be the same among incomplete blocks. If we treat a mixture of complete and incomplete blocks as part of the same random-effect (replicated-within-trial), we assume they have the same variance.
Also error variances might be heterogeneous among different trial-types (blocking scheme available) and/or plot sizes (maxNOHAV).
Start with cleaned data from previous step.
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 1178879 63.0 2106107 112.5 NA 2106107 112.5
Vcells 2203017 16.9 581440346 4436.1 102400 755697347 5765.6library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
dbdata<-readRDS(here::here("output","NRCRI_CleanedTrialData_2020Oct13.rds"))Detect designs
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_1.5 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.18 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.4 generics_0.0.2 htmltools_0.5.0 yaml_2.2.1
[9] utf8_1.1.4 blob_1.2.1 rlang_0.4.8 later_1.1.0.1
[13] pillar_1.4.6 withr_2.3.0 glue_1.4.2 DBI_1.1.0
[17] dbplyr_1.4.4 modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0
[21] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6
[25] evaluate_0.14 knitr_1.30 ps_1.4.0 httpuv_1.5.4
[29] fansi_0.4.1 broom_0.7.1 Rcpp_1.0.5 promises_1.1.1
[33] backports_1.1.10 scales_1.1.1 jsonlite_1.7.1 fs_1.5.0
[37] hms_0.5.3 digest_0.6.25 stringi_1.5.3 rprojroot_1.3-2
[41] grid_4.0.2 here_0.1 cli_2.1.0 tools_4.0.2
[45] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9 rstudioapi_0.11
[53] assertthat_0.2.1 rmarkdown_2.4 httr_1.4.2 R6_2.4.1
[57] git2r_0.27.1 compiler_4.0.2