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rm(list=ls())
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(data.table)
library(dplyr)
library(reshape2)
library(ggplot2)
library(tidyr) #spread
library(RColorBrewer)
library(circlize)
library(ComplexHeatmap)
OF.data <- readRDS("data/OF.data.rds")
dim(OF.data)
[1] 344844 10
The heatmap below presents a visualization of the phenotypic measurements taken for each control mouse. The columns represent individual mice, while the rows correspond to the distinct phenotypes measured.
mtest <- table(OF.data$proc_param_name_stable_id, OF.data$biological_sample_id)
mtest <-as.data.frame.matrix(mtest)
dim(mtest)
[1] 51 24604
if(FALSE){
nmax <-max(mtest)
library(circlize)
col_fun = colorRamp2(c(0, nmax), c("white", "red"))
col_fun(seq(0, nmax))
ht = Heatmap(as.matrix(mtest), cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, col = col_fun,
row_names_gp = gpar(fontsize = 8), name="Count")
draw(ht)
}
To maintain data quality and robustness, we will discard any phenotypes that have fewer than 15,000 recorded observations.
mtest <- table(OF.data$proc_param_name, OF.data$biological_sample_id)
dim(mtest)
[1] 16 24604
#head(mtest[,1:10])
mtest0 <- mtest>0
#head(mtest0[,1:10])
rowSums(mtest0)
OF_Center average speed OF_Center distance travelled
22856 23638
OF_Center permanence time OF_Center resting time
24599 17698
OF_Distance travelled - total OF_Latency to center entry
21805 17883
OF_Number of center entries OF_Number of rears - total
17892 12814
OF_Percentage center time OF_Periphery average speed
21730 22857
OF_Periphery distance travelled OF_Periphery permanence time
23639 24600
OF_Periphery resting time OF_Whole arena average speed
17699 24603
OF_Whole arena permanence OF_Whole arena resting time
23821 24593
rmv.pheno.list <- rownames(mtest)[rowSums(mtest0)<15000]
#rmv.pheno.list
dim(OF.data)
[1] 344844 10
OF.data <- OF.data %>% filter(!(proc_param_name %in% rmv.pheno.list))
dim(OF.data)
[1] 332030 10
# number of phenotypes left
length(unique(OF.data$proc_param_name))
[1] 15
mtest <- table(OF.data$proc_param_name, OF.data$biological_sample_id)
dim(mtest)
[1] 15 24604
head(mtest[,1:10])
21653 21713 21742 21745 21747 21751 21753 21756
OF_Center average speed 1 1 1 1 1 1 1 1
OF_Center distance travelled 1 1 1 1 1 1 1 1
OF_Center permanence time 1 1 1 1 1 1 1 1
OF_Center resting time 1 1 1 1 1 1 1 1
OF_Distance travelled - total 0 0 0 0 0 0 0 0
OF_Latency to center entry 1 1 1 1 1 1 1 1
21759 21800
OF_Center average speed 1 1
OF_Center distance travelled 1 1
OF_Center permanence time 1 1
OF_Center resting time 1 1
OF_Distance travelled - total 0 0
OF_Latency to center entry 1 1
mtest0 <- mtest>0
head(mtest0[,1:10])
21653 21713 21742 21745 21747 21751 21753 21756
OF_Center average speed TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
OF_Center distance travelled TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
OF_Center permanence time TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
OF_Center resting time TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
OF_Distance travelled - total FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
OF_Latency to center entry TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
21759 21800
OF_Center average speed TRUE TRUE
OF_Center distance travelled TRUE TRUE
OF_Center permanence time TRUE TRUE
OF_Center resting time TRUE TRUE
OF_Distance travelled - total FALSE FALSE
OF_Latency to center entry TRUE TRUE
summary(colSums(mtest0))
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 11.00 15.00 13.41 15.00 15.00
rmv.sample.list <- colnames(mtest)[colSums(mtest0)<10]
length(rmv.sample.list)
[1] 1747
dim(OF.data)
[1] 332030 10
OF.data <- OF.data %>% filter(!(biological_sample_id %in% rmv.sample.list))
dim(OF.data)
[1] 319816 10
# number of observations to use
length(unique(OF.data$biological_sample_id))
[1] 22857
if(FALSE){
mtest <- table(OF.data$proc_param_name, OF.data$biological_sample_id)
dim(mtest)
mtest <-as.data.frame.matrix(mtest)
nmax <-max(mtest)
library(circlize)
col_fun = colorRamp2(c(0, nmax), c("white", "red"))
col_fun(seq(0, nmax))
pdf("~/Google Drive Miami/Miami_IMPC/output/measured_phenotypes_controls_after_filtering_OF.pdf", width = 10, height = 3)
ht = Heatmap(as.matrix(mtest), cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, col = col_fun,
row_names_gp = gpar(fontsize = 7), name="Count")
draw(ht)
dev.off()
}
We restructure our data from a long format to a wide one for further processing and analysis.
