Signal drift and batch effect correction and mass spectral quality assessment
2024-05-01
Package
pmp 1.16.0
1 Introduction
This is a step by step tutorial on how to assess and/or correct signal drift and batch effects within/across a multi-batch direct infusion mass spectrometry (DIMS) dataset. The same approach can be used on liquid chromatography mass spectrometry (LCMS) peak table as well.
Deeper details on how the algorithm works are detailed in 4.3
2 Installation
You should have R version 4.0.0 or above and Rstudio installed to be able to run this notebook.
Execute following commands from the R terminal.
install.packages("gridExtra")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("pmp")Load the required libraries into the R environment
library(S4Vectors)
library(SummarizedExperiment)
library(pmp)
library(ggplot2)
library(reshape2)
library(gridExtra)3 Dataset
In this tutorial we will be using a direct infusion mass spectrometry (DIMS)
dataset consisting of 172 samples measured across 8 batches and is included in
pmp package as SummarizedExperiemnt class object MTBLS79.
More detailed description of the dataset is available from Kirwan et al. (2014),
MTBLS79 and R man page.
help ("MTBLS79")feature_names <- c("70.03364", "133.07379", "146.16519", "163.04515",
"174.89483", "200.03196", "207.07818", "221.05062", "240.02445",
"251.03658", "266.01793", "304.99115", "321.07923", "338.98131",
"376.03962", "393.35878", "409.05716", "430.24353", "451.01086",
"465.14937")
summary(t(SummarizedExperiment::assay(MTBLS79[feature_names, ])))
#> 70.03364 133.07379 146.16519 163.04515
#> Min. : 2390 Min. :214680 Min. : 20142 Min. : 85138
#> 1st Qu.:15107 1st Qu.:326793 1st Qu.: 43129 1st Qu.:140682
#> Median :25992 Median :364517 Median : 57957 Median :153265
#> Mean :25715 Mean :355800 Mean : 73911 Mean :154682
#> 3rd Qu.:35395 3rd Qu.:389726 3rd Qu.: 85819 3rd Qu.:170635
#> Max. :56061 Max. :452978 Max. :351751 Max. :216917
#> NA's :18
#> 174.89483 200.03196 207.07818 221.05062
#> Min. : 6839 Min. :18338 Min. :179175 Min. : 52204
#> 1st Qu.:11551 1st Qu.:24673 1st Qu.:206967 1st Qu.: 86777
#> Median :14079 Median :28380 Median :220686 Median : 96552
#> Mean :16982 Mean :30027 Mean :225574 Mean : 96794
#> 3rd Qu.:21159 3rd Qu.:33026 3rd Qu.:234812 3rd Qu.:105478
#> Max. :43762 Max. :58147 Max. :431784 Max. :131368
#>
#> 240.02445 251.03658 266.01793 304.99115
#> Min. :12994 Min. : 4726 Min. : 5283 Min. : 24683
#> 1st Qu.:22179 1st Qu.: 20658 1st Qu.:14247 1st Qu.: 44669
#> Median :28939 Median : 30675 Median :30224 Median : 60338
#> Mean :29429 Mean : 41251 Mean :26680 Mean : 68689
#> 3rd Qu.:34527 3rd Qu.: 58220 3rd Qu.:36028 3rd Qu.: 80126
#> Max. :54417 Max. :164754 Max. :61402 Max. :190193
#> NA's :32 NA's :30
#> 321.07923 338.98131 376.03962 393.35878
#> Min. : 5368 Min. : 2817 Min. : 7082 Min. : 62973
#> 1st Qu.: 16107 1st Qu.: 6413 1st Qu.:17636 1st Qu.:123464
#> Median : 27822 Median : 8215 Median :22720 Median :166579
#> Mean : 38301 Mean : 9494 Mean :24130 Mean :235819
#> 3rd Qu.: 46408 3rd Qu.:11087 3rd Qu.:28685 3rd Qu.:334867
#> Max. :285756 Max. :30462 Max. :67877 Max. :830668
#> NA's :27 NA's :29
#> 409.05716 430.24353 451.01086 465.14937
#> Min. : 110970 Min. : 25415 Min. : 1884 Min. : 3167
#> 1st Qu.: 151404 1st Qu.: 34350 1st Qu.: 5254 1st Qu.: 19916
#> Median : 161617 Median : 38528 Median : 7210 Median : 25224
#> Mean : 183870 Mean : 43203 Mean : 7880 Mean : 27209
#> 3rd Qu.: 174052 3rd Qu.: 43726 3rd Qu.:10490 3rd Qu.: 29656
#> Max. :2828059 Max. :121995 Max. :17602 Max. :101471
#> NA's :1 NA's :9#number of samples:
ncol(MTBLS79)
#> [1] 172#Batches:
unique(MTBLS79$Batch)
#> [1] "1" "2" "3" "4" "5" "6" "7" "8"#Sample classes:
unique(MTBLS79$Class)
#> [1] "QC" "C" "S"4 Exploratory data analysis
A more detailed overview and guidelines on strategies for quality control of mass spectrometry assays is detailed in recent work by Broadhurst et al. (2018).
To evaluate if the data needs correction, it is common practice to examine the relative standard deviation (RSD) of the quality control (QC) samples and biological samples. RSD% is also sometimes referred to as the coefficient of variation (CV). An RSD% for the QC samples below 20-30% is commonly used as an acceptable level of technical variation where signal correction is not required.
The following code calculates and plots the RSD% values of the features within the dataset.
# separate the LCMS data from the meta data
data(MTBLS79)
data <- SummarizedExperiment::assay(MTBLS79[feature_names, ])
class <- SummarizedExperiment::colData(MTBLS79)$Class
batch <- SummarizedExperiment::colData(MTBLS79)$Batch
order <- c(1:ncol(data))
# get index of QC samples
QChits <- which(class == "QC")
# small function to calculate RSD%
FUN <- function(x) sd(x, na.rm=TRUE) / mean(x, na.rm=TRUE) * 100
# RSD% of biological and QC samples within all 8 batches:
out <- matrix(ncol=2, nrow=nrow(data))
colnames(out) <- c("Sample","QC")
rownames(out) <- rownames(data)
# for each feature calculate RSD% for the samples and the QCs
for (i in 1:nrow(data)) {
out[i, 1] <- FUN(data[i, -QChits]) # for samples
out[i, 2] <- FUN(data[i, QChits]) # for QCs
}
# prepare data for plotting
plotdata <- melt(data.frame(out), variable.name="Class", value.name="RSD")
plotdata$feature <- rownames(data)
plotdata$RSD <- round(plotdata$RSD,0)
plotdata$feature <- factor(plotdata$feature, ordered=TRUE,
levels=unique(plotdata$feature))
# plot
ggplot(data=plotdata, aes(x=Class, y=feature, fill=RSD)) +
geom_tile() +
geom_text(aes(label=RSD)) +
scale_fill_gradient2(low="black", mid="white", high="red")
A violin plot is a useful way of summarising the RSD% over all samples/QCs in the data set. Note a very high QC sample RSD% value for feature ‘409.05716’.
