Last updated: 2021-05-05
Checks: 7 0
Knit directory: NRCRI_2021GS/
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Untracked: output/genomicPredictions_ModelADE_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_ModelA_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_NRCRI_2021May03.csv
Untracked: output/maxNOHAV_byStudy.csv
Unstaged changes:
Modified: README.md
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/01-cleanTPdata.Rmd) and HTML (docs/01-cleanTPdata.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 94ec811 | wolfemd | 2021-05-05 | Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI. |
| html | e8b3eeb | wolfemd | 2021-05-03 | Build site. |
| Rmd | c887639 | wolfemd | 2021-05-03 | Publish site up through cleanTPdata step to generate cleaned TP data before continuing pipeline. |
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.
Selected all NRCRI trials currently available. Make a list. Named it ALL_NRCRI_TRIALS_2021April29.
Go to Manage –> Download here. Download phenotypes (plot-basis only) and meta-data as CSV using the corresponding boxes / drop-downs.
Store flatfiles, unaltered in directory data/DatabaseDownload_2021April29/.
rm(list=ls())
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))Read DB data directly from the Cassavabase FTP server.
dbdata<-readDBdata(phenotypeFile = here::here("data/DatabaseDownload_2021April29","2021-04-30T144108phenotype_download.csv"))Make TrialType Variable
dbdata<-makeTrialTypeVar(dbdata)
dbdata %>%
count(TrialType) %>% rmarkdown::paged_table()Comparing to 2020: Slightly more C1b data, twice as much C2a, 3 times as much C2b, 7000 more plots with no identified trial type.
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)) %$% unique(studyName) %>%
write.csv(.,file = here::here("output","NRCRI_trials_NOT_identifiable.csv"), row.names = F)Wrote to disk a CSV in the output/ sub-directory.
Should any of these trials have been included?
dbdata %>%
filter(is.na(TrialType)) %$% unique(studyName) [1] "06cetEarlyBulkingUM" "06SeedlingsEarlyBulkingUM"
[3] "07EarlyBulkingUM" "07pytEarlyBulkingUM"
[5] "08EarlyBulkingIB" "08EarlyBulkingOT"
[7] "08EarlyBulkingUM" "09pytIgariam"
[9] "09pytOtobi" "09pytUmudike"
[11] "10cetNR10BseriesUM" "10cetNR10seriesUM"
[13] "10uytCIATsetAUM" "10uytCIATsetBKA"
[15] "10uytUM" "11aytNR09seriesUM"
[17] "11cetNR11seriesUM" "11clonal63cpUM"
[19] "11cmd48gpUM" "11EarlyBulkingDrtKA"
[21] "11UMUyellowRTuyt" "11uyt10RepeatUM"
[23] "12CETearlyBulkingDrtkano" "12PYT36genpyramOtobi"
[25] "12PYT75genpyramUM" "13AYT12yrtOtobi"
[27] "13AYT259mpopdOtobi" "13AYT25umu"
[29] "13AYT35gpOtobii" "13AYT60gpumu"
[31] "13AYT70mpopfOtobi" "13CET261umu"
[33] "13mlt12yrtIG" "13PYT144mpopumu"
[35] "13PYT17mpopfumu" "13pyt20nr12UM"
[37] "13UYT12yrtumu" "13uyt15umu"
[39] "14cCET150umu" "14CET12Highbetacaroumu"
[41] "14CET178mpopumu" "14clonal35ctasetbUM"
[43] "14clonal35ctaUM" "14highbetacaro12umu"
[45] "14iHighbetacaroIghariam" "14PYT21highbetacaroumu"
[47] "14PYT25wrtumu" "14tHighBetaCaroOtobi"
[49] "14UYT12umu" "15ayt11nr13UM"
[51] "15ayt12wrtUM" "15nextgen351cgm-amUM"
[53] "15uyt10hbcUM" "16uyt10hcbUM"
[55] "17C2aSeedling Nursery" "17cet200yellowrt_umu"
[57] "17ppdyieldexperimentUM" "17pyt30yellowrtumu"
[59] "17uyt10yellowrt_umu" "17uyt12yellowrt_umu"
[61] "17uyt13yellowrt_umu" "18AYT13yrtOtobi"
[63] "18AYT13yrtumu" "18C2bSeedlingNurseryumu"
[65] "18introLatAmeumu" "18pyt_lgbariam"
[67] "18PYTmealiness_umu" "18pyt_otobi"