OF.mat <- OF.data %>%
dplyr::select(biological_sample_id, proc_param_name, data_point, sex, phenotyping_center, strain_name) %>%
##consider weight or age in weeks
arrange(biological_sample_id) %>%
distinct(biological_sample_id, proc_param_name, .keep_all=TRUE) %>% ## remove duplicates, maybe mean() is better.
spread(proc_param_name, data_point) %>%
tibble::column_to_rownames(var="biological_sample_id")
head(OF.mat)
sex phenotyping_center strain_name
21653 female WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
21713 female WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
21742 male WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
21745 male WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
21747 male WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
21751 male WTSI C57BL/6Brd-Tyr<c-Brd> * C57BL/6N
OF_Center average speed OF_Center distance travelled
21653 51.5 4259
21713 40.1 3266
21742 51.0 710
21745 38.0 2580
21747 31.4 3022
21751 14.9 1723
OF_Center permanence time OF_Center resting time
21653 102 20
21713 87 6
21742 17 3
21745 100 33
21747 134 39
21751 240 130
OF_Distance travelled - total OF_Latency to center entry
21653 NA 5.0
21713 NA 6.1
21742 NA 24.0
21745 NA 8.3
21747 NA 6.6
21751 NA 18.7
OF_Number of center entries OF_Percentage center time
21653 193 NA
21713 221 NA
21742 73 NA
21745 165 NA
21747 210 NA
21751 75 NA
OF_Periphery average speed OF_Periphery distance travelled
21653 35.3 12923
21713 31.9 11625
21742 19.1 5769
21745 25.1 7910
21747 26.0 8208
21751 11.5 2150
OF_Periphery permanence time OF_Periphery resting time
21653 498 135
21713 513 152
21742 583 292
21745 500 191
21747 466 154
21751 360 184
OF_Whole arena average speed OF_Whole arena permanence
21653 38.3 600
21713 33.4 600
21742 20.5 600
21745 27.4 600
21747 27.2 600
21751 12.8 600
OF_Whole arena resting time
21653 155
21713 158
21742 295
21745 224
21747 193
21751 314
dim(OF.mat)
[1] 22857 18
summary(colSums(is.na(OF.mat[,-1:-3])))
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 1 1677 3882 5159
ggplot(melt(OF.mat), aes(x=value)) +
geom_histogram() +
facet_wrap(~variable, scales="free", ncol=5)+
theme(strip.text.x = element_text(size = 6))
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
In this step, we conduct a rank Z transformation on the phenotype data to ensure that the data is normally distributed
library(RNOmni)
OF.mat.rank <- OF.mat
dim(OF.mat.rank)
[1] 22857 18
OF.mat.rank <- OF.mat.rank[complete.cases(OF.mat.rank),]
dim(OF.mat.rank)
[1] 14863 18
dim(OF.mat)
[1] 22857 18
OF.mat <- OF.mat[complete.cases(OF.mat),]
dim(OF.mat)
[1] 14863 18
OF.mat.rank <- cbind(OF.mat.rank[,1:3], apply(OF.mat.rank[,-1:-3], 2, RankNorm))
ggplot(melt(OF.mat.rank), aes(x=value)) +
geom_histogram() +
facet_wrap(~variable, scales="free", ncol=5)+
theme(strip.text.x = element_text(size = 6))
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
In this step, we apply Principal Variance Component Analysis (PVCA) on the phenotype matrix data. PVCA is an approach that combines Principal Component Analysis (PCA) and Variance Component Analysis to quantify the proportion of total variance in the data attributed to each important covariate, in this case ‘sex’ and ‘phenotyping_center’.
First, we prepare our metadata which includes our chosen covariates. Any character variables in the metadata are then converted to factors. To avoid potential confounding, we check for associations between our covariates and drop ‘strain_name’ due to its strong association with ‘phenotyping_center’.
