Skip to contents

Programmatic analysis of UV spectra

Source: vignettes/articles/uv_spectra.Rmd
uv_spectra.Rmd
pks <- get_peaks(Sa_warp,lambdas=210)
pktab <- get_peaktable(pks)
pktab <- attach_ref_spectra(pktab, ref = "max.cor")

After assigning reference spectra to our peak table, we can use pearson correlations to easily locate peaks falling within a certain similarity threshold to a reference spectrum. To do this, we must first construct a correlation matrix using the cor function.

Analysis of spectral correlations 
cor_mat <- cor(pktab$ref_spectra)

We can then plot spectra falling within a desired similarity threshold (t) to a reference peak (in this case ‘V21’).

par(mfrow=c(2,2))
idx_90 <- which(cor_mat[,"V21"] > .9)
matplot(pktab$ref_spectra[,idx_90],type='l', ylab="Abs")
legend("top", "t = 0.90", bty = "n")

idx_95 <- which(cor_mat[,"V21"] > .95)
matplot(pktab$ref_spectra[,idx_95],type='l', ylab="Abs")
legend("top", "t = 0.95", bty = "n")

idx_97 <- which(cor_mat[,"V21"] > .97)
matplot(pktab$ref_spectra[,idx_97],type='l', ylab="Abs")
legend("top", "t = 0.97", bty = "n")

idx_99 <- which(cor_mat[,"V21"] > .99)
matplot(pktab$ref_spectra[,idx_99],type='l', ylab="Abs")
legend("top", "t = 0.99", bty = "n")

We can also extract retention times for spectra above the desired spectral similarity threshold with the reference spectra.

rts_99 = pktab$pk_meta["rt", idx_99]
rts_99
#>       V9   V17   V21   V26
#> rt 12.18 14.44 15.51 16.78

Below we plot traces of our four chromatograms at 210 nm. The 4 peaks that match the reference spectrum at the 99% level are marked with solid black lines and the 6 peaks that match the reference spectrum at the 97% level are marked with dotted gray lines.

par(mfrow = c(1, 1))
plot_chroms(Sa_warp, lambdas=210)

abline(v = rts_99)
abline(v = pktab$pk_meta["rt", idx_97], lty=2, col = "darkgray")

If we are interested in the combined area of these peaks, we could also use these indices to sum them together.

apply(pktab$tab[,idx_99], 1, sum)
#>      119      121      122      458 
#> 143.6671 100.9055 112.6122 171.6950
Hierarchical clustering of spectra

chromatographR also includes a function (cluster_spectra) to group spectra using hierarchical clustering. We can set the seed with the iseed argument to ensure repeatability among instantiations of this vignette. For simplicity, we use the argument max.only = TRUE to return only the largest clusters.

par(mfrow=c(2,2))
cl <- cluster_spectra(pktab, alpha = 0.01, iseed = 1, max.only = TRUE)

The output is a dendrogram of peaks annotated with bootstrap values showing the support for each node and spectra of the clusters with p-values below the significance level specified by alpha. In this case, we recovered two clusters, representing two different classes of compounds distinguished by their chromophores. The second cluster closely matches the spectrum represented by peak 'V21'. We can access the peaks in cluster two as follows:

cl2 <- cl$clusters$c2@peaks
cl2
#>  [1] "V6"  "V9"  "V11" "V14" "V15" "V17" "V19" "V21" "V22" "V23" "V26"

In this case, the peaks in cluster two happen to overlap completely with the peaks recovered through pearson correlation at the 90% threshold.

all.equal(cl2, names(idx_90))
#> [1] TRUE