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ICH_MRI_v7.Rmd
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ICH_MRI_v7.Rmd
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---
title: "Consiousness - MRI in ICH Project"
output:
html_document: default
pdf_document: default
word_document: default
---
# Morphological data analysis (volumetrie & location)
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r preproc, include = FALSE}
rm(list = ls())
library(readxl)
library(ggplot2)
library(dplyr)
library(reshape2)
library(doBy)
#setwd("/Volumes/groups/NICU/Shared Projects/Jan Claassen/MRI Consciousness Plots/")
setwd("E:/Experiments/ICH_MRI/Ben files/MRI_ICH")
#setwd("/Users/rohaut/Documents/Data/Columbia/ICH_MRI/")
#setwd("E:/Experiments/ICH_MRI/Ben files/MRI_ICH")
# New CS score data
new_CS <- read_excel("E:/Experiments/ICH_MRI/Command_scores_from_daily.xlsx")
new_CS$MRN <- as.numeric(new_CS$MRN)
#Loc <- read_excel("ICH_MRI_location-asr.xlsx", sheet=1)
## Does this new file have the volume info in it now too?
## Yes, it does.
## Can just reassign the "Vol" dataframe as a subset of the new "Loc" dataframe
Loc <- read_excel("ICH-FinalCompilationFinal_v4-ASRinsula_filled.xlsx", sheet=1)
#Vol <- read_excel("Jan_Claassen_ICH_Study_Calc.xlsx", sheet=1)
# set vol as the beginning part of the new loc table
Vol <- Loc[,c("MRN", "Hemorrhage Volume", "Edema Volume", "Total Brain Volume", "Ratio H/B", "Ratio E/B", "Ratio T(H+E)/B")]
# now subset Loc to have the same columns as before
Loc <- Loc[,c(2,9:ncol(Loc))]
# remove note and 'Fun extra info' column
Loc <- Loc[,-which(names(Loc)=="(write Y/N or ordinal scale, state location)" | names(Loc)=="Fun extra info" |
names(Loc)=="X__1" | names(Loc)=="X__2" | names(Loc)=="X__3" | names(Loc)=="Sum")]
Outcome <- read_excel("MRI Imaging list radiology comprehensive-br.xlsx", sheet=1)
# merge the new outcome scores to the outcome table
Outcome <- merge(Outcome, new_CS[,c("MRN", "CS_ICU_Dch")], by="MRN", all.x=T)
Death <-read_excel("DeathDates.xlsx", sheet=1)
names(Loc)[names(Loc)=="Subfalcine herniation"] <- "sf_hern"
names(Loc)[names(Loc)=="MRI date"] <- "MRI_date_Loc"
names(Vol)[names(Vol)=="Hemorrhage Volume"] <- "Hg_vol"
names(Vol)[names(Vol)=="Edema Volume"] <- "Ed_vol"
names(Vol)[names(Vol)=="Total Brain Volume"] <- "Brain_vol"
#names(Outcome)[names(Outcome)=="Command score before discharge"] <- "follow"
# set the new command score column as "follow"
names(Outcome)[names(Outcome)=="CS_ICU_Dch"] <- "follow"
names(Outcome)[names(Outcome)=="Command Score @ time of MRI eval"] <- "MRI_Cs"
names(Death)[names(Death)=="PATIENT_ID"] <- "MRN"
# correction data
# remove duplicate death dates (one day gap, keep first)
Death[!is.na(Death$MRN) & Death$MRN==5508540,][2,]<-NA
Death[!is.na(Death$MRN) & Death$MRN==1917504,][2,]<-NA
```
## 1) Check volume data
**Note:** This need to be fixed w/ Key new data and Alex before recompute logistic regressions
(We still have a lot of NAs because of duplicates)
#### Brain Volume:
```{r volume, echo=FALSE, warning=FALSE, fig.cap="Brain volume"}
# Control of brain volumes # to be fixed
Vol$Brain_vol<-as.numeric(Vol$Brain_vol)
Vol$Hg_vol<-as.numeric(Vol$Hg_vol)
Vol$Ed_vol<-as.numeric(Vol$Ed_vol)
Vol$MRN<-as.numeric(Vol$MRN)
boxplot(Vol$Brain_vol)
# find patients w/ < 1000000 brain volume
Vol[Vol$Brain_vol<1000000,]$MRN
summary(as.numeric(Vol$Brain_vol))
```
outlier is corrected later by reassigning the mean of the group (incomplet brain volume)
#### Hemorage Volume & Median shift
```{r, echo=TRUE, warning=FALSE, fig.cap="Hemorage volume"}
# Control of brain volumes # to be fixed
boxplot(Vol$Hg_vol)
# patients with an outlier hemorrhage volume
Vol[Vol$Hg_vol > 150000,]$MRN
summary(as.numeric(Vol$Hg_vol))
boxplot(Loc$`MLS [mm]`)
summary(Loc$`MLS [mm]`)
```
#### Edema Volume
```{r, echo=FALSE, warning=FALSE, fig.cap="Edema volume"}
# Control of brain volumes # to be fixed
boxplot(Vol$Ed_vol)
if (length(Vol[!is.na(Vol$Ed_vol) & Vol$Ed_vol == 0,]$Ed_vol) != 0) {
Vol[!is.na(Vol$Ed_vol) & Vol$Ed_vol == 0,]$Ed_vol<-NA
}
# outliers for edema volume
Vol[Vol$Ed_vol > 150000,]$MRN
summary(as.numeric(Vol$Ed_vol))
```
```{r, echo=FALSE}
data.raw <-merge(Loc, Vol, by.x= c("MRN"), all.x=TRUE, all.y=FALSE)
data.raw <-merge( data.raw, Outcome, by=c("MRN"),all.x=TRUE)
data.raw <-merge( data.raw, Death, by=c("MRN"),all.x=TRUE)
# recode errors or ambiguous data
# table (data.raw$follow); table (data.raw$MRI_Cs)
try(data.raw[!is.na(data.raw$follow) & data.raw$follow=='3 or 4',]$follow <- 3, silent=TRUE)
data.raw[!is.na(data.raw$MRI_Cs) & data.raw$MRI_Cs == "4 or 5",]$MRI_Cs <- 4
```
## 2) Check consciousness data
We have 158 patients with both anat location & consciousness mesurements (@MRI & discharge)
Definition Score
----------- -------
No eyes openning 0
Opens eyes to stimulation 1
Opens eyes spontaneously 2
Tracks/attends 3
Follows simple commands 4
Follows complex commands/oriented 5
Table: Consciousness ICU scale reminder.
