Workflow
JalilRT; Victor
2021-01-14
Last updated: 2021-01-15
Checks: 7 0
Knit directory: Pragmatic-language-dataset-code/
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Preprocessing
Cortical thickness as a Psychometric predictor
Load the packages
pacman::p_load(tidyverse,dplyr,
caret, pROC,
readxl, cowplot,
caTools,
class)Calculating k-nn neighboors regression
## CAT12 ##
lr.cat=read.csv("data/ROI_catROIs_aparc_DK40_thickness.csv")
lr.cat.r<-lr.cat %>% select(starts_with('r'))
not_all_na <- function(x) any(!is.na(x))
lr.cat.r<-lr.cat.r %>% select_if(not_all_na)
lr.cat<-lr.cat %>% mutate(rMeanThickness=rowMeans(lr.cat.r))
lr.cat<-data.frame(lr.cat[-139,],software=rep("1",145))
lr.cat$names<-str_replace(lr.cat$names,"_T1w","")
lr.cat<-lr.cat %>% mutate(MeanThickness=lr.cat %>% select(rMeanThickness,lMeanThickness) %>% rowMeans())
## FS ##
lh.fs=read.csv("data/FS_aparc_lh_thickness.csv",sep = "\t",stringsAsFactors = T)
rh.fs=read.csv("data/FS_aparc_rh_thickness.csv",sep = "\t",stringsAsFactors = T)
lr.fs=cbind(lh.fs,rh.fs)
lr.fs=select(lr.fs,-c("BrainSegVolNotVent","eTIV","rh.aparc.thickness"))
lr.fs=lr.fs %>% mutate(Order = as.numeric(gsub("sub-", "", lh.aparc.thickness))) %>% arrange(Order)
lr.fs=data.frame(lr.fs[-139,],software=rep("0",145))
col.lr.fs=str_replace(colnames(lr.fs),"_thickness","")
colnames(lr.fs)=col.lr.fs
col.lr.fs=str_replace(colnames(lr.fs),"h_","")
colnames(lr.fs)=col.lr.fs
lr.fs<-lr.fs %>% mutate(MeanThickness=lr.fs %>% select(rMeanThickness,lMeanThickness) %>% rowMeans())
## CIVET ##
lr.civet.l=read.csv("data/Civet_aparc_lh_thickness.csv")
lr.civet.r=read.csv("data/Civet_aparc_rh_thickness.csv")
lr.civet=cbind(lr.civet.l,lr.civet.r)
lr.civet=lr.civet[-34]
lr.civet=data.frame(lr.civet,software=rep("2",145))
lr.civet<-lr.civet %>% mutate(MeanThickness=lr.civet %>% select(rMeanThickness,lMeanThickness) %>% rowMeans())
#### Merge Software values #### only 31 structures per hemisphere
l.cfsci<-list(lr.fs,lr.cat,lr.civet)
fscc.lr <- Reduce(function(...) merge(..., all=TRUE), l.cfsci)
lr.fs.cat.civet<-fscc.lr %>% select_if(~!any(is.na(.)) > 0)
### Convert to factors ###
lr.fs.cat.civet$software <- factor(lr.fs.cat.civet$software,
levels = c(1, 0, 2),
labels = c("cat", "fs","civet"))
### Select a samples ###
set.seed(42)
######## k-Nearest neighbors (kNN) ########
######## Classification of laterality for FreeSurfer ########
lr.fs<-lr.fs.cat.civet %>% filter(software == "fs")
psych.data<-read.csv("data/data_fluidez_sst_edim2.csv")
lr.fs.psych<-data.frame(cbind(lr.fs[-81,-65],psych.data[-c(139,81),-1])) #remove left side participant = 81
lr.fs.psych<-na.omit(lr.fs.psych)
# Training classification
lr.fs.psych$Hemis<-factor(lr.fs.psych$Hemis)
fs.train.c <- createDataPartition(y = lr.fs.psych$Hemis, p = 0.7, list = FALSE) #training with proportion (p)
fs.train <- lr.fs.psych[fs.train.c,]
fs.test <- lr.fs.psych[-fs.train.c,]
