In sanchezi/phisStatR: Phenoarch Greenhouse Data Analysis
Objective
library(dplyr)
library(tidyr)
library(phisStatR)
library(ggplot2)
This vignette deals with the detection of outlier plants in a lattice experiment using a spatial model using splines (SpATS library) [3]. Here the following steps of this procedure developed for Maize experiment but easily adaptable to others species:
- From a temporal dataset designed as plant1 with biomass (or biovolume) and plant height phenotypes. Extract predictions of these two phenotypes at a specific time point (for example just before the treatment) and create a dataset designed as plant4. Here, we will use the biomass and plant height predicted at 24 days et 20°C.
- From a temporal dataset of count of number of leaves, extract the phyllocron (slope of linear regression for each plant)
We have so a dataset with one row for each plant in the experiment containing the three phenotypes: biomass24, PH24 and Phy.
- Apply a SpATS model on each phenotype, check the diagnostic graphics and retrieve the deviance residuals calculated by the model
-
On the residuals, we can detect outlier plant(s) with a combined physiological criterion applying the following rules:
-
raw procedure: at a threshold=0.95 (can be modified)
- small plants are identified if $res_i < \mu_{res} - qnorm(threshold) \times sd_{res}$ for biomass AND phyllocron
- big plants are identified if $res_i > \mu_{res} + qnorm(threshold) \times sd_{res}$ for biomass AND plant height
Import of data
In this vignette, we use a toy data set of the phisStatR library (anonymized real data set). the first one plant4 is extracted from plant1 and is used to detect outlier at a specific time point. The detected outlier will be checked on the plant1 dataset graphically.
mydata<-plant4
str(mydata)
SpATS library
Biomass
spat.biomass<-fitSpATS(datain=mydata,trait="Biomass24",genotypeId="genotypeAlias",rowId="Line",colId="Position",
typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.biomass)
Plant height
spat.ph<-fitSpATS(datain=mydata,trait="PH24",genotypeId="genotypeAlias",rowId="Line",colId="Position",
typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.ph)
Phyllocron
spat.phy<-fitSpATS(datain=mydata,trait="Phy",genotypeId="genotypeAlias",rowId="Line",colId="Position",
typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.phy)
Data cleaning
The data cleaning procedure is to identify plants having too large or too small features, from deviance residuals obtained with SpATS mixed models for each parameter (biomass, plant height and phyllocron). 2 procedures are tested:
- raw procedure: at a threshold=0.95 or 0.90 or 0.98 (can be modified)
- small plants are identified if $res_i < \mu_{res} - qnorm(threshold) \times sd_{res}$ for biomass AND phyllocron
- big plants are identified if $res_i > \mu_{res} + qnorm(threshold) \times sd_{res}$ for biomass AND plant height
- quantile procedure: Same procedure with quantiles :
- small plants are identified if $res_{i} < Q1_{res} - 1.5 \times(Q3 - Q1)$ for biomass AND phyllocron
- big plants are identified if $res_{i} > Q1_{res} - 1.5 \times(Q3 - Q1)$ for biomass AND plant height