ggplot(data=plotdata, aes(x=Class, y=RSD, fill=Class)) +
geom_violin(draw_quantiles=c(0.25,0.5,0.75)) +
ylab("RSD%") +
guides(fill=FALSE) +
theme(panel.background=element_blank())
#> Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
#> of ggplot2 3.3.4.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
The plots indicates that most features have a QC RSD% lower than 30%, which is a commonly accepted threshold, but for some features the QC RSD% exceeds 30% and is more similar to the signal variation of the biological samples. We can calculate similar statistics per batch and visualise the results with a box plot.
# prepare some matrices to store the results
RSDQC <- matrix(ncol=8, nrow=nrow(data))
RSDsample <- matrix(ncol=8, nrow=nrow(data))
colnames(RSDQC) <- unique(batch)
colnames(RSDsample) <- unique(batch)
rownames(RSDQC) <- rownames(data)
rownames(RSDsample) <- rownames(data)
# for each feature
for (i in 1:nrow(data)) {
# for each batch
for (nb in 1:8) {
# RSD% of QCs in this batch
RSDQC[i, nb] <- FUN(data[i, which(class == "QC" & batch == nb)])
# RSD% of samples in this batch
RSDsample[i, nb] <- FUN(data[i, which(!class == "QC" & batch == nb)])
}
}
# prepare results for plotting
plotdataQC <- melt(as.data.frame(RSDQC), variable.name="batch",
value.name="RSD")
plotdataQC$Class <- "QC"
plotdataBio <- melt(as.data.frame(RSDsample), variable.name="batch",
value.name="RSD")
plotdataBio$Class <- "Sample"
plotdata <- rbind(plotdataQC, plotdataBio)
plotdata$Class <- as.factor(plotdata$Class)
# plot
ggplot(data=plotdata, aes(x=Class, y=RSD, fill=Class)) + geom_boxplot() +
facet_wrap(~ batch, ncol=3) +
ylab("RSD%") +
xlab("") +
scale_x_discrete(labels=NULL) +
theme(panel.background=element_blank(), axis.text.x=element_blank(),
axis.ticks.x=element_blank())
Summary of RSD% of QC samples
summary(RSDQC)
#> 1 2 3 4
#> Min. : 5.494 Min. : 1.544 Min. : 1.253 Min. : 2.242
#> 1st Qu.: 9.390 1st Qu.: 5.139 1st Qu.: 5.318 1st Qu.: 4.828
#> Median :11.940 Median : 6.895 Median : 7.585 Median : 7.729
#> Mean :13.850 Mean :11.106 Mean : 9.513 Mean :10.728
#> 3rd Qu.:14.824 3rd Qu.:12.203 3rd Qu.:12.187 3rd Qu.:13.141
#> Max. :33.582 Max. :51.256 Max. :23.322 Max. :31.683
#> 5 6 7 8
#> Min. : 2.254 Min. : 7.432 Min. : 2.553 Min. : 2.640
#> 1st Qu.: 6.775 1st Qu.:11.348 1st Qu.: 6.403 1st Qu.: 6.462
#> Median : 10.094 Median :13.542 Median : 9.571 Median : 8.557
#> Mean : 19.690 Mean :17.684 Mean : 9.285 Mean :10.252
#> 3rd Qu.: 16.091 3rd Qu.:16.199 3rd Qu.:12.397 3rd Qu.:13.754
#> Max. :170.303 Max. :79.814 Max. :20.479 Max. :20.977Summary of RSD% of biological samples
summary(RSDsample)
#> 1 2 3 4
#> Min. : 6.746 Min. : 6.604 Min. : 4.211 Min. : 5.679
#> 1st Qu.: 10.896 1st Qu.:17.282 1st Qu.: 12.397 1st Qu.:16.656
#> Median : 30.560 Median :36.431 Median : 32.848 Median :37.214
#> Mean : 38.338 Mean :37.854 Mean : 39.372 Mean :37.242
#> 3rd Qu.: 61.465 3rd Qu.:49.282 3rd Qu.: 57.670 3rd Qu.:50.813
#> Max. :117.884 Max. :98.010 Max. :112.353 Max. :74.824
#> 5 6 7 8
#> Min. : 6.412 Min. : 8.684 Min. : 9.647 Min. : 4.821
#> 1st Qu.:18.916 1st Qu.: 17.918 1st Qu.: 18.379 1st Qu.: 14.845
#> Median :34.609 Median : 36.553 Median : 31.890 Median : 33.948
#> Mean :33.461 Mean : 43.971 Mean : 37.071 Mean : 36.726
#> 3rd Qu.:43.464 3rd Qu.: 59.891 3rd Qu.: 49.187 3rd Qu.: 50.618
#> Max. :76.129 Max. :138.689 Max. :117.526 Max. :108.413From the above we can conclude that for every analytical batch RSD% tends to be higher in the analytical samples than it is in the QC samples for all 20 measured features. A few outlier QC samples can be observed.
An alternative measure of QC and biological sample variability is the so called D-ratio, which indicates if the technical variation within the QC samples exceeds the biological variation within biological samples.