## parameters ##
#control with more
sof.control <- trainControl(
method = "repeatedcv",
number = 10, repeats = 3,
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = "final")
#preprocessing with center (mean) and scale (sd), this is for normalizing
fs.knntrain <- train(Hemis ~ .,
data = fs.train,
method = "knn",
metric="ROC",
tuneLength = 20,
trControl = sof.control,
preProc = c("center","scale"),
tuneGrid = expand.grid(k = 1:60))
class(fs.knntrain)
fs.knntrain
plot(fs.knntrain) #accuracy plot with different k
| Version | Author | Date |
|---|---|---|
| 57fe138 | JalilRT | 2021-01-15 |
varImp(fs.knntrain)
## Predict ##
fs.knnPrediccion <- predict(fs.knntrain, newdata = fs.test )
confusionMatrix(fs.knnPrediccion, fs.test$Hemis) #accuracy of model and others
## REGRESSION
reg.control <- trainControl(
method = "repeatedcv",
number = 10, repeats = 3,
verboseIter = TRUE,
savePredictions = "final")
reg.fs.knntrain<-train(Edimburgo ~ .,
data = fs.train %>% select(-c("Hemis","Fluidez","SSTcomp","MeanThickness","rMeanThickness","lMeanThickness")),
tuneGrid = expand.grid(k=1:70),
method = 'knn',
metric = 'Rsquared',
trControl = reg.control,
preProc = c('center', 'scale'))
reg.fs.psych.predictions <- reg.fs.knntrain %>% predict(fs.test %>% select(-c("Hemis","Fluidez","SSTcomp")))
# Fluidez
freg.fs.knntrain<-train(Fluidez ~ .,
data = fs.train %>% select(-c("Hemis","Edimburgo","SSTcomp","MeanThickness","rMeanThickness","lMeanThickness")),
tuneGrid = expand.grid(k=1:70),
method = 'knn',
metric = 'Rsquared',
trControl = reg.control,
preProc = c('center', 'scale'))
freg.fs.psych.predictions <- freg.fs.knntrain %>% predict(fs.test%>% select(-c("Hemis","Edimburgo","SSTcomp","MeanThickness","rMeanThickness","lMeanThickness")))
# SSTcomp
sreg.fs.knntrain<-train(SSTcomp ~ .,
data = fs.train%>% select(-c("Hemis","Edimburgo","Fluidez","MeanThickness","rMeanThickness","lMeanThickness")),
tuneGrid = expand.grid(k=1:70),
method = 'knn',
metric = 'Rsquared',
trControl = reg.control,
preProc = c('center', 'scale'))
sreg.fs.psych.predictions <- sreg.fs.knntrain %>% predict(fs.test%>% select(-c("Hemis","Edimburgo","Fluidez","MeanThickness","rMeanThickness","lMeanThickness")))
### Comparison metrics
# Edimburgo
varImp(reg.fs.knntrain)
fs.mean.pred<-tibble(MeanThickness=fs.test$MeanThickness,Predictions=reg.fs.psych.predictions,Observed=fs.test$Edimburgo)
fs.mean.pred<-fs.mean.pred %>% gather("Group","Edinburgh",2:3)
ggthemr::ggthemr("fresh")
fs_edim<-ggplot(fs.mean.pred,aes(x=Edinburgh,y=MeanThickness, colour=Group))+geom_point()+
ylab("Mean Thickness (mm)")+
geom_smooth(method = "lm", formula = y~x, se = F) + theme(legend.position = c(.2, .9),
legend.title = element_blank())
# Fluidez
varImp(freg.fs.knntrain)
fs.mean.pred.Fluidez<-tibble(MeanThickness=fs.test$MeanThickness,Predictions=freg.fs.psych.predictions,Observed=fs.test$Fluidez)