# concatenate raw data and residuals calculated with the SpATS model
devResBio<-spat.biomass$residuals
devResPH<-spat.ph$residuals
devResPhy<-spat.phy$residuals
myglobal<-cbind.data.frame(mydata,devResBio,devResPH,devResPhy)
threshold<-0.95
myglobal<-mutate(myglobal,mean.devBio=mean(devResBio,na.rm=TRUE),
sd.devBio=sd(devResBio,na.rm=TRUE),
mean.devPH=mean(devResPH,na.rm=TRUE),
sd.devPH=sd(devResPH,na.rm=TRUE),
mean.devPhy=mean(devResPhy,na.rm=TRUE),
sd.devPhy=sd(devResPhy,na.rm=TRUE),
lower.devBio=mean.devBio - sd.devBio*qnorm(threshold),
lower.devPH=mean.devPH - sd.devPH*qnorm(threshold),
lower.devPhy=mean.devPhy - sd.devPhy*qnorm(threshold),
upper.devBio=mean.devBio + sd.devBio*qnorm(threshold),
upper.devPH=mean.devPH + sd.devPH*qnorm(threshold),
upper.devPhy=mean.devPhy + sd.devPhy*qnorm(threshold),
# yes=1 == OK
# no =0 == problem
lower.critraw.spatsBio=ifelse(devResBio - lower.devBio > 0,yes=1,no=0),
lower.critraw.spatsPH=ifelse(devResPH - lower.devPH > 0,yes=1,no=0),
lower.critraw.spatsPhy=ifelse(devResPhy - lower.devPhy > 0,yes=1,no=0),
upper.critraw.spatsBio=ifelse(devResBio - upper.devBio < 0,yes=1,no=0),
upper.critraw.spatsPH=ifelse(devResPH - upper.devPH < 0,yes=1,no=0),
upper.critraw.spatsPhy=ifelse(devResPhy - upper.devPhy < 0,yes=1,no=0),
Q1.devBio=quantile(devResBio,probs=0.25,na.rm=TRUE) -
1.5*(quantile(devResBio,probs=0.75,na.rm=TRUE) -
quantile(devResBio,probs=0.25,na.rm=TRUE)),
Q1.devPH=quantile(devResPH,probs=0.25,na.rm=TRUE) -
1.5*(quantile(devResPH,probs=0.75,na.rm=TRUE) -
quantile(devResPH,probs=0.25,na.rm=TRUE)),
Q1.devPhy=quantile(devResPhy,probs=0.25,na.rm=TRUE) -
1.5*(quantile(devResPhy,probs=0.75,na.rm=TRUE) -
quantile(devResPhy,probs=0.25,na.rm=TRUE)),
Q3.devBio=quantile(devResBio,probs=0.75,na.rm=TRUE) +
1.5*(quantile(devResBio,probs=0.75,na.rm=TRUE) -
quantile(devResBio,probs=0.25,na.rm=TRUE)),
Q3.devPH=quantile(devResPH,probs=0.75,na.rm=TRUE) +
1.5*(quantile(devResPH,probs=0.75,na.rm=TRUE) -
quantile(devResPH,probs=0.25,na.rm=TRUE)),
Q3.devPhy=quantile(devResPhy,probs=0.75,na.rm=TRUE) +
1.5*(quantile(devResPhy,probs=0.75,na.rm=TRUE) -
quantile(devResPhy,probs=0.25,na.rm=TRUE)),
lower.critci.spatsBio=ifelse(devResBio - Q1.devBio > 0,yes=1,no=0),
lower.critci.spatsPH=ifelse(devResPH - Q1.devPH > 0,yes=1,no=0),
lower.critci.spatsPhy=ifelse(devResPhy - Q1.devPhy > 0,yes=1,no=0),
upper.critci.spatsBio=ifelse(devResBio - Q3.devBio < 0,yes=1,no=0),
upper.critci.spatsPH=ifelse(devResPH - Q3.devPH < 0,yes=1,no=0),
upper.critci.spatsPhy=ifelse(devResPhy - Q3.devPhy < 0,yes=1,no=0)
)
# small plants
# yes=1 == OK
# no =0 == problem
myglobal<-mutate(myglobal,
flagLowerRawSpats=ifelse(lower.critraw.spatsBio + lower.critraw.spatsPhy==0,yes=0,no=1),
flagLowerCiSpats=ifelse(lower.critci.spatsBio + lower.critci.spatsPhy==0,yes=0,no=1)
)
# big plants
# yes=1 == OK
# no =0 == problem
myglobal<-mutate(myglobal,
flagUpperRawSpats=ifelse(upper.critraw.spatsBio + upper.critraw.spatsPH==0,yes=0,no=1),
flagUpperCiSpats=ifelse(upper.critci.spatsBio + upper.critci.spatsPH==0,yes=0,no=1)
)
The user can save the residuals and detected outliers in an output file.