# prepare a list of colours for plotting
manual_color = c("#386cb0", "#ef3b2c", "#7fc97f", "#fdb462", "#984ea3",
"#a6cee3", "#778899", "#fb9a99", "#ffff33")
# Function to calculate median absolute deviation
DRatfun <- function(samples, qcs) mad(qcs) / mad(samples)
# prepare matrix for dratio output
dratio <- matrix(ncol=8, nrow=nrow(data))
colnames(dratio) <- unique(batch)
rownames(dratio) <- rownames(data)
# calculate dratio for each feature, per batch
for (i in 1:nrow(dratio)){
for (nb in 1:8) {
dratio[i, nb] <- DRatfun(samples=data[i, which(!class == "QC" &
batch == nb)], qcs=data[i, which(class == "QC" & batch == nb)])
}
}
# prepare data for plotting
dratio <- as.data.frame(round(dratio, 2))
plotdata2 <- melt(dratio, variable.name="batch")
plotdata2$index <- rownames(data)
plotdata2$index <- factor(plotdata2$index, ordered=TRUE,
levels=unique(plotdata2$index))
ggplot(data=plotdata2, aes(x=index, y=value, color=batch)) +
geom_point(size=2) +
xlab("index") + ylab("D-ratio") +
geom_hline(yintercept=1) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
theme(axis.text.x=element_text(angle=90))
The D-ratio is a convenient measure to assess if technical variation in the QC samples (MAD QC) is higher than the variation within the biological samples (MAD sample). Ratio values close to, or higher than 1 indicate that technical variation of the measured feature is higher than the biological variation and therefore should be treated carefully during interperation of the dataset. The colors in the figure above indicate different analytical batches. In the example above we can see that feature ‘409.05716’ has a D-ratio value above 1 in four batches, while for features ‘70.0336’ and ‘393.35878’ the D-ratio is reproducibly low within all eight batches.
Principal components analysis (PCA) can be used to check common trends in the data. Let’s inspect the scores of the first two principal components and samples colored by batch and class. For PCA model data should be normalised and missing values should be replaced using imputation, followed by data scaling. We will use the probabilistic quotient normalisation (PQN) method to normalise the data, k-nearest neighbours (KNN) for missing value imputation and finally the glog method to stabilise the variance across low and high intensity mass spectral features. See Di Guida et al. (2016) for a more detailed review on common pre-processing steps and methods.
pca_data <- MTBLS79[feature_names, ]
pca_data <- pqn_normalisation(pca_data, classes=class, qc_label="QC")
pca_data <- mv_imputation(pca_data, method="KNN", k=5, rowmax=0.5,
colmax=0.5, maxp=NULL, check_df=FALSE)
pca_data <- glog_transformation(pca_data, classes=class, qc_label="QC")
pca_data <- prcomp(t(SummarizedExperiment::assay(pca_data)), center=TRUE,
scale=FALSE)
exp_var_pca <- round(((pca_data$sdev^2)/sum(pca_data$sdev^2)*100)[1:2], 2)
plots <- list()
plotdata <- data.frame(PC1=pca_data$x[, 1], PC2=pca_data$x[, 2],
batch=as.factor(batch), class=class)
plots[[1]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=batch)) +
geom_point(size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores, before correction") +
xlab(paste0("PC1 (", exp_var_pca[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
plots[[2]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=class)) +
geom_point(size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores plot, before correction") +
xlab(paste0("PC1 (", exp_var_pca[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
grid.arrange(ncol=2, plots[[1]], plots[[2]])
Left side plot above clearly shows that samples measured in batches 7 and 8 are differentiating from bathes 1 to 6. On the right hand side plot the seperation between samples classes is still visible, but also seperation between measurement batches is clearly visible across PC2 axis.
4.1 Correlation between signal intensity and injection order of QC samples
Alternatively, trends in measured signal related to injection order could indicate if signal drif and/or batch effect correction is required. The plot below illustrates the measured signal of QC samples across all 8 batches. To be able to compare all 20 features measured at different signal ranges, the data will be scaled to unit variance (UV).
# autoscale the QC data
QCdata <- data[ ,QChits]
QCdata2 <- as.data.frame(scale(t(QCdata), scale=TRUE, center=TRUE))
# prepare the data for plotting
plotdata <- melt(QCdata2, value.name="intensity")
plotdata$index <- rep(1:nrow(QCdata2), ncol(QCdata2))
plotdata$batch <- as.factor(batch[QChits])
# plot
ggplot(data=plotdata, aes(x=index, y=intensity, col=batch)) +
geom_point(size=2) +
facet_wrap(~ variable, ncol=4) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color)
This figure indicates that there is some fluctuation in the measured signal across the eight batches, and that some features are following a similar pattern, i.e. they are correlated. We can create a similar plot to the one above including linear regression fit between measured data points.
ggplot(data=plotdata, aes(x=index, y=intensity, col=batch)) +
geom_point(size=2) +
facet_wrap(~ variable, ncol=4) +
geom_smooth(method="lm", se=TRUE, colour="black") +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color)
#> `geom_smooth()` using formula = 'y ~ x'
The plot above indicates that some trends can be observed. It is possible to calculate actual correlation values within QC samples for each measured feature, and we will use Kendall’s tau statistic to estimate a rank-based measure of association.
sampleorder <- c(1:ncol(QCdata))
correlations <- matrix(ncol=2, nrow=nrow(data))
rownames(correlations) <- rownames(data)
colnames(correlations) <- c("tau","p.value")
correlations <- as.data.frame(correlations)
for (coln in 1:nrow(data)) {
stat <- cor.test(sampleorder, QCdata[coln, ], method="kendall")
correlations$tau[coln] <- stat$estimate
correlations$p.value[coln] <- stat$p.value
}
correlations
#> tau p.value
#> 70.03364 -0.39118065 4.243865e-04
#> 133.07379 0.62162162 2.908914e-09
#> 146.16519 -0.56472262 1.190541e-07
#> 163.04515 0.10953058 3.417433e-01
#> 174.89483 0.35419630 1.515758e-03
#> 200.03196 0.35704125 1.381142e-03
#> 207.07818 0.18065434 1.135934e-01
#> 221.05062 0.48506401 8.425472e-06
#> 240.02445 0.39118065 4.243865e-04
#> 251.03658 0.26600284 1.863325e-02
#> 266.01793 0.18065434 1.135934e-01
#> 304.99115 0.28591750 1.123389e-02
#> 321.07923 0.43669986 7.237859e-05
#> 338.98131 0.32290185 4.004242e-03
#> 376.03962 0.59601707 1.672311e-08
#> 393.35878 -0.19203414 9.217241e-02
#> 409.05716 0.14935989 1.924150e-01
#> 430.24353 0.21763869 5.561224e-02
#> 451.01086 0.24624625 3.233060e-02
#> 465.14937 0.04694168 6.894542e-01While most of the calculated tau values and corresponding p-values indicate that there is not a strong trend between injection order and the measured QC sample signal, some of the values don’t match the trends we can observe in figure above. For features “133.07379” and “376.03962” calculated correlation values are aroun 0.6 and in figure above intensity increasing tren can be observed. For other features correlation values are relatively low, but in figure above clear trends in signal changes in batches 7 and 8 can be osberved.