[69] "18UYT12yrtIgbariam" "18UYT12yrtOtobi"
[71] "18UYT12yrtumu_1" "18UYT12yrtumu_2"
[73] "19C3SeedlingNursery_umu" "19cetcftumu_1"
[75] "19cetcftumu_2" "19cetcftumu_3"
[77] "19crossingblockCETubiaja" "19introLatAmeumu"
[79] "19S1CETumu" "19UYTumudike"
[81] "2020regionaltrial12Otobi" "2020regionaltrial16Otobi"
[83] "2020regionaltrial17Otobi" "2020regionaltrial21Otobi"
[85] "2020regionaltrial23Umudike" "2020regionaltrial24Otobi"
[87] "2020regionaltrial29Otobi" "2020regionaltrial36Otobi"
[89] "2020regionaltrial3Otobi" "2020regionaltrial46Umudike"
[91] "2020regionaltrial50Otobi" "2020regionaltrial9Otobi"
[93] "20AYTintroLatAmeAgo-Owu" "20AYTintroLatAmekano"
[95] "20AYTintroLatAmeOnne" "20AYTintroLatAmeUmudike"
[97] "20C4crossingblockCETubiaja" "20CFT1AYTumu"
[99] "20CFT2AYTumu" "20CFT3AYTmum"
[101] "20event2D8083CFTumu" "20event3D9001CFTumu"
[103] "20eventD5001CFTumu" "20NRS1pytubiaja"
[105] "20NRS1pytumu" "20NRS1pytumudike"
[107] "20pyt_GPR_ikenne_set1" "20pyt_GPR_ikenne_set2"
[109] "20pyt_GPR_ikenne_set3" "20pyt_GPR_ikenne_set4"
[111] "20pyt_GPR_umu_Set1" "20pyt_GPR_umu_Set2"
[113] "20pyt_GPR_umu_Set3" "20pyt_GPR_umu_Set4"
[115] "20pytNUEset1umudike" "20pytNUEset2umudike"
[117] "20pytNUEumu_2" "20pytNUset1igbariam"
[119] "20pytNUset2igbariam" "20seedlingnurseryumu"
[121] "20uytnrcri_iita_reg_oto" "20uytnrcri_iita_reg_umu"
[123] "CETCrossingblock19_ubiaja" "Ikenne2019RootPhenotyping"
[125] "PYT 2010" "Umudike2013set1CGM"
[127] "Umudike2013set2CGM" "Umudike2019RootPhenotyping"Should any of these trials have been included?
Especially among the following new trials (post 2018):
dbdata %>%
filter(is.na(TrialType),
as.numeric(studyYear)>2018) %$% unique(studyName) [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] "20pytNUEset1umudike" "20pytNUEset2umudike"
[45] "20pytNUEumu_2" "20pytNUset1igbariam"
[47] "20pytNUset2igbariam" "20seedlingnurseryumu"
[49] "20uytnrcri_iita_reg_oto" "20uytnrcri_iita_reg_umu"
[51] "CETCrossingblock19_ubiaja" "Ikenne2019RootPhenotyping"
[53] "Umudike2019RootPhenotyping"dbdata %<>%
filter(!is.na(TrialType))
dbdata %>%
group_by(programName) %>%
summarize(N=n()) %>% rmarkdown::paged_table()# 18591 (now including a ~5K plot seedling nursery) plotsMaking a table of abbreviations for renaming
dbdata %>% colnames %>% grep("dry.matter",.,value = T)[1] "dry.matter.content.by.specific.gravity.method.CO_334.0000160"
[2] "dry.matter.content.percentage.CO_334.0000092" 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",
"DMsg","dry.matter.content.by.specific.gravity.method.CO_334.0000160",
"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
dbdata<-renameAndSelectCols(traitabbrevs,indata=dbdata,customColsToKeep = c("TrialType","observationUnitName"))dbdata<-dbdata %>%
mutate(CMD1S=ifelse(CMD1S<1 | CMD1S>5,NA,CMD1S),
CMD3S=ifelse(CMD3S<1 | CMD3S>5,NA,CMD3S),
CMD6S=ifelse(CMD6S<1 | CMD6S>5,NA,CMD6S),
CMD9S=ifelse(CMD9S<1 | CMD9S>5,NA,CMD9S),
# CBSD3S=ifelse(CBSD3S<1 | CBSD3S>5,NA,CBSD3S),
# CBSD6S=ifelse(CBSD6S<1 | CBSD6S>5,NA,CBSD6S),
# CBSD9S=ifelse(CBSD9S<1 | CBSD9S>5,NA,CMD9S),
# CBSDRS=ifelse(CBSDRS<1 | CBSDRS>5,NA,CBSDRS),
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),
DMsg=ifelse(DMsg>100 | DMsg<=0,NA,DMsg),
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))dbdata<-dbdata %>%
mutate(HI=RTWT/(RTWT+SHTWT))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 next part is copied from my 2020 NRCRI analysis
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))Now back the standard workflow.