write.table(myglobal,"OutputFile_detectOutlierCurves_SpATS.csv",row.names=FALSE,sep="\t")
Summary of the cleaning procedure:
- Number of too small plants according to raw criteria:
r ifelse(length(table(myglobal[,"flagLowerRawSpats"]))==1,
0,table(myglobal[,"flagLowerRawSpats"])[[1]])
- Number of too big plants according to raw criteria:
r ifelse(length(table(myglobal[,"flagUpperRawSpats"]))==1,
0,table(myglobal[,"flagUpperRawSpats"])[[1]])
- Number of too small plants according to quantile criteria:
r ifelse(length(table(myglobal[,"flagLowerCiSpats"]))==1,
0,table(myglobal[,"flagLowerCiSpats"])[[1]])
- Number of too big plants according to quantile criteria:
r ifelse(length(table(myglobal[,"flagUpperCiSpats"]))==1,
0,table(myglobal[,"flagUpperCiSpats"])[[1]])
raw criteria
cat("Lower raw outlier:")
myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagLowerRawSpats) %>%
filter(flagLowerRawSpats !=1)
cat("Upper raw outlier:")
myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagUpperRawSpats) %>%
filter(flagUpperRawSpats !=1)
quantile criteria
cat("Lower quantile outlier:")
myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagLowerCiSpats) %>%
filter(flagLowerCiSpats !=1)
cat("Upper quantile outlier:")
myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagUpperCiSpats) %>%
filter(flagUpperCiSpats !=1)
raw criteria : Plots
The user can check the outlier curves detected by this procedure. We use plant1 the dataset containing all the phenotypes of the same toy experiment as plant4 but over thermal time.
outlierId<-filter(myglobal,flagLowerRawSpats !=1 | flagUpperRawSpats !=1) %>%
select(genotypeAlias,scenario,repetition,flagLowerRawSpats,flagUpperRawSpats) %>%
unite("geno_sce",genotypeAlias,scenario,sep="_",remove=FALSE)
mycurve<-plant1 %>%
unite("geno_sce",genotypeAlias,scenario,sep="_",remove=FALSE) %>%
left_join(outlierId,mycurve,by=c("geno_sce","repetition"))
# flagLower ou upper == 0 problem
# flagLower ou upper == 1 OK
# flag == "lower" problem en lower
# flag == "upper" problem en upper
# flag == "OK" plant OK
tmp<-filter(mycurve,geno_sce %in% outlierId[,"geno_sce"]) %>%
mutate(flagLowerRawSpats=ifelse(is.na(flagLowerRawSpats),1,flagLowerRawSpats),
flagUpperRawSpats=ifelse(is.na(flagUpperRawSpats),1,flagUpperRawSpats),
flag=case_when(flagLowerRawSpats == 0 ~ "lower",
flagUpperRawSpats == 0 ~ "upper",
flagUpperRawSpats + flagLowerRawSpats ==2 ~ "OK"
))
ggplot(data=tmp,aes(x=thermalTime,y=biovolume,color=as.factor(repetition))) + geom_line() +
geom_point(aes(shape=flag,color=as.factor(repetition))) + scale_shape_manual(values = c(0,19,2)) +
facet_wrap(~geno_sce)
ggplot(data=tmp,aes(x=thermalTime,y=plantHeight,color=as.factor(repetition))) + geom_line() +
geom_point(aes(shape=flag,color=as.factor(repetition))) + scale_shape_manual(values = c(0,19,2)) +
facet_wrap(~geno_sce)
Session info
sessionInfo()
References
- R Development Core Team (2015). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
- Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2017). Correcting for spatial heterogeneity
in plant breeding experiments with P-splines. Spatial Statistics URL https://doi.org/10.1016/j.spasta.2017.10.003
- Alvarez Prado, S., Sanchez, I., Cabrera Bosquet, L., Grau, A., Welcker, C., Tardieu, F., Hilgert, N. (2019). To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?. Journal of Experimental Botany, 70 (15), 3693-3698. , DOI : 10.1093/jxb/erz191 https://prodinra.inra.fr/record/481355
sanchezi/phisStatR documentation built on Nov. 14, 2019, 7:10 p.m.