```{r, include = FALSE}
# shape dates
data.raw$Discharge_date<-as.Date(data.raw$Discharge_date)
data.raw$DEATH_DATE<-as.Date(data.raw$DEATH_DATE)
data.raw$MRI_date_Loc<-as.numeric(data.raw$MRI_date_Loc)
data.raw$MRI_date_Loc<-as.Date(data.raw$MRI_date_Loc,origin = "1899-12-30")
```
Patient who died in ICU:
```{r died patients, echo=FALSE}
dim(data.raw[!is.na(data.raw$DEATH_DATE) & (data.raw$Discharge_date>=data.raw$DEATH_DATE)==TRUE,])[1]
# Correction for patient who died in ICU: "following at discharge" = 0 (n=1)
data.raw[!is.na(data.raw$DEATH_DATE) & (data.raw$Discharge_date>=data.raw$DEATH_DATE)==TRUE,]$follow <- 0
```
Five patients died in ICU; consciousness at discharge coded as 0
```{r Consciousness data, echo=FALSE}
# dataframe for anat plot
####### WILL NEED TO CHANGE THE INDICES HERE IF ADD ANY NEW COLUMNS ######
loc_analyse.raw<-subset(data.raw,select=c(MRN,4,7:93,follow,MRI_Cs,Discharge_date,DEATH_DATE,MRI_date_Loc,mri_date))
loc_analyse.raw$follow<-as.integer(loc_analyse.raw$follow)
loc_analyse.raw$MRI_Cs<-as.integer(loc_analyse.raw$MRI_Cs)
barplot(table(loc_analyse.raw$MRI_Cs))
title(main = list("1: Consiousness at MRI", font = 4))
table(loc_analyse.raw$MRI_Cs)
sum(table(loc_analyse.raw$MRI_Cs))
barplot(table(loc_analyse.raw$follow))
title(main = list("2: Consiousness at discharge", font = 4))
table(loc_analyse.raw$follow)
sum(table(loc_analyse.raw$follow))
```
```{r ipsi/contro, echo=FALSE}
library(dplyr)
# Add ispi / controlateral variables
#### Do the sub_tent and sus_tent checking ####
## Not taking hypothalmus into accounr (Hypo_ICH_C. Hypo edema wasn't in list)
infra_tent_R <- c("AntPons_ICH_R",
"Teg_ICH_R",
"Cereb_ICH_R",
# "Vermis_ICH",
# "MB_ICH_C",
"MB_peduncle_ICH_R")
infra_tent_L <- c("AntPons_ICH_L",
"Teg_ICH_L",
"Cereb_ICH_L",
# "Vermis_ICH",
# "MB_ICH_C",
"MB_peduncle_ICH_L")
supra_tent_R <- c("TH_ant_ICH_R",
"TH_lat_ICH_R",
"TH_med_ICH_R",
"TH_post_ICH_R",
"GP_ICH_R",
"PUT_ICH_R",
"Caudate_ICH_R",
"IC_ant_ICH_R",
"IC_post_ICH_R",
"FCx_ICH_R",
"PCx_ICH_R",
"TCx_ICH_R",
"OCx_ICH_R",
"INS_ICH_R")
supra_tent_L <- c("TH_ant_ICH_L",
"TH_lat_ICH_L",
"TH_med_ICH_L",
"TH_post_ICH_L",
"GP_ICH_L",
"PUT_ICH_L",
"Caudate_ICH_L",
"IC_ant_ICH_L",
"IC_post_ICH_L",
"FCx_ICH_L",
"PCx_ICH_L",
"TCx_ICH_L",
"OCx_ICH_L",
"INS_ICH_L")
# melt seperate tables containing just the MRN and the respective names, and then merge them together
# to have 3 seperate columsns for each area to check.
loc_analyse.infRm <- melt(loc_analyse.raw[,c("MRN", infra_tent_R)], id="MRN")
loc_analyse.infLm <- melt(loc_analyse.raw[,c("MRN", infra_tent_L)], id="MRN")
loc_analyse.supRm <- melt(loc_analyse.raw[,c("MRN", supra_tent_R)], id="MRN")
loc_analyse.supLm <- melt(loc_analyse.raw[,c("MRN", supra_tent_L)], id="MRN")
# rename the variable and value column names
colnames(loc_analyse.infRm)<- c("MRN", "infR_name", "infR_value")
colnames(loc_analyse.infLm)<- c("MRN", "infL_name", "infL_value")
colnames(loc_analyse.supRm) <- c("MRN", "supR_name", "supR_value")
colnames(loc_analyse.supLm) <- c("MRN", "supL_name", "supL_value")