Alternatively it is possible to calculate correlation statistics per batch and visualise the results.
correlations <- matrix(ncol=8, nrow=nrow(data))
rownames(correlations) <- rownames(data)
colnames(correlations) <- unique(batch)
QCbatch <- batch[QChits]
for (coln in 1:nrow(data)) {
for (bch in 1:8) {
sampleorder <- scale(c(1:length(which(QCbatch==bch))),
center=TRUE, scale=TRUE)
if ((length(sampleorder) -
length(which(is.na(QCdata[coln, which(QCbatch==bch)])))) >= 3){
correlations[coln, bch] <- cor.test(sampleorder,
QCdata[coln, which(QCbatch==bch)], method="kendall")$estimate
}
}
}
round(correlations, 2)
#> 1 2 3 4 5 6 7 8
#> 70.03364 0.52 0.67 0.33 -0.67 0.0 0.0 0.4 0.67
#> 133.07379 0.62 0.67 0.67 0.33 0.0 0.2 0.4 0.67
#> 146.16519 -0.43 0.33 -0.33 -0.67 -0.6 -0.2 0.4 0.33
#> 163.04515 0.90 0.33 0.67 0.00 0.0 0.2 0.6 -0.67
#> 174.89483 0.14 0.00 0.00 0.00 0.4 0.0 0.6 0.67
#> 200.03196 0.62 0.33 1.00 -0.33 -0.2 0.6 0.8 -0.33
#> 207.07818 -0.71 -0.33 0.33 0.00 0.4 -0.8 0.4 0.67
#> 221.05062 0.52 0.33 0.67 0.33 -0.4 0.0 0.0 0.67
#> 240.02445 0.90 -0.67 0.67 -0.67 0.4 0.6 0.8 -0.33
#> 251.03658 0.43 0.67 0.00 0.67 0.6 0.8 -0.4 0.33
#> 266.01793 0.14 0.33 0.67 0.33 -1.0 0.4 -0.2 1.00
#> 304.99115 0.52 -0.67 1.00 0.00 0.0 0.0 0.0 0.00
#> 321.07923 0.62 0.33 0.67 0.00 0.6 0.4 -0.2 0.67
#> 338.98131 0.71 0.00 0.67 -0.67 0.4 0.2 -0.4 0.67
#> 376.03962 0.43 0.67 0.00 0.00 0.6 -0.2 0.0 -0.67
#> 393.35878 -0.05 0.00 0.00 -0.67 -0.8 0.0 0.4 0.33
#> 409.05716 -0.52 0.67 -0.67 -1.00 0.4 -1.0 0.4 -0.33
#> 430.24353 -0.52 0.00 0.33 -0.33 -0.8 -0.4 0.4 -0.67
#> 451.01086 0.62 -0.33 0.33 -1.00 -0.2 -1.0 -0.4 -0.33
#> 465.14937 0.14 0.33 0.00 -0.33 -0.2 -0.2 0.6 0.33plotdata <- as.data.frame(correlations)
plotdata$feature <- rownames(plotdata)
plotdata <- melt(plotdata, variable.name="batch")
#> Using feature as id variables
plotdata$feature <- factor(plotdata$feature, ordered=TRUE,
levels = unique(plotdata$feature))
ggplot(data=plotdata, aes(x=batch, y=feature, fill=value)) +
geom_tile() + scale_fill_gradient2()
Figure above indicates that there are significant acquisition order related trends within some batches. Fore example, bathes 4 and 6 for features ‘451.01081’ and ‘409.05716’.
4.2 Using regression to estimate signal trends and variability across QC sample
It is possible to apply univariate regression to the QC sample injection order and signal intensity, to estimate correlation and spread (R2) of the measured data points.
sampleorder <- c(1:ncol(QCdata))
regressionout <- matrix(ncol=3, nrow=nrow(data))
rownames(regressionout) <- rownames(data)
colnames(regressionout) <- c("R2.adj","coefficient","p.value")
regressionout <- as.data.frame(regressionout)
for (coln in 1:nrow(data)) {
tempdat <- data.frame(x=sampleorder, y=QCdata[coln, ])
stat <- lm(x ~ y, data=tempdat)
stat <- summary(stat)
regressionout$R2.adj[coln] <- stat$adj.r.squared
regressionout$coefficient[coln] <- stat$coefficients[2,1]
regressionout$p.value[coln] <- stat$coefficients[2,4]
}
regressionout
#> R2.adj coefficient p.value
#> 70.03364 0.51755345 -6.974939e-04 2.171124e-07
#> 133.07379 0.71719172 1.679293e-04 1.256275e-11
#> 146.16519 0.57693876 -5.866596e-04 1.946902e-08
#> 163.04515 0.05007205 1.684384e-04 9.445244e-02
#> 174.89483 0.47156655 1.185480e-03 1.163525e-06
#> 200.03196 0.33228648 1.071348e-03 9.073995e-05
#> 207.07818 -0.02752121 3.324954e-06 9.249914e-01
#> 221.05062 0.47782665 6.996785e-04 9.336168e-07
#> 240.02445 0.33644972 9.756783e-04 8.067073e-05
#> 251.03658 0.24756124 9.028676e-04 8.748657e-04
#> 266.01793 0.03893146 8.832713e-04 1.226793e-01
#> 304.99115 0.46249993 2.492342e-04 1.593517e-06
#> 321.07923 0.48054403 5.318280e-04 8.478678e-07
#> 338.98131 0.31485610 2.306224e-03 1.474563e-04
#> 376.03962 0.69755324 1.741943e-03 4.258938e-11
#> 393.35878 0.10180922 -1.517244e-04 2.869818e-02
#> 409.05716 -0.01861817 2.395911e-06 5.729142e-01
#> 430.24353 0.48561786 2.260156e-04 7.073656e-07
#> 451.01086 0.29461592 2.231798e-03 3.081623e-04
#> 465.14937 -0.01675834 2.478707e-04 5.361537e-01And regression statistics per batch.