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Objective
library(dplyr) library(tidyr) library(phisStatR) library(ggplot2)
This vignette deals with the detection of outlier plants in a lattice experiment using a spatial model using splines (SpATS library) [3]. Here the following steps of this procedure developed for Maize experiment but easily adaptable to others species:
- From a temporal dataset designed as plant1 with biomass (or biovolume) and plant height phenotypes. Extract predictions of these two phenotypes at a specific time point (for example just before the treatment) and create a dataset designed as plant4. Here, we will use the biomass and plant height predicted at 24 days et 20°C.
- From a temporal dataset of count of number of leaves, extract the phyllocron (slope of linear regression for each plant)
We have so a dataset with one row for each plant in the experiment containing the three phenotypes: biomass24, PH24 and Phy.
- Apply a SpATS model on each phenotype, check the diagnostic graphics and retrieve the deviance residuals calculated by the model
-
On the residuals, we can detect outlier plant(s) with a combined physiological criterion applying the following rules:
-
raw procedure: at a threshold=0.95 (can be modified)
- small plants are identified if $res_i < \mu_{res} - qnorm(threshold) \times sd_{res}$ for biomass AND phyllocron
- big plants are identified if $res_i > \mu_{res} + qnorm(threshold) \times sd_{res}$ for biomass AND plant height
Import of data
In this vignette, we use a toy data set of the phisStatR library (anonymized real data set). the first one plant4 is extracted from plant1 and is used to detect outlier at a specific time point. The detected outlier will be checked on the plant1 dataset graphically.
mydata<-plant4 str(mydata)
SpATS library
Biomass
spat.biomass<-fitSpATS(datain=mydata,trait="Biomass24",genotypeId="genotypeAlias",rowId="Line",colId="Position", typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.biomass)
Plant height
spat.ph<-fitSpATS(datain=mydata,trait="PH24",genotypeId="genotypeAlias",rowId="Line",colId="Position", typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.ph)
Phyllocron
spat.phy<-fitSpATS(datain=mydata,trait="Phy",genotypeId="genotypeAlias",rowId="Line",colId="Position", typeModel="anova",genotype.as.random=FALSE,nseg=c(14,30),verbose)
plot(spat.phy)
Data cleaning
The data cleaning procedure is to identify plants having too large or too small features, from deviance residuals obtained with SpATS mixed models for each parameter (biomass, plant height and phyllocron). 2 procedures are tested:
- raw procedure: at a threshold=0.95 or 0.90 or 0.98 (can be modified)
- small plants are identified if $res_i < \mu_{res} - qnorm(threshold) \times sd_{res}$ for biomass AND phyllocron
- big plants are identified if $res_i > \mu_{res} + qnorm(threshold) \times sd_{res}$ for biomass AND plant height
- quantile procedure: Same procedure with quantiles :
- small plants are identified if $res_{i} < Q1_{res} - 1.5 \times(Q3 - Q1)$ for biomass AND phyllocron
- big plants are identified if $res_{i} > Q1_{res} - 1.5 \times(Q3 - Q1)$ for biomass AND plant height