Next, we run PVCA on randomly chosen subsets of our phenotype data (for computational efficiency). Finally, we compute the average effect size across all random samples and visualize the results in a PVCA plot.
source("code/PVCA.R")
meta <- OF.mat.rank[,1:3] ## examining covariates sex, phenotyping_center, and strain_name
head(meta)
sex phenotyping_center strain_name
39638 female MRC Harwell C57BL/6NTac
39639 female HMGU C57BL/6NCrl
39640 female HMGU C57BL/6NTac
39641 male HMGU C57BL/6NCrl
39642 female MRC Harwell C57BL/6NTac
39643 female HMGU C57BL/6NCrl
dim(meta)
[1] 14863 3
summary(meta) # variables are currently characters
sex phenotyping_center strain_name
Length:14863 Length:14863 Length:14863
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
meta[sapply(meta, is.character)] <- lapply(meta[sapply(meta, is.character)], as.factor)
summary(meta) # now all variables are converted to factors
sex phenotyping_center strain_name
female:7428 MRC Harwell:4532 C57BL/6N :3655
male :7435 HMGU :3119 C57BL/6NCrl:3510
ICS :2417 C57BL/6NJcl: 459
RBRC :1323 C57BL/6NTac:7239
CCP-IMG :1141
TCP :1093
(Other) :1238
chisq.test(meta[,1],meta[,2])
Pearson's Chi-squared test
data: meta[, 1] and meta[, 2]
X-squared = 3.7637, df = 7, p-value = 0.8066
chisq.test(meta[,2],meta[,3])
Pearson's Chi-squared test
data: meta[, 2] and meta[, 3]
X-squared = 29526, df = 21, p-value < 2.2e-16
meta<-meta[,-3] # phenotyping_center and strain_name strongly associated which could cause confounding in the PVCA analysis, so we drop 'strain_name'.
G <- t(OF.mat.rank[,-1:-3]) # preparing the phenotype matrix data
set.seed(09302021)
# Perform PVCA for 10 random samples of size 1000 (more computationally efficient)
pvca.res <- matrix(nrow=10, ncol=3)
for (i in 1:10){
sample <- sample(1:ncol(G), 1000, replace=FALSE)
pvca.res[i,] <- PVCA(G[,sample], meta[sample,], threshold=0.6, inter=FALSE)
}
# Compute average effect size across the 10 random samples
pvca.means <- colMeans(pvca.res)
names(pvca.means) <- c(colnames(meta), "resid")
# Create PVCA plot
pvca.plot <- PlotPVCA(pvca.means, "PVCA of Phenotype Matrix Data")
pvca.plot
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
png(file="docs/figure/figures.Rmd/pvca_OF_1_v16.png", width=600, height=350)
pvca.plot
dev.off()
quartz_off_screen
2
We remove batch effects (the center effect) in the phenotype data set by using the ComBat method.
library(sva)
combat_komp = ComBat(dat=G, batch=meta$phenotyping_center, par.prior=TRUE, prior.plots=TRUE, mod=NULL)
Found 1 genes with uniform expression within a single batch (all zeros); these will not be adjusted for batch.
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
combat_komp[1:5,1:5]
39638 39639 39640 39641
OF_Center average speed 0.59446136 -0.1046728 0.12751142 0.2488662
OF_Center distance travelled 0.49851881 -0.3961273 0.26323301 -0.1860701
OF_Center permanence time 0.05536160 -0.5740468 0.28108074 -0.3840993
OF_Center resting time 0.09818595 -0.9419518 0.58940330 -0.4078182
OF_Distance travelled - total 0.03620577 -0.0705752 0.03808406 0.4042278
39642
OF_Center average speed -0.16244236
OF_Center distance travelled -0.98532415
OF_Center permanence time -0.81014937
OF_Center resting time 0.05024321
OF_Distance travelled - total -0.11281875
G[1:5,1:5] # for comparison, combat_komp is same form and same dimensions as G
39638 39639 39640 39641
OF_Center average speed 0.425513501 1.234197021 1.5167530 1.6644355
OF_Center distance travelled 0.129374222 0.997641495 1.5808081 1.1834251
OF_Center permanence time -0.515972599 0.009697299 0.8574740 0.1980120
OF_Center resting time 0.001686461 -1.202492878 0.3483423 -0.6615647
OF_Distance travelled - total -0.470073474 1.298879853 1.4120914 1.7935746
39642
OF_Center average speed -0.44088826
OF_Center distance travelled -1.46426536
OF_Center permanence time -1.37773801
OF_Center resting time -0.04360882
OF_Distance travelled - total -0.61963422
After using ComBat to account for batch effects, we perform a PVCA on the residuals. We expect to observe a significantly reduced effect from the phenotyping centers.