fs.mean.pred.Fluidez<-fs.mean.pred.Fluidez %>% gather("Group","Fluency",2:3)
fs_flu<-ggplot(fs.mean.pred.Fluidez,aes(x=Fluency,y=MeanThickness, colour=Group))+geom_point()+
ylab("")+
geom_smooth(method = "lm", formula = y~x, se = F)+ theme(legend.position = c(.85, .9),
legend.title = element_blank())
# SSTcom
asdf<-varImp(sreg.fs.knntrain)
plot(asdf,top=4)
| Version | Author | Date |
|---|---|---|
| 57fe138 | JalilRT | 2021-01-15 |
fs.mean.pred.SSTcom<-tibble(MeanThickness=fs.test$MeanThickness,Predictions=round(sreg.fs.psych.predictions),Observed=fs.test$SSTcom)
fs.mean.pred.SSTcom<-fs.mean.pred.SSTcom %>% gather("Group","Comprehension",2:3)
fs_sst<-ggplot(fs.mean.pred.SSTcom,aes(x=Comprehension,y=MeanThickness, colour=Group))+geom_point()+
ylab("")+
geom_smooth(method = "lm", formula = y~x, se = F)+ theme(legend.position = c(.2, .9),
legend.title = element_blank())
##### Plot figure #####
FS_145<-ggdraw()+draw_image("/home/jalil/Documents/Doctorado/Pragmaticlab/paper_dataR/Project/145_FS.png")
fig1<-plot_grid(FS_145,fs_flu,fs_edim,fs_sst, labels = "AUTO")Plots
FS_145
| Version | Author | Date |
|---|---|---|
| 57fe138 | JalilRT | 2021-01-15 |
fig1
| Version | Author | Date |
|---|---|---|
| 57fe138 | JalilRT | 2021-01-15 |
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_MX.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=es_MX.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=es_MX.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] class_7.3-15 caTools_1.18.0 cowplot_1.1.0 readxl_1.3.1
[5] pROC_1.16.2 caret_6.0-86 lattice_0.20-38 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-151 bitops_1.0-6 fs_1.5.0
[4] lubridate_1.7.9 httr_1.4.2 rprojroot_1.3-2
[7] tools_3.6.3 backports_1.1.10 R6_2.5.0
[10] rpart_4.1-15 mgcv_1.8-31 DBI_1.1.0
[13] colorspace_1.4-1 nnet_7.3-12 withr_2.3.0
[16] tidyselect_1.1.0 compiler_3.6.3 git2r_0.27.1
[19] cli_2.1.0 rvest_0.3.6 pacman_0.5.1
[22] ggthemr_1.1.0 xml2_1.3.2 labeling_0.4.2
[25] scales_1.1.1 digest_0.6.27 rmarkdown_2.5
[28] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[31] rlang_0.4.9 rstudioapi_0.11 farver_2.0.3
[34] generics_0.0.2 jsonlite_1.7.1 ModelMetrics_1.2.2.2
[37] magrittr_2.0.1 Matrix_1.2-18 Rcpp_1.0.5
[40] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[43] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[46] MASS_7.3-53 plyr_1.8.6 recipes_0.1.14
[49] grid_3.6.3 blob_1.2.1 promises_1.1.1
[52] crayon_1.3.4 haven_2.3.1 splines_3.6.3
[55] hms_0.5.3 magick_2.5.2 knitr_1.30
[58] pillar_1.4.6 reshape2_1.4.4 codetools_0.2-16
[61] stats4_3.6.3 reprex_0.3.0 glue_1.4.2
[64] evaluate_0.14 data.table_1.13.2 modelr_0.1.8
[67] vctrs_0.3.4 httpuv_1.5.4 foreach_1.5.1
[70] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[73] xfun_0.19 gower_0.2.2 prodlim_2019.11.13
[76] broom_0.7.2 e1071_1.7-4 later_1.1.0.1
[79] survival_3.1-8 timeDate_3043.102 iterators_1.0.13
[82] lava_1.6.8 ellipsis_0.3.1 ipred_0.9-9