regPerBatch <- matrix(ncol=8, nrow=nrow(data))
rownames(regPerBatch) <- rownames(data)
colnames(regPerBatch) <- unique(batch)
QCbatch <- MTBLS79$Batch[QChits]
for (coln in 1:nrow(data)) {
for (bch in 1:8) {
sampleorder <- c(1:length(which(QCbatch == bch)))
tempdat <- data.frame(x=sampleorder, y=QCdata[coln,
which(QCbatch==bch)])
stat <- lm(x ~ y, data=tempdat)
stat <- summary(stat)
regPerBatch[coln,bch] <- stat$adj.r.squared
}
}
round(regPerBatch,2)
#> 1 2 3 4 5 6 7 8
#> 70.03364 0.33 0.34 0.12 0.03 -0.33 -0.04 -0.22 -0.05
#> 133.07379 0.24 0.46 0.32 -0.07 -0.23 -0.24 0.06 0.50
#> 146.16519 0.39 -0.40 -0.06 0.18 0.62 -0.28 0.10 0.04
#> 163.04515 0.92 -0.29 0.47 0.18 -0.30 -0.33 0.68 0.31
#> 174.89483 -0.19 -0.49 -0.50 -0.38 0.05 -0.31 0.39 0.34
#> 200.03196 0.61 -0.06 0.89 -0.43 -0.26 0.54 0.70 -0.33
#> 207.07818 0.76 0.09 0.23 -0.45 -0.03 0.43 -0.01 0.65
#> 221.05062 0.49 0.15 0.48 -0.09 0.15 -0.33 0.11 0.32
#> 240.02445 0.85 0.41 0.54 0.75 -0.11 0.39 0.44 -0.50
#> 251.03658 0.08 0.59 -0.47 0.55 0.55 0.84 0.08 -0.13
#> 266.01793 -0.14 -0.12 -0.12 -0.48 0.89 -0.33 -0.32 0.90
#> 304.99115 0.70 0.45 0.85 -0.47 -0.23 -0.27 -0.21 -0.49
#> 321.07923 0.70 -0.41 0.73 -0.49 0.31 0.24 -0.13 0.50
#> 338.98131 0.58 -0.46 0.56 0.70 0.29 -0.30 -0.15 0.27
#> 376.03962 0.05 -0.03 -0.35 -0.45 0.41 -0.29 -0.32 0.47
#> 393.35878 -0.16 -0.49 -0.44 0.63 0.81 -0.30 0.46 0.07
#> 409.05716 0.29 0.22 0.62 0.89 0.33 0.40 0.03 -0.43
#> 430.24353 0.47 -0.50 -0.07 -0.18 0.84 0.29 0.63 0.70
#> 451.01086 0.46 -0.42 0.23 0.46 -0.32 0.89 0.08 -0.48
#> 465.14937 -0.15 -0.22 -0.47 -0.44 -0.32 -0.22 0.46 -0.17plotdata <- as.data.frame(regPerBatch)
plotdata$feature <- rownames(plotdata)
plotdata <- melt(plotdata, variable.name="batch")
#> Using feature as id variables
plotdata$feature <- factor(plotdata$feature, ordered=TRUE,
levels=unique(plotdata$feature))
ggplot(data=plotdata, aes(x=batch, y=feature, fill=value)) +
geom_tile() + scale_fill_gradient2()
4.3 Example of signal drift and batch effect correction for a single feature
Let’s have a closer look to ‘451.01086’ measured feature and how signal correction can be applied.
data <- data.frame(data=
as.vector(SummarizedExperiment::assay(MTBLS79["451.01086", ])), batch=batch,
class=factor(class, ordered=TRUE))
data$order <- c(1:nrow(data))
data$batch <- as.factor(data$batch)
ggplot(data=data, aes(x=order, y=log(data,10), col=batch, shape=class)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color)
In this plot circles and squares represent biological samples and triangles are the QC samples. Analytical batches are represented by colours. Differences in measured intensities can be observed between analytical batches.
Similar plot for QC samples only
QCdata <- data[data$class == "QC",]
ggplot(data=QCdata, aes(x=order, y=log(data,10), col=batch, shape=class)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color, drop=FALSE) +
scale_shape_manual(values=c(16, 17, 15), drop=FALSE)
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
This figure indicates that there is signal drift present within each analytical batch and between analytical batches for this feature. Let’s have a look at the RSD% for all QC samples and QC samples within each analytical batch.
FUN <- function(x) sd(x, na.rm=TRUE)/mean(x, na.rm=TRUE) * 100
# RSD% of biological and QC samples within all 6 batches:
out <- c(NA,NA)
names(out) <- c("Biological","QC")
out[1] <-FUN(data$data[-QChits])
out[2] <-FUN(data$data[QChits])
out
#> Biological QC
#> 39.76459 49.97187# RSD% per batch:
out <- matrix(ncol=8,nrow=2)
colnames(out) <- unique(batch)
rownames(out) <- c("Biological","QC")
for (i in 1:8) {
out[1, i] <- FUN(data$data[which(!class=="QC" & batch==i)])
out[2, i] <- FUN(data$data[which(class=="QC" & batch==i)])
}
out
#> 1 2 3 4 5 6 7
#> Biological 35.55823 39.56935 34.76017 49.61587 38.86485 47.646959 31.45103
#> QC 21.27721 51.25584 23.32173 31.68287 25.70845 7.814082 13.79785
#> 8
#> Biological 32.97728
#> QC 13.49659From the outputs above it’s clear that variance of measured QC sample intensities between batches is high (RSD% = 50), and is relatively high within batches 1 to 5. For batch 3 QC variation exceeds that of the biological samples.
5 Signal drift and batch effect correction using smoothed spline fitting
We will apply QC-RSC signal correction method as it is described in Kirwan et al. (2013).
The first step involves extracting QC sample data
qcData <- data$data[class == "QC"]
qc_batch <- batch[class == "QC"]
qc_order <- order[class == "QC"]
qcData
#> [1] 4005.083 2955.796 4552.907 4958.136 5760.821 5812.888 4900.625
#> [8] 6010.015 3120.287 8224.495 2765.307 3964.597 3459.146 5813.192
#> [15] 5104.457 6388.102 3694.081 3691.042 3553.922 3431.332 2165.299
#> [22] 1883.597 3399.188 2998.507 4360.648 4282.248 NA 4016.637
#> [29] 3654.521 11715.840 8870.079 11131.250 8710.432 9369.025 11047.540
#> [36] 9127.718 8363.207 10924.000Note that the QC data has 1 missing value. Smoothed spline regression doesn’t support missing values, so the workaround is to apply missing value imputation or remove the NA values from input to the smoothed spline fit function (which we will do here). We recommend at least 4 QC values be present per batch for the fit to be reliable.