# concatenate raw data and residuals calculated with the SpATS model devResBio<-spat.biomass$residuals devResPH<-spat.ph$residuals devResPhy<-spat.phy$residuals myglobal<-cbind.data.frame(mydata,devResBio,devResPH,devResPhy) threshold<-0.95 myglobal<-mutate(myglobal,mean.devBio=mean(devResBio,na.rm=TRUE), sd.devBio=sd(devResBio,na.rm=TRUE), mean.devPH=mean(devResPH,na.rm=TRUE), sd.devPH=sd(devResPH,na.rm=TRUE), mean.devPhy=mean(devResPhy,na.rm=TRUE), sd.devPhy=sd(devResPhy,na.rm=TRUE), lower.devBio=mean.devBio - sd.devBio*qnorm(threshold), lower.devPH=mean.devPH - sd.devPH*qnorm(threshold), lower.devPhy=mean.devPhy - sd.devPhy*qnorm(threshold), upper.devBio=mean.devBio + sd.devBio*qnorm(threshold), upper.devPH=mean.devPH + sd.devPH*qnorm(threshold), upper.devPhy=mean.devPhy + sd.devPhy*qnorm(threshold), # yes=1 == OK # no =0 == problem lower.critraw.spatsBio=ifelse(devResBio - lower.devBio > 0,yes=1,no=0), lower.critraw.spatsPH=ifelse(devResPH - lower.devPH > 0,yes=1,no=0), lower.critraw.spatsPhy=ifelse(devResPhy - lower.devPhy > 0,yes=1,no=0), upper.critraw.spatsBio=ifelse(devResBio - upper.devBio < 0,yes=1,no=0), upper.critraw.spatsPH=ifelse(devResPH - upper.devPH < 0,yes=1,no=0), upper.critraw.spatsPhy=ifelse(devResPhy - upper.devPhy < 0,yes=1,no=0), Q1.devBio=quantile(devResBio,probs=0.25,na.rm=TRUE) - 1.5*(quantile(devResBio,probs=0.75,na.rm=TRUE) - quantile(devResBio,probs=0.25,na.rm=TRUE)), Q1.devPH=quantile(devResPH,probs=0.25,na.rm=TRUE) - 1.5*(quantile(devResPH,probs=0.75,na.rm=TRUE) - quantile(devResPH,probs=0.25,na.rm=TRUE)), Q1.devPhy=quantile(devResPhy,probs=0.25,na.rm=TRUE) - 1.5*(quantile(devResPhy,probs=0.75,na.rm=TRUE) - quantile(devResPhy,probs=0.25,na.rm=TRUE)), Q3.devBio=quantile(devResBio,probs=0.75,na.rm=TRUE) + 1.5*(quantile(devResBio,probs=0.75,na.rm=TRUE) - quantile(devResBio,probs=0.25,na.rm=TRUE)), Q3.devPH=quantile(devResPH,probs=0.75,na.rm=TRUE) + 1.5*(quantile(devResPH,probs=0.75,na.rm=TRUE) - quantile(devResPH,probs=0.25,na.rm=TRUE)), Q3.devPhy=quantile(devResPhy,probs=0.75,na.rm=TRUE) + 1.5*(quantile(devResPhy,probs=0.75,na.rm=TRUE) - quantile(devResPhy,probs=0.25,na.rm=TRUE)), lower.critci.spatsBio=ifelse(devResBio - Q1.devBio > 0,yes=1,no=0), lower.critci.spatsPH=ifelse(devResPH - Q1.devPH > 0,yes=1,no=0), lower.critci.spatsPhy=ifelse(devResPhy - Q1.devPhy > 0,yes=1,no=0), upper.critci.spatsBio=ifelse(devResBio - Q3.devBio < 0,yes=1,no=0), upper.critci.spatsPH=ifelse(devResPH - Q3.devPH < 0,yes=1,no=0), upper.critci.spatsPhy=ifelse(devResPhy - Q3.devPhy < 0,yes=1,no=0) ) # small plants # yes=1 == OK # no =0 == problem myglobal<-mutate(myglobal, flagLowerRawSpats=ifelse(lower.critraw.spatsBio + lower.critraw.spatsPhy==0,yes=0,no=1), flagLowerCiSpats=ifelse(lower.critci.spatsBio + lower.critci.spatsPhy==0,yes=0,no=1) ) # big plants # yes=1 == OK # no =0 == problem myglobal<-mutate(myglobal, flagUpperRawSpats=ifelse(upper.critraw.spatsBio + upper.critraw.spatsPH==0,yes=0,no=1), flagUpperCiSpats=ifelse(upper.critci.spatsBio + upper.critci.spatsPH==0,yes=0,no=1) )
The user can save the residuals and detected outliers in an output file.