set.seed(09302021)
# Perform PVCA for 10 random samples (more computationally efficient)
pvca.res.nobatch <- matrix(nrow=10, ncol=3)
for (i in 1:10){
sample <- sample(1:ncol(combat_komp), 1000, replace=FALSE)
pvca.res.nobatch[i,] <- PVCA(combat_komp[,sample], meta[sample,], threshold=0.6, inter=FALSE)
}
# Compute average effect size across samples
pvca.means.nobatch <- colMeans(pvca.res.nobatch)
names(pvca.means.nobatch) <- c(colnames(meta), "resid")
# Generate PVCA plot
pvca.plot.nobatch <- PlotPVCA(pvca.means.nobatch, "PVCA of Phenotype Matrix Data with Reduced Batch Effect")
pvca.plot.nobatch
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
if(FALSE){
png(file="docs/figure/figures.Rmd/pvca_OF_2_v16.png", width=600, height=350)
pvca.plot.nobatch
dev.off()
}
We compute the phenotype correlations using different methods and compare them.
OF.cor.rank <- cor(OF.mat.rank[,-1:-3], use="pairwise.complete.obs") # pearson correlation coefficient
OF.cor <- cor(OF.mat[,-1:-3], use="pairwise.complete.obs", method="spearman") # spearman
OF.cor.combat <- cor(t(combat_komp), use="pairwise.complete.obs")
pheno.list <- rownames(OF.cor)
ht1 = Heatmap(OF.cor, show_column_names = F, row_names_gp = gpar(fontsize = 9), name="Spearm. Corr.")
draw(ht1)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
ht2 = Heatmap(OF.cor.rank, show_column_names = F, row_names_gp = gpar(fontsize = 9), name="Corr. RankZ")
draw(ht2)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
ht3 = Heatmap(OF.cor.combat, show_column_names = F, row_names_gp = gpar(fontsize = 9), name="Corr. ComBat")
draw(ht3)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
OF.stat <- readRDS("data/OF.stat.v16.rds")
dim(OF.stat)
[1] 61854 8
table(OF.stat$parameter_name, OF.stat$procedure_name)
OF
Center average speed 3896
Center distance travelled 4176
Center permanence time 4275
Center resting time 3570
Distance travelled - total 4201
Latency to center entry 2989
Number of center entries 3614
Number of rears - total 2553
Percentage center movement time 3600
Percentage center time 4275
Periphery average speed 4176
Periphery distance travelled 4176
Periphery permanence time 4275
Periphery resting time 3529
Whole arena average speed 4275
Whole arena resting time 4274
length(unique(OF.stat$marker_symbol)) #3362
[1] 3823
length(unique(OF.stat$allele_symbol)) #3412
[1] 3868
length(unique(OF.stat$proc_param_name)) #15, number of phenotypes in association statistics data set
[1] 16
length(unique(OF.data$proc_param_name)) #15, number of phenotypes in final control data
[1] 15
pheno.list.stat <- unique(OF.stat$proc_param_name)
pheno.list.ctrl <- unique(OF.data$proc_param_name)
sum(pheno.list.stat %in% pheno.list.ctrl)
[1] 14
sum(pheno.list.ctrl %in% pheno.list.stat)
[1] 14
# Identifying common phenotypes between statistics and control data
common.pheno.list <- sort(intersect(pheno.list.ctrl, pheno.list.stat))
common.pheno.list
[1] "OF_Center average speed" "OF_Center distance travelled"
[3] "OF_Center permanence time" "OF_Center resting time"
[5] "OF_Distance travelled - total" "OF_Latency to center entry"
[7] "OF_Number of center entries" "OF_Percentage center time"
[9] "OF_Periphery average speed" "OF_Periphery distance travelled"
[11] "OF_Periphery permanence time" "OF_Periphery resting time"
[13] "OF_Whole arena average speed" "OF_Whole arena resting time"
length(common.pheno.list) # 14 - each data set had one phenotype not present in the other
[1] 14
# Filtering summary statistics to contain only common phenotypes
dim(OF.stat)
[1] 61854 8
OF.stat <- OF.stat %>% filter(proc_param_name %in% common.pheno.list)
dim(OF.stat)
[1] 55701 8
length(unique(OF.stat$proc_param_name))
[1] 14
mtest <- table(OF.stat$proc_param_name, OF.stat$marker_symbol)
mtest <-as.data.frame.matrix(mtest)
nmax <-max(mtest)
col_fun = colorRamp2(c(0, nmax), c("white", "red"))
col_fun(seq(0, nmax))
[1] "#FFFFFFFF" "#FFEAE2FF" "#FFD5C6FF" "#FFBFAAFF" "#FFA98FFF" "#FF9374FF"
[7] "#FF7B5AFF" "#FF6140FF" "#FF4124FF" "#FF0000FF"
ht = Heatmap(as.matrix(mtest), cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, col = col_fun,
row_names_gp = gpar(fontsize = 8), name="Count")
draw(ht)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
## sum(z-score)/sqrt(# of zscore)
sumz <- function(z){ sum(z)/sqrt(length(z)) }
OF.z = OF.stat %>%