# now merge.
# This creates "duplicate" rows based on all the various combos you can (and have to) make
# between all the Sub, R, and L names
# but that shouldn't matter, because we're just checking if a 1 exists in any of these columns
# per patient, and that shouldn't change even if rows are "duplicated"
loc_analyse.infra.m <- merge(loc_analyse.infRm, loc_analyse.infLm)
loc_analyse.supr.m <- merge(loc_analyse.supRm, loc_analyse.supLm)
loc_analyse.m <- merge(loc_analyse.infra.m, loc_analyse.supr.m)
# create two new columns, one for the sub_tent stuff and another for the L, R, Both, None stuff
# then check each column for each patient and assign to these new columns accordingly
loc_analyse.raw2 <- loc_analyse.raw
loc_analyse.raw2$infra_tent <- NA
loc_analyse.raw2$supra_tent <- NA
# find unique patient IDs to loop over
ID.u <- unique(loc_analyse.raw2$MRN)
# check via looping over each MRN
# I think I need to do this without dplyr because
# that way, when filtering the table, I can keep the same indices of those filtered rows
# when assigning the value of 'sus_tentorial' back into the main table.
for (i in 1:length(ID.u)) {
tmp <- loc_analyse.m[loc_analyse.m$MRN == ID.u[i], ]
# check for L, R, Both, None for infra tent first
if (any(tmp$infL_value >= 1) & !any(tmp$infR_value >= 1)) { # if patient has lesion on left side and not right side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$infra_tent <- "L"
} else if (!any(tmp$infL_value >= 1) & any(tmp$infR_value >= 1)) { # if patient doesn't have lesion of left side but has on right side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$infra_tent <- "R"
} else if (any(tmp$infL_value >= 1) & any(tmp$infR_value >= 1)) { # if patient has lesion on both sides
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$infra_tent <- "Both"
} else if (!any(tmp$infL_value >= 1) & !any(tmp$infR_value >= 1)) { # if patient doesn't have lesion on either side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$infra_tent <- "None"
} else {
stop("patient value doesn't match any of these categories for infra tent")
}
# check for L, R, Both, None for supra tent
if (any(tmp$supL_value >= 1) & !any(tmp$supR_value >= 1)) { # if patient has lesion on left side and not right side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$supra_tent <- "L"
} else if (!any(tmp$supL_value >= 1) & any(tmp$supR_value >= 1)) { # if patient doesn't have lesion of left side but has on right side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$supra_tent <- "R"
} else if (any(tmp$supL_value >= 1) & any(tmp$supR_value >= 1)) { # if patient has lesion on both sides
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$supra_tent <- "Both"
} else if (!any(tmp$supL_value >= 1) & !any(tmp$supR_value >= 1)) { # if patient doesn't have lesion on either side
loc_analyse.raw2[loc_analyse.raw2$MRN == ID.u[i],]$supra_tent <- "None"
} else {
stop("patient value doesn't match any of these categories for infra tent")
}
}
table(loc_analyse.raw2$infra_tent, loc_analyse.raw2$supra_tent)
loc_analyse.raw2$supra_tent2 <- loc_analyse.raw2$supra_tent
loc_analyse.raw2$infra_tent2 <- loc_analyse.raw2$infra_tent
# Chance patients who had "Both" to whichever side has the dominant lesion.
# (These patients' scans were looked at afterwards to determine this)
## Ugly way to change the locations for these subset of patients.
# MRNstoChange <- c(6573486,2028844,4606033,5792222,5853384,3194111,3457859,3677658,4556221,5701092,6119448,6451744,6661386,6788523,7261043,3708159)
#
# #6573486,2028844,4606033,5792222,5853384,3194111,3457859,3677658,4556221,5701092,6119448,6451744,6661386,6788523,7261043,)
#
# newLocations <- c("L","R","L","L","L","R","R","R","L","L","R","R","L","R","L","R")
# #"L","R","L","L","L","R","R","R","L","L","R","R","L","R","L",)
#
# for (i in 1:length(MRNstoChange)) {
# loc_analyse.raw2[loc_analyse.raw2$MRN == MRNstoChange[i], "supra_tent2"] <- newLocations[i]
# loc_analyse.raw2[loc_analyse.raw2$MRN == MRNstoChange[i], "infra_tent2"] <- newLocations[i]
# }
# "better" way, as list.
# If a patient wound up having the larger lesion on R for supra and on the L for infra (or vice versa),
# then make a seperate list for supra and infra and then use those to assign.
## OR could make a list of a list of the values for supra and infra.
## like this:
# a <- list('MRN' = list('sup'='R', 'infra'='L'))
# and then can subset out sup and infra on each loop like:
# a[['MRN']][['sup']] or a[['MRN']][['infra']]
newLocationsforMRNs <- list(
'6573486'='L',
'2028844'='R',
'4606033'='L',
'5792222'='L',
'5853384'='L',
'3194111'='R',
'3457859'='R',
'3677658'='R',
'4556221'='L',
'5701092'='L',
'6119448'='R',
'6451744'='R',
'6661386'='L',
'6788523'='R',
'7261043'='L',
'3708159'='R',
# these patients have only IVH, so assigning them a fake R label to keep them in the analysis
'3284895'='R',
'4901703'='R',
'6625551'='R',
'6999193'='R'
)
for (mrn in names(newLocationsforMRNs)) {
loc_analyse.raw2[loc_analyse.raw2$MRN == mrn, "supra_tent2"] <- newLocationsforMRNs[[mrn]]
loc_analyse.raw2[loc_analyse.raw2$MRN == mrn, "infra_tent2"] <- newLocationsforMRNs[[mrn]]
}
table(loc_analyse.raw2$infra_tent2, loc_analyse.raw2$supra_tent2)