The next step involves applying the smoothed spline fit function to the QC sample data within each batch. We will look at the data for batch 6 in detail.
nbatch <- unique(qc_batch)
nb <- 6
# Sample injection order
x <- qc_order[qc_batch==nbatch[nb]]
# Measured peak intensity or area
y <- qcData[qc_batch==nbatch[nb]]
y
#> [1] 4360.648 4282.248 NA 4016.637 3654.521In this example, signal for 1 QC sample wasn’t measured, so these samples need to be removed. The smoothed spline regression input will look like this:
NAhits <- which(is.na(y))
if (length(NAhits)>0) {
x <- x[-c(NAhits)]
y <- y[-c(NAhits)]
rbind(x,y)
}
#> [,1] [,2] [,3] [,4]
#> x 109.000 112.000 123.000 128.000
#> y 4360.648 4282.248 4016.637 3654.521We will apply a log transformation to the data before fitting
y <- log((y + sqrt(y^2)) / 2)
y
#> [1] 8.380376 8.362233 8.298200 8.203720Fit a smoothed cubic spline using internal cross-validation for parameter estimation
sp.obj <- smooth.spline(x, y, cv=TRUE)
sp.obj
#> Call:
#> smooth.spline(x = x, y = y, cv = TRUE)
#>
#> Smoothing Parameter spar= 0.3748593 lambda= 0.001544601 (15 iterations)
#> Equivalent Degrees of Freedom (Df): 3.34102
#> Penalized Criterion (RSS): 7.185226e-05
#> PRESS(l.o.o. CV): 0.001528177
out <- rbind(y,sp.obj$y)
row.names(out) <- c("measured","fitted")
out
#> [,1] [,2] [,3] [,4]
#> measured 8.380376 8.362233 8.298200 8.203720
#> fitted 8.379251 8.365584 8.291754 8.207941Now the smoothed spline fit is used to predict values for the biological sample for the current batch.
valuePredict=predict(sp.obj, order[batch==nb])
plotchr <- as.numeric(data$class)
# reverse the log transformation to convert the predictions back to the
# original scale
valuePredict$y <- exp(valuePredict$y)
plotdata <- data.frame(measured=data$data[batch==nb], fitted=valuePredict$y,
Class=class[batch==nb], order=order[batch==nb])
plotdata2 <- melt(plotdata, id.vars=c("Class","order"), value.name="intensity",
variable.name="data")
ggplot(data=plotdata2, aes(x=order, y=log(intensity,10), color=data,
shape=Class)) + geom_point(size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
scale_shape_manual(values=c(16, 17, 15), drop=FALSE)
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
The figure above shows the original data points in blue and the fitted data in red. Triangles represent QC samples.
The next step in signal correction is to “flatten” the fitted curve to correct for signal drift. This can usually be done by subtracting the fitted values from the actual measured values for each feature. To avoid getting negative values we will add the median value of the feature to the corrected data.
fitmedian <- median(plotdata$measured, na.rm=TRUE)
plotdata$corrected_subt <- (plotdata$measured - plotdata$fitted) + fitmedian
plotdata2 <- melt(plotdata, id.vars=c("Class","order"),
value.name="intensity", variable.name="data")
plotdata_class <- as.character(plotdata2$Class)
plotdata_class[plotdata_class == "S"] <- "Sample"
plotdata_class[plotdata_class == "C"] <- "Sample"
plotdata2$Class <- factor(plotdata_class)
ggplot(data=plotdata2, aes(x=order, y=intensity, color=data, shape=Class)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
facet_grid(Class ~ .) +
scale_shape_manual(values=c(17, 16), drop=FALSE)
An alternative to subtraction of the fitted values is to divide them by the median of the fit and use the resulting coefficients to correct the data points. The same general relative trends should be observed in either case.
plotdata$corrected_div <- plotdata$measured/(plotdata$fitted/fitmedian)
plotdata3 <- plotdata[,c("Class", "order", "corrected_subt", "corrected_div")]
plotdata3 <- melt(plotdata3, id.vars=c("Class","order"),
value.name="intensity", variable.name="data")
plotdata_class <- as.character(plotdata3$Class)
plotdata_class[plotdata_class=="S"] <- "Sample"
plotdata_class[plotdata_class=="C"] <- "Sample"
plotdata3$Class <- factor(plotdata_class)
ggplot(data=plotdata3, aes(x=order, y=intensity, color=data, shape=Class)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
geom_smooth(se=FALSE) + facet_grid(Class ~ .)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
So far we have applied signal correction for data points within one analytical batch. The code below will perform the same steps for each of the 8 batches.
outl <- rep(NA, nrow(data))
for (nb in 1:length(nbatch)){
# assigning sample injection order for a batch to 'x', and corresponding
# intensities to 'y'
x <- qc_order[qc_batch == nbatch[nb]]
y <- qcData[qc_batch == nbatch[nb]]
# remove measurements with missing values
NAhits <- which(is.na(y))
if (length(NAhits) > 0) {
x <- x[-c(NAhits)]
y <- y[-c(NAhits)]
}
# require at least 4 data points for QC fit
if (length(y) >= 4) {
range <- c(batch == nbatch[nb])
# Order is a vector of sample indices for the current batch
outl[range] <- pmp:::splineSmoother(x=x, y=y, newX=order[range],
log=TRUE, a=1, spar=0)
# If less than 5 data points are present, return empty values
} else {
range <- c(batch == nbatch[nb])
outl[range] <- rep(NA, nrow(data))[range]
}
}
plotdata <- data.frame(measured=data$data, fitted=outl, Class=class,
batch=batch, order=c(1:nrow(data)))
plotdata2 <- melt(plotdata, id.vars=c("batch","Class","order"),
value.name="intensity", variable.name="data")
ggplot(data=plotdata2, aes(x=order, y=log(intensity,10),
color=data, shape=Class)) + geom_point(alpha=0.5, size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color)
After smoothed spline fit per each batch is calculated, we can apply signal correction within each batch.
# median intensity value is used to adjust batch effect
mpa <- rep(NA, nrow(data))
for (bch in 1:8) {
mpa[batch==bch] <- median(data$data[batch==bch], na.rm=TRUE)
}
QC_fit <- outl/mpa
# and correct actual data
res <- data$data/QC_fit
# correct data using subtratcion
res2 <- (data$data -outl) +mpa
plotdata <- data.frame(measured=data$data, corrected_subt=res2,
corrected_div=res, Class=class, batch=batch, order=c(1:nrow(data)))
plotdata2 <- melt(plotdata, id.vars=c("batch","Class","order"),
value.name="intensity", variable.name="data")
ggplot(data=plotdata2, aes(x=order, y=log(intensity,10),
color=data, shape=Class)) + geom_point(alpha=0.2, size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
geom_smooth(se=FALSE) +
facet_grid(Class ~ .)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
The figure above shows the measured data points in blue and the corrected values using subtraction (red) or division(green). Fitted smoothed spline curves of the corrected data over all batches still indicates that there is batch related signal drift in the data. This can be corrected using the “grand median”.