dplyr::select(marker_symbol, proc_param_name, z_score) %>%
na.omit() %>%
group_by(marker_symbol, proc_param_name) %>%
summarize(zscore = sumz(z_score)) ## combine z-scores
dim(OF.z)
[1] 49921 3
# Function to convert NaN to NA
nan2na <- function(df){
out <- data.frame(sapply(df, function(x) ifelse(is.nan(x), NA, x)))
colnames(out) <- colnames(df)
out
}
# Converting the long format of z-scores to a wide format matrix
OF.zmat = dcast(OF.z, marker_symbol ~ proc_param_name, value.var = "zscore",
fun.aggregate = mean) %>% tibble::column_to_rownames(var="marker_symbol")
OF.zmat = nan2na(OF.zmat) #convert nan to na
dim(OF.zmat)
[1] 3823 14
dim(OF.zmat)
[1] 3823 14
saveRDS(OF.zmat, file = "data/OF.zmat.v16.rds")
The heatmap illustrates tested (red) and untested (white) gene-phenotype pairs.
# Generate a matrix indicating where z-scores are present
id.mat <- 1*(!is.na(OF.zmat)) # multiply 1 to make this matrix numeric
nrow(as.data.frame(colSums(id.mat)))
[1] 14
dim(id.mat)
[1] 3823 14
ht = Heatmap(t(id.mat),
cluster_rows = T, clustering_distance_rows ="binary",
cluster_columns = T, clustering_distance_columns = "binary",
show_row_dend = F, show_column_dend = F, # do not show dendrogram
show_column_names = F, col = c("white","red"),
row_names_gp = gpar(fontsize = 10), name="Missing")
draw(ht)
The histogram presents the distribution of association Z-scores for each phenotype.
ggplot(melt(OF.zmat), aes(x=value)) +
geom_histogram() +
facet_wrap(~variable, scales="free", ncol=5)+
theme(strip.text.x = element_text(size = 6))
Here, we estimate the genetic correlations between phenotypes utilizing the association Z-score matrix.
# Select common phenotypes
OF.zmat <- OF.zmat[,common.pheno.list]
dim(OF.zmat)
[1] 3823 14
# Compute genetic correlations
OF.zcor = cor(OF.zmat, use="pairwise.complete.obs")
# Generate heatmap of the correlation matrix
ht = Heatmap(OF.zcor, cluster_rows = T, cluster_columns = T, show_column_names = F, #col = col_fun,
row_names_gp = gpar(fontsize = 10),
name="Genetic Corr (Z-score)"
)
draw(ht)
We will compare the correlation matrix obtained from control mice phenotype data and the genetic correlation matrix estimated using association Z-scores. As depicted below, both correlation heatmaps show similar correlation patterns.
OF.cor.rank.fig <- OF.cor.rank[common.pheno.list,common.pheno.list]
OF.cor.fig <- OF.cor[common.pheno.list,common.pheno.list]
OF.cor.combat.fig <- OF.cor.combat[common.pheno.list, common.pheno.list]
OF.zcor.fig <- OF.zcor
ht = Heatmap(OF.cor.rank.fig, cluster_rows = TRUE, cluster_columns = TRUE, show_column_names = F, #col = col_fun,
show_row_dend = F, show_column_dend = F, # do not show dendrogram
row_names_gp = gpar(fontsize = 8), column_title="Phenotype Corr (RankZ, Pearson)", column_title_gp = gpar(fontsize = 8),
name="Corr")
pheno.order <- row_order(ht)
#draw(ht)
OF.cor.rank.fig <- OF.cor.rank.fig[pheno.order,pheno.order]
ht1 = Heatmap(OF.cor.rank.fig, cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, #col = col_fun,
show_row_dend = F, show_column_dend = F, # do not show dendrogram
row_names_gp = gpar(fontsize = 8), column_title="Phenotype Corr (RankZ, Pearson)", column_title_gp = gpar(fontsize = 8),
name="Corr")
OF.cor.fig <- OF.cor.fig[pheno.order,pheno.order]
ht2 = Heatmap(OF.cor.fig, cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, #col = col_fun,
row_names_gp = gpar(fontsize = 8), column_title="Phenotype Corr (Spearman)", column_title_gp = gpar(fontsize = 8),
name="Corr")
OF.cor.combat.fig <- OF.cor.combat.fig[pheno.order,pheno.order]
ht3 = Heatmap(OF.cor.combat.fig, cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, #col = col_fun,
row_names_gp = gpar(fontsize = 8), column_title="Phenotype Corr (Combat, Pearson)", column_title_gp = gpar(fontsize = 8),
name="Corr")
OF.zcor.fig <- OF.zcor.fig[pheno.order,pheno.order]
ht4 = Heatmap(OF.zcor.fig, cluster_rows = FALSE, cluster_columns = FALSE, show_column_names = F, #col = col_fun,
row_names_gp = gpar(fontsize = 8), column_title="Genetic Corr (Pearson)", column_title_gp = gpar(fontsize = 8),
name="Corr"
)
draw(ht1+ht2+ht3+ht4)
if(FALSE){
png(file="docs/figure/figures.Rmd/cors_OF.png", width=800, height=250)
draw(ht1+ht2+ht3+ht4)
dev.off()
}
To evaluate the correlation between different genetic correlation matrices, we apply Mantel’s test, which measures the correlation between two distance matrices.