### So now loc_analyse.m contains info, for each patient, on whether or not
### they had a lesion in the sub_tent area or if they had a lesion in the sus_tent area
### on the left, right, both, or none of these sides.
## BEN: Kevin I changed minimally your script to get one line / patient (filling loc_analyse.raw2)
## Find patients who have (supra_tent == None & infra_tent == Both) and (supra_tent == Both & infra_tent == None)
# supra_tent: None infra_tent: Both
noSupBothInf <- loc_analyse.raw2 %>%
filter(supra_tent == "None" & infra_tent == "Both") %>%
dplyr::select(MRN, MRI_date_Loc)
bothSupNoInf <- loc_analyse.raw2 %>%
filter(supra_tent == "Both" & infra_tent == "None") %>%
dplyr::select(MRN, MRI_date_Loc)
bothSupBothInf <- loc_analyse.raw2 %>%
filter(supra_tent == "Both" & infra_tent == "Both") %>%
dplyr::select(MRN, MRI_date_Loc)
noSupNoInf <- loc_analyse.raw2 %>%
filter(supra_tent == "None" & infra_tent == "None") %>%
dplyr::select(MRN, MRI_date_Loc)
# also want where L in one but R in the other.
# only need for R sup and L inf, because there are no patients with L sup and R inf
RsupLinf <- loc_analyse.raw2 %>%
filter(supra_tent == "R" & infra_tent == "L") %>%
dplyr::select(MRN, MRI_date_Loc)
```
```{r recode following scale, echo=FALSE, warning=FALSE}
library(car)
loc_analyse.raw<- loc_analyse.raw2;
# now loc_analyse.raw has the 2 more row sub_tent & sus_tent
# recode following scale
# in binary in xxx2
# loc_analyse.raw$follow2<-loc_analyse.raw$follow
# loc_analyse.raw[loc_analyse.raw$follow2 <= 3,]$follow2<- 0
# loc_analyse.raw[loc_analyse.raw$follow2 > 3,]$follow2<- 1
#
# loc_analyse.raw$MRI_Cs2<- loc_analyse.raw$MRI_Cs
# loc_analyse.raw[!is.na(loc_analyse.raw$MRI_Cs2) & loc_analyse.raw$MRI_Cs2 <= 3,]$MRI_Cs2<- 0 # this was loc_analyse.raw$MRI_Cs2 <Cs_cutoff <- 0
# loc_analyse.raw[!is.na(loc_analyse.raw$MRI_Cs2) & loc_analyse.raw$MRI_Cs2 > 3,]$MRI_Cs2<- 1 # and loc_analyse.raw$MRI_Cs2 >=Cs_cutoff <- 1
# # making it 0-1 vs 3-5, but don't we want 0-3 vs 4-5?
# # in 3 groups in xxx3
# loc_analyse.raw$follow3<-loc_analyse.raw$follow
# loc_analyse.raw[loc_analyse.raw$follow3 < 1,]$follow3<- 0
# loc_analyse.raw[(loc_analyse.raw$follow3 >= 1) & (loc_analyse.raw$follow3 <4),]$follow3<- 1
# loc_analyse.raw[loc_analyse.raw$follow3 >3 ,]$follow3<- 2
#
# loc_analyse.raw$MRI_Cs3<- loc_analyse.raw$MRI_Cs
# loc_analyse.raw[!is.na(loc_analyse.raw$MRI_Cs3) & loc_analyse.raw$MRI_Cs3 <1,]$MRI_Cs3<- 0
# loc_analyse.raw[!is.na(loc_analyse.raw$MRI_Cs3) & (loc_analyse.raw$MRI_Cs3 >= 1) & (loc_analyse.raw$MRI_Cs3 <4) ,]$MRI_Cs3<- 1
# loc_analyse.raw[!is.na(loc_analyse.raw$MRI_Cs3) & loc_analyse.raw$MRI_Cs3 >3,]$MRI_Cs3<- 2
# Possible other way to recode. A little bit easier to read:
loc_analyse.raw$follow2<-loc_analyse.raw$follow
loc_analyse.raw$follow3<-loc_analyse.raw$follow
loc_analyse.raw$MRI_Cs2<- loc_analyse.raw$MRI_Cs
loc_analyse.raw$MRI_Cs3<- loc_analyse.raw$MRI_Cs
# 2 groups
loc_analyse.raw$follow2 <- recode(loc_analyse.raw$follow2, "0:3 = 0")
loc_analyse.raw$follow2 <- recode(loc_analyse.raw$follow2, "4:5 = 1")
loc_analyse.raw$MRI_Cs2 <- recode(loc_analyse.raw$MRI_Cs2, "0:3 = 0")
loc_analyse.raw$MRI_Cs2 <- recode(loc_analyse.raw$MRI_Cs2, "4:5 = 1")
# 3 groups
loc_analyse.raw$follow3 <- recode(loc_analyse.raw$follow3, "0 = 0") # unnecessary?
loc_analyse.raw$follow3 <- recode(loc_analyse.raw$follow3, "1:3 = 1")
loc_analyse.raw$follow3 <- recode(loc_analyse.raw$follow3, "4:5 = 2")
loc_analyse.raw$MRI_Cs3 <- recode(loc_analyse.raw$MRI_Cs3, "0 = 0") # unnecessary?