First, a grand median is calculated across all batches, and then difference between each batch median and the grand median is subtracted from all the samples in that batch, to remove the difference.
mpa <- rep(NA, nrow(data))
for (bch in 1:8) {
mpa[batch == bch] <- median(res2[batch == bch], na.rm=TRUE)
}
grandMedian <- median(res2, na.rm=TRUE)
mpa <- mpa - grandMedian
plotdata$corrected_subt <- plotdata$corrected_subt - mpa
mpa <- rep(NA, nrow(data))
for (bch in 1:8) {
mpa[batch == bch] <- median(res[batch == bch], na.rm=TRUE)
}
grandMedian <- median(res, na.rm=TRUE)
mpa <- mpa - grandMedian
plotdata$corrected_div <- plotdata$corrected_div - mpa
plotdata2 <- melt(plotdata, id.vars=c("batch","Class","order"),
value.name="intensity", variable.name="data")
ggplot(data=plotdata2, aes(x=order, y=log(intensity,10),
color=data, shape=Class)) + geom_point(alpha=0.2, size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
geom_smooth(se=FALSE) +
facet_grid(Class ~ .)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
We can calculate RSD% before and after correction.
FUN <- function(x) sd(x, na.rm=TRUE)/mean(x, na.rm=TRUE) * 100
# RSD% of biological and QC samples within all 6 batches:
out <- matrix(nrow=2, ncol=2)
colnames(out) <- c("Biological","QC")
rownames(out) <- c("measured", "corrected")
out[1,1] <-FUN(data$data[-QChits])
out[1,2] <-FUN(data$data[QChits])
out[2,1] <-FUN(res[-QChits])
out[2,2] <-FUN(res[QChits])
round(out, 2)
#> Biological QC
#> measured 39.76 49.97
#> corrected 50.21 33.19
# RSD% per batch:
out <- matrix(ncol=8,nrow=4)
colnames(out) <- unique(batch)
rownames(out) <- c("Biological","QC","Corrected biological","Corrected QC")
for(i in 1:8) {
out[1, i] <- FUN(data$data[which(!class=="QC" & batch==i)])
out[2, i] <- FUN(data$data[which(class=="QC" & batch==i)])
out[3, i] <- FUN(res[which(!class=="QC" & batch==i)])
out[4, i] <- FUN(res[which(class=="QC" & batch==i)])
}
round(out, 2)
#> 1 2 3 4 5 6 7 8
#> Biological 35.56 39.57 34.76 49.62 38.86 47.65 31.45 32.98
#> QC 21.28 51.26 23.32 31.68 25.71 7.81 13.80 13.50
#> Corrected biological 37.82 45.26 38.67 59.79 38.23 47.99 31.22 37.15
#> Corrected QC 11.54 53.40 16.76 6.17 18.34 0.49 11.02 0.00The data has now been corrected for batch and signal drift effects.
All the steps from example above can be applied to all or susbet of features in the data set using function “QCRSC”.
data <- MTBLS79[feature_names,]
class <- MTBLS79$Class
batch <- MTBLS79$Batch
sample_order <- c(1:ncol(data))
corrected_data <- QCRSC(df=data, order=sample_order, batch=batch,
classes=class, spar=0, minQC=4)We can calculate RSD% statistics per batch before and after correction and visualise the results with a box plot.
data <- SummarizedExperiment::assay(data)
corrected_data <- SummarizedExperiment::assay(corrected_data)
RSDQC <- matrix(ncol=8, nrow=nrow(data))
RSDsample <- matrix(ncol=8, nrow=nrow(data))
colnames(RSDQC) <- unique(batch)
colnames(RSDsample) <- unique(batch)
RSDQC_corrected <- matrix(ncol=8, nrow=nrow(data))
RSDsample_corrected <- matrix(ncol=8, nrow=nrow(data))
colnames(RSDQC_corrected) <- unique(batch)
colnames(RSDsample_corrected) <- unique(batch)
rownames(RSDQC) <- rownames(data)
rownames(RSDsample) <- rownames(data)
rownames(RSDQC_corrected) <- rownames(data)
rownames(RSDsample_corrected) <- rownames(data)
# for each feature
for (i in 1:nrow(data)) {
# for each batch
for (nb in 1:8) {
# RSD% of QCs in this batch
RSDQC[i, nb] <- FUN(data[i, which(class == "QC" & batch == nb)])
# RSD% of samples in this batch
RSDsample[i, nb] <- FUN(data[i, which(!class == "QC" & batch == nb)])
# RSD% of QCs in this batch after correction
RSDQC_corrected[i, nb] <- FUN(corrected_data[i, which(class == "QC"
& batch == nb)])
# RSD% of samples in this batch after correction
RSDsample_corrected[i, nb] <- FUN(corrected_data[i, which(!class == "QC"
& batch == nb)])
}
}
# prepare results for plotting
plotdataQC <- melt(as.data.frame(RSDQC), variable.name="batch",
value.name="RSD")
#> No id variables; using all as measure variables
plotdataQC$Class <- "QC"
plotdataBio <- melt(as.data.frame(RSDsample), variable.name="batch",
value.name="RSD")
#> No id variables; using all as measure variables
plotdataBio$Class <- "Sample"
plotdataQC_corrected <- melt(as.data.frame(RSDQC_corrected),
variable.name="batch", value.name="RSD")
#> No id variables; using all as measure variables
plotdataQC_corrected$Class <- "QC_corr"
plotdataBio_corrected <- melt(as.data.frame(RSDsample_corrected),
variable.name="batch", value.name="RSD")
#> No id variables; using all as measure variables
plotdataBio_corrected$Class <- "Sample_corr"
plotdata <- rbind(plotdataQC, plotdataQC_corrected)
plotdata$Class <- as.factor(plotdata$Class)
# plot
ggplot(data=plotdata, aes(x=Class, y=RSD, fill=Class)) + geom_boxplot() +
facet_wrap(~ batch, ncol=3) +
ylab("RSD%") +
xlab("") +
scale_x_discrete(labels=NULL) +
theme(panel.background=element_blank(), axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_y_continuous(limits=c(0, 50))
plotdata <- rbind(plotdataBio, plotdataBio_corrected)
plotdata$Class <- as.factor(plotdata$Class)
# plot
ggplot(data=plotdata, aes(x=Class, y=RSD, fill=Class)) + geom_boxplot() +
facet_wrap(~ batch, ncol=3) +
ylab("RSD%") +
xlab("") +
theme(panel.background=element_blank(), axis.text.x=element_blank(),
axis.ticks.x=element_blank())
Or compare the scores plots of principal components analysis (PCA) before and after correction.