####################
# Use Mantel test
# https://stats.idre.ucla.edu/r/faq/how-can-i-perform-a-mantel-test-in-r/
# install.packages("ade4")
library(ade4)
# Function to extract upper triangular elements of a matrix
to.upper<-function(X) X[upper.tri(X,diag=FALSE)]
a1 <- to.upper(OF.cor.fig)
a2 <- to.upper(OF.cor.rank.fig)
a3 <- to.upper(OF.cor.combat.fig)
a4 <- to.upper(OF.zcor.fig)
plot(a4, a1)
Version | Author | Date |
---|---|---|
9b680ec | statsleelab | 2023-06-20 |
plot(a4, a2)
Version | Author | Date |
---|---|---|
9b680ec | statsleelab | 2023-06-20 |
plot(a4, a3)
Version | Author | Date |
---|---|---|
9b680ec | statsleelab | 2023-06-20 |
mantel.rtest(as.dist(1-OF.cor.fig), as.dist(1-OF.zcor.fig), nrepet = 9999) #nrepet = number of permutations
Monte-Carlo test
Call: mantelnoneuclid(m1 = m1, m2 = m2, nrepet = nrepet)
Observation: 0.9481883
Based on 9999 replicates
Simulated p-value: 1e-04
Alternative hypothesis: greater
Std.Obs Expectation Variance
8.2821799635 -0.0003203414 0.0131157538
mantel.rtest(as.dist(1-OF.cor.rank.fig), as.dist(1-OF.zcor.fig), nrepet = 9999)
Monte-Carlo test
Call: mantelnoneuclid(m1 = m1, m2 = m2, nrepet = nrepet)
Observation: 0.9544471
Based on 9999 replicates
Simulated p-value: 1e-04
Alternative hypothesis: greater
Std.Obs Expectation Variance
8.2504397969 0.0008944113 0.0133578074
mantel.rtest(as.dist(1-OF.cor.combat.fig), as.dist(1-OF.zcor.fig), nrepet = 9999)
Monte-Carlo test
Call: mantelnoneuclid(m1 = m1, m2 = m2, nrepet = nrepet)
Observation: 0.9759421
Based on 9999 replicates
Simulated p-value: 1e-04
Alternative hypothesis: greater
Std.Obs Expectation Variance
8.920438043 -0.001681222 0.012010766
# Check if KOMPUTE is installed, if not, install it from GitHub using devtools
if(!"kompute" %in% rownames(installed.packages())){
library(devtools)
devtools::install_github("dleelab/kompute")
}
library(kompute)
In this section, we conduct a simulation study to compare the performance of the KOMPUTE method with the measured gene-phenotype association z-scores. We randomly select some of these measured z-scores, mask them, and then use the KOMPUTE method to impute them. We then compare the imputed z-scores with the measured ones.