loc_analyse.raw$MRI_Cs3 <- recode(loc_analyse.raw$MRI_Cs3, "1:3 = 1")
loc_analyse.raw$MRI_Cs3 <- recode(loc_analyse.raw$MRI_Cs3, "4:5 = 2")
# plot with original 6 cat scale done in "plot consciousness data"
```
#### Split in 2 categories: 0 = No attending/traking / 1 = Attending/traking or better
```{r, echo=FALSE}
# plot with 2 cat
barplot(table(loc_analyse.raw$MRI_Cs2))
title(main = list("3: Consiousness at MRI", font = 4))
barplot(table(loc_analyse.raw$follow2))
title(main = list("4: Consiousness at discharge", font = 4))
table(loc_analyse.raw$MRI_Cs2); table(loc_analyse.raw$follow2)
```
#### Split in 3 categories: 0 = Coma / 1 = "Awakening" (1,2,3) / 2 = Following commands (4,5)
```{r, echo=FALSE,warning = FALSE}
# plot with 3 cat
barplot(table(loc_analyse.raw$MRI_Cs3))
title(main = list("5: Consiousness at MRI", font = 4))
barplot(table(loc_analyse.raw$follow3))
title(main = list("6: Consiousness at discharge", font = 4))
table(loc_analyse.raw$MRI_Cs3); table(loc_analyse.raw$follow3)
sum(table(loc_analyse.raw$MRI_Cs3))
# recodin cortex values >1 as 1
loc_analyse.raw[,2:76]<- ifelse(loc_analyse.raw[,2:76]>=1,1,0)
loc_analyse.raw[,2:76]<- lapply(loc_analyse.raw[,2:76], as.factor)
# rm NAs
#loc_analyse.raw <- loc_analyse.raw[complete.cases(loc_analyse.raw[,2:75]),] No need here
```
```{r, echo=FALSE,warning = FALSE}
library(dplyr)
# adding new variables
# Thalamus Thal_R Thal_L
loc_analyse.raw$TH_ICH_L <- NA
loc_analyse.raw$TH_ICH_R <- NA
loc_analyse.raw$TH_ICH_L <- ifelse(loc_analyse.raw[,"TH_ant_ICH_L"]==1 |loc_analyse.raw[,"TH_med_ICH_L"]==1|loc_analyse.raw[,"TH_lat_ICH_L"]==1 |loc_analyse.raw[,"TH_post_ICH_L"]==1 ,1,0)
loc_analyse.raw$TH_ICH_R <- ifelse(loc_analyse.raw[,"TH_ant_ICH_R"]==1 |loc_analyse.raw[,"TH_med_ICH_R"]==1|loc_analyse.raw[,"TH_lat_ICH_R"]==1 |loc_analyse.raw[,"TH_post_ICH_R"]==1 ,1,0)
loc_analyse.raw$TH_edema_L <- NA
loc_analyse.raw$TH_edema_R <- NA
loc_analyse.raw$TH_edema_L <- ifelse(loc_analyse.raw[,"TH_ant_edema_L"]==1 |loc_analyse.raw[,"TH_med_edema_L"]==1|loc_analyse.raw[,"TH_lat_edema_L"]==1 |loc_analyse.raw[,"TH_post_edema_L"]==1 ,1,0)
loc_analyse.raw$TH_edema_R <- ifelse(loc_analyse.raw[,"TH_ant_edema_R"]==1 |loc_analyse.raw[,"TH_med_edema_R"]==1|loc_analyse.raw[,"TH_lat_edema_R"]==1 |loc_analyse.raw[,"TH_post_edema_R"]==1 ,1,0)
# Mesocicuite MesoC_R MesoC_L MesoC
loc_analyse.raw$MesoC_ICH_R <- NA
loc_analyse.raw$MesoC_ICH_L <- NA
# loc_analyse.raw$MesoC_ICH_Bi <- NA
# loc_analyse.raw$MesoC_ICH_Uni <- NA
### BE CAREFUL!! MB_ICH_C is present in both Right and Left variables!
loc_analyse.raw$MesoC_ICH_R <- ifelse(loc_analyse.raw[,"FCx_ICH_R"]==1|
loc_analyse.raw[,"TH_ICH_R"]==1|
loc_analyse.raw[,"GP_ICH_R"]==1|
loc_analyse.raw[,"Caudate_ICH_R"]==1|
loc_analyse.raw[,"PUT_ICH_R"]==1|
loc_analyse.raw[,"MB_peduncle_ICH_R"]==1|
loc_analyse.raw[,"MB_ICH_C"]==1|
loc_analyse.raw[,"Teg_ICH_R"]==1 ,1,0)
loc_analyse.raw$MesoC_ICH_L <- ifelse(loc_analyse.raw[,"FCx_ICH_L"]==1|
loc_analyse.raw[,"TH_ICH_L"]==1|
loc_analyse.raw[,"GP_ICH_L"]==1|
loc_analyse.raw[,"Caudate_ICH_L"]==1|
loc_analyse.raw[,"PUT_ICH_L"]==1|
loc_analyse.raw[,"MB_peduncle_ICH_L"]==1|
loc_analyse.raw[,"MB_ICH_C"]==1|
loc_analyse.raw[,"Teg_ICH_L"]==1 ,1,0)
# loc_analyse.raw$MesoC_ICH_Bi<- ifelse(loc_analyse.raw[,"MesoC_ICH_R"]==1 &
# loc_analyse.raw[,"MesoC_ICH_L"]==1 ,1,0)
#
# loc_analyse.raw$MesoC_ICH_Uni<- ifelse(loc_analyse.raw[,"MesoC_ICH_R"]==1 |
# loc_analyse.raw[,"MesoC_ICH_L"]==1 ,1,0)
# Brainstem
loc_analyse.raw$BS_ICH <- NA
loc_analyse.raw$BS_ICH <- ifelse(loc_analyse.raw[,"MB_peduncle_ICH_R"]==1|
loc_analyse.raw[,"MB_peduncle_ICH_L"]==1|
loc_analyse.raw[,"MB_ICH_C"]==1|
loc_analyse.raw[,"Teg_ICH_R"]==1|
loc_analyse.raw[,"Teg_ICH_L"]==1 ,1,0)
## adding in multi-leveled meso-ICH code
## basically just sum up the number of occurrences to create the new value
### BE CAREFUL!! MB_ICH_C is present in both Right and Left variables!
# Right side
loc_analyse.raw <- loc_analyse.raw %>%
group_by(MRN) %>%
mutate(Meso_ICH_2_R = sum(as.numeric(as.character(FCx_ICH_R)),
as.numeric(as.character(TH_ICH_R)),
as.numeric(as.character(GP_ICH_R)),
as.numeric(as.character(Caudate_ICH_R)),
as.numeric(as.character(PUT_ICH_R)),
as.numeric(as.character(MB_peduncle_ICH_R)),
as.numeric(as.character(MB_ICH_C)),
as.numeric(as.character(Teg_ICH_R)))) %>%
# Left side
mutate(Meso_ICH_2_L = sum(as.numeric(as.character(FCx_ICH_L)),
as.numeric(as.character(TH_ICH_L)),
as.numeric(as.character(GP_ICH_L)),
as.numeric(as.character(Caudate_ICH_L)),
as.numeric(as.character(PUT_ICH_L)),
as.numeric(as.character(MB_peduncle_ICH_L)),
as.numeric(as.character(MB_ICH_C)),
as.numeric(as.character(Teg_ICH_L))))
```
**Note:** MesoC = TH + GP + Caudate + PUT + Teg + MB_pedoncle + MB_C + FCx
I took only ICH yet.