write.table(myglobal,"OutputFile_detectOutlierCurves_SpATS.csv",row.names=FALSE,sep="\t")
Summary of the cleaning procedure:
- Number of too small plants according to raw criteria:
r ifelse(length(table(myglobal[,"flagLowerRawSpats"]))==1, 0,table(myglobal[,"flagLowerRawSpats"])[[1]]) - Number of too big plants according to raw criteria:
r ifelse(length(table(myglobal[,"flagUpperRawSpats"]))==1, 0,table(myglobal[,"flagUpperRawSpats"])[[1]]) - Number of too small plants according to quantile criteria:
r ifelse(length(table(myglobal[,"flagLowerCiSpats"]))==1, 0,table(myglobal[,"flagLowerCiSpats"])[[1]]) - Number of too big plants according to quantile criteria:
r ifelse(length(table(myglobal[,"flagUpperCiSpats"]))==1, 0,table(myglobal[,"flagUpperCiSpats"])[[1]])
raw criteria
cat("Lower raw outlier:") myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagLowerRawSpats) %>% filter(flagLowerRawSpats !=1) cat("Upper raw outlier:") myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagUpperRawSpats) %>% filter(flagUpperRawSpats !=1)
quantile criteria
cat("Lower quantile outlier:") myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagLowerCiSpats) %>% filter(flagLowerCiSpats !=1) cat("Upper quantile outlier:") myglobal %>% select(Ref,genotypeAlias,repetition,Biomass24,PH24,Phy,flagUpperCiSpats) %>% filter(flagUpperCiSpats !=1)
raw criteria : Plots
The user can check the outlier curves detected by this procedure. We use plant1 the dataset containing all the phenotypes of the same toy experiment as plant4 but over thermal time.
outlierId<-filter(myglobal,flagLowerRawSpats !=1 | flagUpperRawSpats !=1) %>% select(genotypeAlias,scenario,repetition,flagLowerRawSpats,flagUpperRawSpats) %>% unite("geno_sce",genotypeAlias,scenario,sep="_",remove=FALSE) mycurve<-plant1 %>% unite("geno_sce",genotypeAlias,scenario,sep="_",remove=FALSE) %>% left_join(outlierId,mycurve,by=c("geno_sce","repetition")) # flagLower ou upper == 0 problem # flagLower ou upper == 1 OK # flag == "lower" problem en lower # flag == "upper" problem en upper # flag == "OK" plant OK tmp<-filter(mycurve,geno_sce %in% outlierId[,"geno_sce"]) %>% mutate(flagLowerRawSpats=ifelse(is.na(flagLowerRawSpats),1,flagLowerRawSpats), flagUpperRawSpats=ifelse(is.na(flagUpperRawSpats),1,flagUpperRawSpats), flag=case_when(flagLowerRawSpats == 0 ~ "lower", flagUpperRawSpats == 0 ~ "upper", flagUpperRawSpats + flagLowerRawSpats ==2 ~ "OK" ))
ggplot(data=tmp,aes(x=thermalTime,y=biovolume,color=as.factor(repetition))) + geom_line() + geom_point(aes(shape=flag,color=as.factor(repetition))) + scale_shape_manual(values = c(0,19,2)) + facet_wrap(~geno_sce)
ggplot(data=tmp,aes(x=thermalTime,y=plantHeight,color=as.factor(repetition))) + geom_line() + geom_point(aes(shape=flag,color=as.factor(repetition))) + scale_shape_manual(values = c(0,19,2)) + facet_wrap(~geno_sce)
Session info
sessionInfo()
References
- R Development Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
- Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2017). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics URL https://doi.org/10.1016/j.spasta.2017.10.003
- Alvarez Prado, S., Sanchez, I., Cabrera Bosquet, L., Grau, A., Welcker, C., Tardieu, F., Hilgert, N. (2019). To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?. Journal of Experimental Botany, 70 (15), 3693-3698. , DOI : 10.1093/jxb/erz191 https://prodinra.inra.fr/record/481355
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