zmat <-t(OF.zmat)
dim(zmat)
[1] 14 3823
# filter genes with less than 1 missing data point (na)
zmat0 <- is.na(zmat)
num.na<-colSums(zmat0)
summary(num.na)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.9419 1.0000 8.0000
dim(zmat)
[1] 14 3823
dim(zmat[,num.na<1])
[1] 14 2576
dim(zmat[,num.na<5])
[1] 14 3510
dim(zmat[,num.na<10])
[1] 14 3823
# filter genes with less than 1 missing data point (na)
zmat <- zmat[,num.na<1]
dim(zmat)
[1] 14 2576
# Set correlation method for phenotypes
#pheno.cor <- OF.cor.fig
#pheno.cor <- OF.cor.rank.fig
pheno.cor <- OF.cor.combat.fig
#pheno.cor <- OF.zcor.fig
#save phenotype correlation matrix
saveRDS(pheno.cor, file = "data/OF.pheno.cor.v16.rds")
zmat <- zmat[rownames(pheno.cor),,drop=FALSE]
rownames(zmat)
[1] "OF_Center distance travelled" "OF_Number of center entries"
[3] "OF_Whole arena average speed" "OF_Periphery average speed"
[5] "OF_Distance travelled - total" "OF_Periphery distance travelled"
[7] "OF_Center average speed" "OF_Percentage center time"
[9] "OF_Center permanence time" "OF_Center resting time"
[11] "OF_Latency to center entry" "OF_Periphery permanence time"
[13] "OF_Whole arena resting time" "OF_Periphery resting time"
rownames(pheno.cor)
[1] "OF_Center distance travelled" "OF_Number of center entries"
[3] "OF_Whole arena average speed" "OF_Periphery average speed"
[5] "OF_Distance travelled - total" "OF_Periphery distance travelled"
[7] "OF_Center average speed" "OF_Percentage center time"
[9] "OF_Center permanence time" "OF_Center resting time"
[11] "OF_Latency to center entry" "OF_Periphery permanence time"
[13] "OF_Whole arena resting time" "OF_Periphery resting time"
colnames(pheno.cor)
[1] "OF_Center distance travelled" "OF_Number of center entries"
[3] "OF_Whole arena average speed" "OF_Periphery average speed"
[5] "OF_Distance travelled - total" "OF_Periphery distance travelled"
[7] "OF_Center average speed" "OF_Percentage center time"
[9] "OF_Center permanence time" "OF_Center resting time"
[11] "OF_Latency to center entry" "OF_Periphery permanence time"
[13] "OF_Whole arena resting time" "OF_Periphery resting time"
npheno <- nrow(zmat)
# calculate the percentage of missing Z-scores in the original data
100*sum(is.na(zmat))/(nrow(zmat)*ncol(zmat)) # 0%
[1] 0
nimp <- 1000 # # of missing/imputed Z-scores
set.seed(1111)
# find index of all measured zscores
all.i <- 1:(nrow(zmat)*ncol(zmat))
measured <- as.vector(!is.na(as.matrix(zmat)))
measured.i <- all.i[measured]
# mask 2000 measured z-scores
mask.i <- sort(sample(measured.i, nimp))
org.z = as.matrix(zmat)[mask.i]
zvec <- as.vector(as.matrix(zmat))
zvec[mask.i] <- NA
zmat.imp <- matrix(zvec, nrow=npheno)
rownames(zmat.imp) <- rownames(zmat)
kompute.res <- kompute(zmat.imp, pheno.cor, 0.01)
# Compare measured vs imputed z-scores
length(org.z)
[1] 1000
imp.z <- as.matrix(kompute.res$zmat)[mask.i]
imp.info <- as.matrix(kompute.res$infomat)[mask.i]
plot(imp.z, org.z)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
# Create a dataframe with the original and imputed z-scores and the information of imputed z-scores
imp <- data.frame(org.z=org.z, imp.z=imp.z, info=imp.info)
dim(imp)
[1] 1000 3
imp <- imp[complete.cases(imp),]
imp <- subset(imp, info>=0 & info <= 1)
dim(imp)
[1] 1000 3
cor.val <- round(cor(imp$imp.z, imp$org.z), digits=3)
cor.val
[1] 0.929
plot(imp$imp.z, imp$org.z)
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