MB_C is present on both the L and R sides.
```{r, echo=FALSE,warning = FALSE}
# melting and using dplyr (thanks Kevin)
dat.m <- melt(loc_analyse.raw,
id=c("MRN", "follow", "MRI_Cs", "Discharge_date", "DEATH_DATE", "MRI_date_Loc", "mri_date",
"follow2","MRI_Cs2", "follow3","MRI_Cs3", "infra_tent", "supra_tent"))
# ERROR here why?
### This happens because some of the columns being melted are factors with different levels from one column to another.
### When it tries to melt these columns, it sees that the factor levels aren't the same between them, so it apparently
### just drops the levels and converts everything to character.
### So, basically, I don't think we really need to care about this warning.
# BEN: OK
# can't convert to numeric with the "L, R, Both, None" values in there
dat.m$value<-as.numeric(dat.m$value)
```
## 3) Plot anatomical data
Percentage (not mean as ploted) of patient with a lesion (ICH or eadema) in each ROIs according to level of consciousness (at time of MRI or discharge))
### Consiousness split in 2 categories
#### Right / Left analyse
```{r, echo=FALSE}
# for follow at MRI
avg2<- dat.m %>%
group_by(MRI_Cs2, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ","Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_ICH_L","TH_med_ICH_L","TH_lat_ICH_L","TH_post_ICH_L",
"TH_ant_ICH_R","TH_med_ICH_R","TH_lat_ICH_R","TH_post_ICH_R",
"TH_ant_edema_L","TH_med_edema_L","TH_lat_edema_L","TH_post_edema_L",
"TH_ant_edema_R","TH_med_edema_R","TH_lat_edema_R","TH_post_edema_R") )) %>%
summarize(mean=mean(value,na.rm=TRUE))
avg2$MRI_Cs2<-as.integer(avg2$MRI_Cs2);avg2$MRI_Cs2<-as.factor(avg2$MRI_Cs2)
ggplot(data=na.omit(rbind(avg2[grep(pattern = "ICH", avg2$variable),],avg2[avg2$variable=="IVH",])), aes(x=variable, y=mean, fill=MRI_Cs2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
labs(title = "7: ICH ~ Follow @ mri")
ggplot(data=na.omit(avg2[grep(pattern = "edema", avg2$variable), ]), aes(x=variable, y=mean, fill=MRI_Cs2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "8: Edema ~ Follow @ mri")
# for follow at discharge
avg1<- dat.m %>%
group_by(follow2, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_ICH_L","TH_med_ICH_L","TH_lat_ICH_L","TH_post_ICH_L",
"TH_ant_ICH_R","TH_med_ICH_R","TH_lat_ICH_R","TH_post_ICH_R",
"TH_ant_edema_L","TH_med_edema_L","TH_lat_edema_L","TH_post_edema_L",
"TH_ant_edema_R","TH_med_edema_R","TH_lat_edema_R","TH_post_edema_R") )) %>%
#summarize(mean=mean(value,na.rm=TRUE), sd=sd(value,na.rm=TRUE)) no need sd (%)
summarize(mean=mean(value,na.rm=TRUE))
avg1$follow2<-as.integer(avg1$follow2);avg1$follow2<-as.factor(avg1$follow2)
ggplot(data=rbind(avg1[grep(pattern = "ICH", avg1$variable),],avg1[avg1$variable=="IVH",]), aes(x=variable, y=mean, fill=follow2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
labs(title = "9: ICH ~ Follow @ discharge")
ggplot(data=avg1[grep(pattern = "edema", avg1$variable), ], aes(x=variable, y=mean, fill=follow2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "10: Edema ~ Follow @ discharge")
```
#### Ipsi / Controlateral analyse
In the following table, left 0/1 code corresponds to infra-tentorial ICH and R/L.. to supra-tentorial ICH
**Note:** We have one patients without infra or supra tent ICH or IVH (check w/ Alex)
```{r}
table(loc_analyse.raw2$infra_tent,loc_analyse.raw2$supra_tent)
sum(table(loc_analyse.raw2$infra_tent,loc_analyse.raw2$supra_tent))