# 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),
FYLD=RTWT/(MaxNOHAV*PlotSpacing)*10,
DYLD=FYLD*(DM/100),
logFYLD=log(FYLD),
logDYLD=log(DYLD),
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,-FYLD,-DYLD)# [NEW AS OF APRIL 2021]
## VERSION with vs. without CBSD
## Impervious to particular timepoints between 1, 3, 6 and 9 scores
# Without CBSD (West Africa)
dbdata<-dbdata %>%
mutate(MCMDS=rowMeans(.[,colnames(.) %in% c("CMD1S","CMD3S","CMD6S","CMD9S")], na.rm = T)) %>%
select(-any_of(c("CMD1S","CMD3S","CMD6S","CMD9S")))
# With CBSD (East Africa)
# dbdata<-dbdata %>%
# mutate(MCMDS=rowMeans(.[,colnames(.) %in% c("CMD1S","CMD3S","CMD6S","CMD9S")], na.rm = T),
# MCBSDS=rowMeans(.[,colnames(.) %in% c("CBSD1S","CBSD3S","CBSD6S","CBSD9S")], na.rm = T)) %>%
# select(-any_of(c("CMD1S","CMD3S","CMD6S","CMD9S","CBSD1S","CBSD3S","CBSD6S","CBSD9S")))This step is mostly copy-pasted from previous processing of IITA- and NRCRI-specific data.
Uses 4 flat files, which are available e.g. here. Specifically, IITA_GBStoPhenoMaster_33018.csv, GBSdataMasterList_31818.csv and NRCRI_GBStoPhenoMaster_40318.csv and chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam. 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 1260427 67.4 2306572 123.2 NA 2306572 123.2
Vcells 3894731 29.8 727217682 5548.3 65536 756131983 5768.9# current matches to SNP data
dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
semi_join(tibble(GID=rownames_snps)) %>% nrow() #[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] [1] 1762rownames_snps %>% grep("c2",.,value = T,ignore.case = T) %>% length # [1] [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 rowsgerm2snps %>%
count(FullSampleName) %>% arrange(desc(n))# 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 rowslength(unique(dbdata$FullSampleName)) # [1] [1] 2911table(unique(dbdata$FullSampleName) %in% rownames_snps)
FALSE TRUE
1584 1327 # FALSE TRUE
#
dbdata %>%
select(-GID,-FullSampleName) %>%
left_join(germ2snps) %$%
length(unique(FullSampleName)) # [1] [1] 3304dbdata %>%
select(-GID,-FullSampleName) %>%
left_join(germ2snps) %$%
table(unique(FullSampleName) %in% rownames_snps)
FALSE TRUE
1 3303 # FALSE TRUE
#
# 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))dbdata %>%
distinct(GID,germplasmName,FullSampleName) %>%
write.csv(.,
file = here::here("output","OnlyChosen_germplasmName_to_FullSampleName_matches_NRCRI_2021May03.csv"),
row.names = F)saveRDS(dbdata,file=here::here("output","NRCRI_CleanedTrialData_2021May03.rds"))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.
rm(list=ls()); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 1220275 65.2 2306572 123.2 NA 2306572 123.2
Vcells 2301934 17.6 581774146 4438.6 65536 756131983 5768.9library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
dbdata<-readRDS(here::here("output","NRCRI_CleanedTrialData_2021May03.rds"))dbdata %>% head %>% rmarkdown::paged_table()Detect designs
dbdata<-detectExptDesigns(dbdata)dbdata %>%
count(programName,CompleteBlocks,IncompleteBlocks) %>% rmarkdown::paged_table()saveRDS(dbdata,file=here::here("output","NRCRI_ExptDesignsDetected_2021May03.rds"))Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
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_2.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.1
[9] ggplot2_3.3.3 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.22 bslib_0.2.4 haven_2.4.0
[5] colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.10 jquerylib_0.1.3
[13] later_1.1.0.1 pillar_1.6.0 withr_2.4.2 glue_1.4.2
[17] DBI_1.1.1 dbplyr_2.1.1 readxl_1.3.1 modelr_0.1.8
[21] lifecycle_1.0.0 cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0
[25] rvest_1.0.0 evaluate_0.14 knitr_1.32 httpuv_1.5.5
[29] fansi_0.4.2 broom_0.7.6 Rcpp_1.0.6 promises_1.2.0.1
[33] backports_1.2.1 scales_1.1.1 jsonlite_1.7.2 fs_1.5.0
[37] hms_1.0.0 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.3 here_1.0.1 cli_2.4.0 tools_4.0.3
[45] sass_0.3.1 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.1 xml2_1.3.2 reprex_2.0.0 lubridate_1.7.10
[53] rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.7 httr_1.4.2
[57] R6_2.5.0 git2r_0.28.0 compiler_4.0.3