# Set a cutoff for information content and filter the data accordingly
info.cutoff <- 0.8
imp.sub <- subset(imp, info>info.cutoff)
dim(imp.sub)
[1] 846 3
summary(imp.sub$imp.z)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-6.0260 -1.1783 -0.2011 -0.1391 0.7985 16.7432
summary(imp.sub$info)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.8051 0.8547 0.9209 0.9119 0.9653 0.9878
cor.val <- round(cor(imp.sub$imp.z, imp.sub$org.z), digits=3)
cor.val
[1] 0.961
g <- ggplot(imp.sub, aes(x=imp.z, y=org.z, col=info)) +
geom_point() +
labs(title=paste0("IMPC Behavior Data (OF), Info>", info.cutoff, ", Cor=",cor.val),
x="Imputed Z-scores", y = "Measured Z-scores", col="Info") +
theme_minimal()
g
Version | Author | Date |
---|---|---|
7685a09 | statsleelab | 2023-01-10 |
# save plot
png(file="docs/figure/figures.Rmd/sim_results_OF_v16.png", width=600, height=350)
g
dev.off()
quartz_off_screen
2
# Part 3 of Figure 2
fig2.3 <- ggplot(imp.sub, aes(x=imp.z, y=org.z, col=info)) +
geom_point() +
labs(title="Open Field",
x="Imputed Z-scores", y = "", col="Info") +
scale_x_continuous(limits=c(-9,9), breaks=c(seq(-9,9,3)), minor_breaks = NULL) +
scale_y_continuous(limits=c(-9,9), breaks=c(seq(-9,9,3))) +
scale_color_gradient(limits=c(0.8,1), low="#98cdf9", high="#084b82") +
theme_bw() +
#theme(legend.position="none", plot.title=element_text(hjust=0.5))
theme(plot.title=element_text(hjust=0.5))
save(fig2.3, file="docs/figure/figures.Rmd/sim_OF_v16.rdata")
sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] kompute_0.1.0 ade4_1.7-20 sva_3.44.0
[4] BiocParallel_1.30.3 genefilter_1.78.0 mgcv_1.8-40
[7] nlme_3.1-158 lme4_1.1-31 Matrix_1.5-1
[10] RNOmni_1.0.1 ComplexHeatmap_2.12.1 circlize_0.4.15
[13] RColorBrewer_1.1-3 tidyr_1.2.0 ggplot2_3.4.1
[16] reshape2_1.4.4 dplyr_1.0.9 data.table_1.14.2
[19] workflowr_1.7.0.1
loaded via a namespace (and not attached):
[1] minqa_1.2.5 colorspace_2.1-0 rjson_0.2.21
[4] rprojroot_2.0.3 XVector_0.36.0 GlobalOptions_0.1.2
[7] fs_1.5.2 clue_0.3-62 rstudioapi_0.13
[10] farver_2.1.1 bit64_4.0.5 AnnotationDbi_1.58.0
[13] fansi_1.0.4 codetools_0.2-18 splines_4.2.1
[16] doParallel_1.0.17 cachem_1.0.6 knitr_1.39
[19] jsonlite_1.8.0 nloptr_2.0.3 annotate_1.74.0
[22] cluster_2.1.3 png_0.1-8 compiler_4.2.1
[25] httr_1.4.3 assertthat_0.2.1 fastmap_1.1.0
[28] limma_3.52.4 cli_3.6.0 later_1.3.0
[31] htmltools_0.5.3 tools_4.2.1 gtable_0.3.1
[34] glue_1.6.2 GenomeInfoDbData_1.2.8 Rcpp_1.0.10
[37] Biobase_2.56.0 jquerylib_0.1.4 vctrs_0.5.2
[40] Biostrings_2.64.0 iterators_1.0.14 xfun_0.31
[43] stringr_1.4.0 ps_1.7.1 lifecycle_1.0.3
[46] XML_3.99-0.10 edgeR_3.38.4 getPass_0.2-2
[49] MASS_7.3-58.1 zlibbioc_1.42.0 scales_1.2.1
[52] promises_1.2.0.1 parallel_4.2.1 yaml_2.3.5
[55] memoise_2.0.1 sass_0.4.2 stringi_1.7.8
[58] RSQLite_2.2.15 highr_0.9 S4Vectors_0.34.0
[61] foreach_1.5.2 BiocGenerics_0.42.0 boot_1.3-28
[64] shape_1.4.6 GenomeInfoDb_1.32.3 rlang_1.0.6
[67] pkgconfig_2.0.3 matrixStats_0.62.0 bitops_1.0-7
[70] evaluate_0.16 lattice_0.20-45 purrr_0.3.4
[73] labeling_0.4.2 bit_4.0.4 processx_3.7.0
[76] tidyselect_1.2.0 plyr_1.8.7 magrittr_2.0.3
[79] R6_2.5.1 IRanges_2.30.0 generics_0.1.3
[82] DBI_1.1.3 pillar_1.8.1 whisker_0.4
[85] withr_2.5.0 survival_3.3-1 KEGGREST_1.36.3
[88] RCurl_1.98-1.8 tibble_3.1.8 crayon_1.5.1
[91] utf8_1.2.3 rmarkdown_2.14 GetoptLong_1.0.5
[94] locfit_1.5-9.6 blob_1.2.3 callr_3.7.1
[97] git2r_0.30.1 digest_0.6.29 xtable_1.8-4
[100] httpuv_1.6.5 stats4_4.2.1 munsell_0.5.0
[103] bslib_0.4.0