# patients without any hemorrage ??
loc_analyse.raw2[loc_analyse.raw2$IVH==0 & loc_analyse.raw2$infra_tent==0 & loc_analyse.raw2$supra_tent=="None" ,]$MRN
```
```{r, echo=FALSE, warning=FALSE}
## analyse ipsi / contro
loc_analyse.raw3<-loc_analyse.raw[loc_analyse.raw$supra_tent2=='L' | loc_analyse.raw$infra_tent2=='L',]
# R->contro L->ipsi
# change by gsubbing instead
colnames(loc_analyse.raw3) <- gsub("_R$", "_contro",
gsub("_L$", "_ipsi", colnames(loc_analyse.raw3)) )
# R->contro L->ipsi
temp<-loc_analyse.raw[loc_analyse.raw$supra_tent2=='R' | loc_analyse.raw$infra_tent2=='R',]
# L->contro R->ipsi
colnames(temp) <- gsub("_L$", "_contro",
gsub("_R$", "_ipsi", colnames(temp)) )
# L->contro R->ipsi
loc_analyse.raw3<-rbind(temp,loc_analyse.raw3)
# save final data set for later statistical analysis in another script
openxlsx::write.xlsx(loc_analyse.raw3, file="E:/Experiments/ICH_MRI/MRI_Merged_Data_158patients.xlsx", rowNames=FALSE)
dat2.m <- melt(loc_analyse.raw3, id=c("MRN", "Discharge_date", "DEATH_DATE", "MRI_date_Loc", "mri_date", "follow2","MRI_Cs2", "follow3","MRI_Cs3"))
dat2.m$value<-as.numeric(dat2.m$value)
# for follow at MRI
avg2<- dat2.m %>%
group_by(MRI_Cs2, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_edema_contro", "TH_ant_edema_ipsi", "TH_lat_edema_contro",
"TH_lat_edema_ipsi", "TH_med_edema_contro", "TH_med_edema_ipsi",
"TH_post_edema_contro", "TH_post_edema_ipsi", "TH_ant_ICH_contro",
"TH_ant_ICH_ipsi", "TH_lat_ICH_contro", "TH_lat_ICH_ipsi",
"TH_med_ICH_contro", "TH_med_ICH_ipsi", "TH_post_ICH_contro",
"TH_post_ICH_ipsi") )) %>%
summarize(mean=mean(value,na.rm=TRUE))
avg2$MRI_Cs2<-as.integer(avg2$MRI_Cs2);avg2$MRI_Cs2<-as.factor(avg2$MRI_Cs2)
ggplot(data=na.omit(rbind(avg2[grep(pattern = "ICH", avg2$variable), ],avg2[avg2$variable=="IVH",])), aes(x=variable, y=mean, fill=MRI_Cs2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
labs(title = "11: ICH ~ Follow @ mri")
ggplot(data=na.omit(avg2[grep(pattern = "edema", avg2$variable), ]), aes(x=variable, y=mean, fill=MRI_Cs2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "12: Edema ~ Follow @ mri")
# for follow at discharge
avg1<- dat2.m %>%
group_by(follow2, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_edema_contro", "TH_ant_edema_ipsi", "TH_lat_edema_contro",
"TH_lat_edema_ipsi", "TH_med_edema_contro", "TH_med_edema_ipsi",
"TH_post_edema_contro", "TH_post_edema_ipsi", "TH_ant_ICH_contro",
"TH_ant_ICH_ipsi", "TH_lat_ICH_contro", "TH_lat_ICH_ipsi",
"TH_med_ICH_contro", "TH_med_ICH_ipsi", "TH_post_ICH_contro",
"TH_post_ICH_ipsi") )) %>%
#summarize(mean=mean(value,na.rm=TRUE), sd=sd(value,na.rm=TRUE)) no need sd (%)
summarize(mean=mean(value,na.rm=TRUE))
avg1$follow2<-as.integer(avg1$follow2);avg1$follow2<-as.factor(avg1$follow2)
ggplot(data=rbind(avg1[grep(pattern = "ICH", avg1$variable), ],avg1[avg1$variable=="IVH",]), aes(x=variable, y=mean, fill=follow2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "13: ICH ~ Follow @ discharge")
ggplot(data=avg1[grep(pattern = "edema", avg1$variable), ], aes(x=variable, y=mean, fill=follow2) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "14: Edema ~ Follow @ discharge")
```
```{r percentage of unconscious patients in each location, echo=FALSE, warning=FALSE}
library(RColorBrewer)
avg3<- dat2.m %>%
group_by(MRI_Cs2, variable, value) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_edema_contro", "TH_ant_edema_ipsi", "TH_lat_edema_contro",
"TH_lat_edema_ipsi", "TH_med_edema_contro", "TH_med_edema_ipsi",
"TH_post_edema_contro", "TH_post_edema_ipsi", "TH_ant_ICH_contro",
"TH_ant_ICH_ipsi", "TH_lat_ICH_contro", "TH_lat_ICH_ipsi",
"TH_med_ICH_contro", "TH_med_ICH_ipsi", "TH_post_ICH_contro",
"TH_post_ICH_ipsi", "Hg_vol") )) %>%
summarise(count=n()) %>%
group_by(variable, value) %>%
#group_by(MRI_Cs2, variable) %>%
mutate(group_tot=sum(count)) %>%
mutate(percentage=count / group_tot) %>%
#complete(MRI_Cs2, value)
#filter(value==1)
filter(value==1 & MRI_Cs2==0)
#summarize(mean=mean(value,na.rm=TRUE))
# turn everything to numeric then take the column sums
sumData <- loc_analyse.raw3[,grep("ICH", names(loc_analyse.raw3))]
sumData[,1:ncol(sumData)] <- lapply( lapply(sumData[,1:ncol(sumData)], as.character), as.numeric)
coltotals <- colSums(sumData)
coltotalsTable <- data.frame(total=coltotals)
# only include regions that have 4 or more patients with legions there
avg3_2 <- filter(avg3, group_tot >= 4)
# ggplot( data=na.omit(rbind(avg1[grep(pattern = "ICH", avg1$variable), ],avg1[avg1$variable=="IVH",]))) +
# geom_tile(aes(x=variable, y=follow3, fill=mean))+
# scale_fill_gradientn(colours=rev(brewer.pal(10,"Spectral")), limits=c(0,1))+
# coord_flip()+
# ggtitle("Plot for Sketch 21_2: ICH ~ Follow @ discharge")
## Percent patients
ggplot( data=rbind(avg3_2[grep(pattern = "ICH", avg3_2$variable), ],avg3_2[avg3_2$variable=="IVH",])) +