# PQN used to normalise data
# KNN for missing value imputation
# glog scaling method
# A more detailed overview is detailed in
# Di Guida et al, Metabolomics, 12:93, 2016
# https://dx.doi.org/10.1007/s11306-016-1030-9
pca_data <- pqn_normalisation(data, classes=class, qc_label="QC")
pca_data <- mv_imputation(pca_data, method="KNN", k=5, rowmax=0.5,
colmax=0.5, maxp=NULL, check_df=FALSE)
pca_data <- glog_transformation(pca_data, classes=class, qc_label="QC")
pca_corrected_data <- pqn_normalisation(corrected_data, classes=class,
qc_label="QC")
pca_corrected_data <- mv_imputation(pca_corrected_data, method="KNN", k=5,
rowmax=0.5, colmax=0.5, maxp=NULL, check_df=FALSE)
pca_corrected_data <- glog_transformation(pca_corrected_data,
classes=class, qc_label="QC")
pca_data <- prcomp(t(pca_data), center=TRUE, scale=FALSE)
pca_corrected_data <- prcomp(t(pca_corrected_data), center=TRUE, scale=FALSE)
# Calculate percentage of explained variance of the first two PC's
exp_var_pca <- round(((pca_data$sdev^2)/sum(pca_data$sdev^2)*100)[1:2],2)
exp_var_pca_corrected <- round(((pca_corrected_data$sdev^2) /
sum(pca_corrected_data$sdev^2)*100)[1:2],2)
plots <- list()
plotdata <- data.frame(PC1=pca_data$x[, 1], PC2=pca_data$x[, 2],
batch=as.factor(batch), class=class)
plots[[1]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=batch)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores, before correction") +
xlab(paste0("PC1 (", exp_var_pca[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
plots[[2]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=class)) +
geom_point(size=2) + theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores, before correction") +
xlab(paste0("PC1 (", exp_var_pca[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
plotdata_corr <- data.frame(PC1=pca_corrected_data$x[, 1],
PC2=pca_corrected_data$x[, 2], batch=as.factor(batch), class=class)
plots[[3]] <- ggplot(data=plotdata_corr, aes(x=PC1, y=PC2, col=batch)) +
geom_point(size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores, after correction") +
xlab(paste0("PC1 (", exp_var_pca_corrected[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca_corrected[2] ," %)"))
plots[[4]] <- ggplot(data=plotdata_corr, aes(x=PC1, y=PC2, col=class)) +
geom_point(size=2) +
theme(panel.background=element_blank()) +
scale_color_manual(values=manual_color) +
ggtitle("PCA scores, after correction") +
xlab(paste0("PC1 (", exp_var_pca_corrected[1] ," %)")) +
ylab(paste0("PC2 (", exp_var_pca_corrected[2] ," %)"))
grid.arrange(ncol=2, plots[[1]], plots[[2]], plots[[3]], plots[[4]])
6 Session information
sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] gridExtra_2.3 reshape2_1.4.4
#> [3] ggplot2_3.5.1 SummarizedExperiment_1.34.0
#> [5] Biobase_2.64.0 GenomicRanges_1.56.0
#> [7] GenomeInfoDb_1.40.0 IRanges_2.38.0
#> [9] S4Vectors_0.42.0 BiocGenerics_0.50.0
#> [11] MatrixGenerics_1.16.0 matrixStats_1.3.0
#> [13] pmp_1.16.0 BiocStyle_2.32.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.5 impute_1.78.0 xfun_0.43
#> [4] bslib_0.7.0 lattice_0.22-6 vctrs_0.6.5
#> [7] tools_4.4.0 generics_0.1.3 parallel_4.4.0
#> [10] missForest_1.5 tibble_3.2.1 fansi_1.0.6
#> [13] highr_0.10 pkgconfig_2.0.3 Matrix_1.7-0
#> [16] rngtools_1.5.2 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
#> [19] farver_2.1.1 stringr_1.5.1 compiler_4.4.0
#> [22] tinytex_0.50 munsell_0.5.1 codetools_0.2-20
#> [25] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8
#> [28] pillar_1.9.0 crayon_1.5.2 jquerylib_0.1.4
#> [31] DelayedArray_0.30.0 cachem_1.0.8 magick_2.8.3
#> [34] doRNG_1.8.6 iterators_1.0.14 abind_1.4-5
#> [37] foreach_1.5.2 nlme_3.1-164 pcaMethods_1.96.0
#> [40] tidyselect_1.2.1 digest_0.6.35 stringi_1.8.3
#> [43] dplyr_1.1.4 bookdown_0.39 splines_4.4.0
#> [46] labeling_0.4.3 fastmap_1.1.1 grid_4.4.0
#> [49] colorspace_2.1-0 cli_3.6.2 SparseArray_1.4.0
#> [52] magrittr_2.0.3 S4Arrays_1.4.0 randomForest_4.7-1.1
#> [55] utf8_1.2.4 withr_3.0.0 scales_1.3.0
#> [58] UCSC.utils_1.0.0 rmarkdown_2.26 XVector_0.44.0
#> [61] httr_1.4.7 evaluate_0.23 knitr_1.46
#> [64] mgcv_1.9-1 rlang_1.1.3 itertools_0.1-3
#> [67] Rcpp_1.0.12 glue_1.7.0 BiocManager_1.30.22
#> [70] jsonlite_1.8.8 plyr_1.8.9 R6_2.5.1
#> [73] zlibbioc_1.50.0References
Broadhurst, D., Goodacre, R., Reinke, S.N., et al. (2018) Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14 (6): 72. doi:10.1007/s11306-018-1367-3.
Di Guida, R., Engel, J., Allwood, J.W., et al. (2016) Non-targeted uhplc-ms metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics, 12 (5): 93. doi:10.1007/s11306-016-1030-9.
Kirwan, J.A., Weber, R.J., Broadhurst, D.I., et al. (2014) Direct infusion mass spectrometry metabolomics dataset: A benchmark for data processing and quality control. Scientific data, 1: 140012. doi:10.1038/sdata.2014.12.
Kirwan, J., Broadhurst, D., Davidson, R., et al. (2013) Characterising and correcting batch variation in an automated direct infusion mass spectrometry (dims) metabolomics workflow. Analytical and bioanalytical chemistry, 405 (15): 5147–5157. doi:10.1007/s00216-013-6856-7.