geom_tile(aes(x=variable, y=MRI_Cs2, fill=percentage))+
scale_fill_gradientn(colours=rev(brewer.pal(10,"Spectral")), limits=c(0,1))+
coord_flip()+
ggtitle("Percentage of unconscious patient for each location at MRI \n (only locs w/ > 3 obs)")
## Number of patients for each ROIs
# ggplot( data=rbind(avg3[grep(pattern = "ICH", avg3$variable), ],avg3[avg3$variable=="IVH",])) +
# geom_tile(aes(x=variable, y=MRI_Cs2, fill=group_tot))+
# #scale_colour_grey() +
# scale_fill_gradientn(colours=brewer.pal(10,"Greys"), limits=c(0,59))+
# coord_flip()+
# ggtitle("Total Number of patient for each location at MRI")
# TODO need to be fixed, unable to plot a simple grey scale of the of observation / ROIs
# ggplot( data=coltotalsTable) +
# geom_tile( aes(x=rownames(coltotalsTable), y=0, fill=total))+
# #scale_colour_grey() +
# scale_fill_gradientn(colours=brewer.pal(9,"Greys"), limits=c(0,60))+
# coord_flip()+
# ggtitle("Total Number of patient for each location at MRI")
#
# ggplot(coltotalsTable, aes(x=rownames(coltotalsTable), y=0, color=total)) + geom_point(size=10) +
# scale_color_gradient2(low="white", high="black", midpoint=0)+
# coord_flip()+
# theme_bw()
#
#
#
# ggplot( data=coltotalsTable) +
# geom_tile( aes(x=rownames(coltotalsTable), y=0, fill=total))+
# #scale_colour_grey() +
# scale_fill_gradientn(colours=brewer.pal(9,"Greys"), limits=c(0,100))+
# coord_flip()+
# ggtitle("Total Number of patient for each location at MRI")
#
# ggplot( data=coltotalsTable) +
# geom_tile( aes(x=rownames(coltotalsTable), y=0, fill=total))+
# #scale_colour_grey() +
# scale_fill_gradientn(colours=brewer.pal(9,"Greys"),breaks=c(0,35,70),labels=c("0","35","70"))+
# coord_flip()+
# ggtitle("Total Number of patient for each location at MRI")
#display.brewer.all()
```
### Consiousness split in 3 categories
#### according to Right / Left
```{r, echo=FALSE}
# for follow at MRI
avg2<- dat.m %>%
group_by(MRI_Cs3, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_edema_contro", "TH_ant_edema_ipsi", "TH_lat_edema_contro",
"TH_lat_edema_ipsi", "TH_med_edema_contro", "TH_med_edema_ipsi",
"TH_post_edema_contro", "TH_post_edema_ipsi", "TH_ant_ICH_contro",
"TH_ant_ICH_ipsi", "TH_lat_ICH_contro", "TH_lat_ICH_ipsi",
"TH_med_ICH_contro", "TH_med_ICH_ipsi", "TH_post_ICH_contro",
"TH_post_ICH_ipsi") )) %>%
summarize(mean=mean(value,na.rm=TRUE))
avg2$MRI_Cs3<-as.integer(avg2$MRI_Cs3);avg2$MRI_Cs3<-as.factor(avg2$MRI_Cs3)
ggplot(data=na.omit(rbind(avg2[grep(pattern = "ICH", avg2$variable), ],avg2[avg2$variable=="IVH",])), aes(x=variable, y=mean, fill=MRI_Cs3) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
labs(title = "15: ICH ~ Follow @ mri")
ggplot(data=na.omit(avg2[grep(pattern = "edema", avg2$variable), ]), aes(x=variable, y=mean, fill=MRI_Cs3) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "16: Edema ~ Follow @ mri")
# for follow at discharge
avg1<- dat.m %>%
group_by(follow3, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",
"Uncal herniation (to which side)","Transtentorial herniation","sf_hern", "Hypo_edema_C","Hypo_ICH_C","TH_ant_edema_contro", "TH_ant_edema_ipsi", "TH_lat_edema_contro",
"TH_lat_edema_ipsi", "TH_med_edema_contro", "TH_med_edema_ipsi",
"TH_post_edema_contro", "TH_post_edema_ipsi", "TH_ant_ICH_contro",
"TH_ant_ICH_ipsi", "TH_lat_ICH_contro", "TH_lat_ICH_ipsi",
"TH_med_ICH_contro", "TH_med_ICH_ipsi", "TH_post_ICH_contro",
"TH_post_ICH_ipsi") )) %>%
#summarize(mean=mean(value,na.rm=TRUE), sd=sd(value,na.rm=TRUE)) no need sd (%)
summarize(mean=mean(value,na.rm=TRUE))
avg1$follow3<-as.integer(avg1$follow3);avg1$follow3<-as.factor(avg1$follow3)
ggplot(data=rbind(avg1[grep(pattern = "ICH", avg1$variable), ],avg1[avg1$variable=="IVH",]), aes(x=variable, y=mean, fill=follow3) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip() +
labs(title = "17: ICH ~ Follow @ discharge")
ggplot(data=avg1[grep(pattern = "edema", avg1$variable), ], aes(x=variable, y=mean, fill=follow3) )+
geom_bar(stat="identity", position=position_dodge()) +
coord_flip()+
labs(title = "18: Edema ~ Follow @ discharge")
```
#### according to Ipsi / Controlateral
```{r, echo=FALSE, warning=FALSE}
## analyse ipsi / contro
library(cumplyr)
library(scales)
library(RColorBrewer)
# for follow at MRI
avg2<- dat2.m %>%
group_by(MRI_Cs3, variable) %>%
filter( !(variable %in% c("Cerebellar tonsillar herniation","MLS [mm]", "Old stroke", "Old ICH", "Uncal herniation (to which side) ","Transtentorial herniation ",