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<h1 class="title toc-ignore">Classify cells based on FUCCI: compare PAM vs. Mclust</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
shared across the workflowr files. -->
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<p><strong>Last updated:</strong> 2018-02-23</p>
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package git2r is installed -->
<p><strong>Code version:</strong> c881cde</p>
<hr />
<div id="overviewresults" class="section level2">
<h2>Overview/Results</h2>
<p>I fit PAM and norma-based mixture models on the data that have already been filtered for quality single cells using both RNA-sequencing and microscopy data. The goal here is to identify a subset of cells that are less noisy and use these cells to estimate cell cycle phase.</p>
<p><span class="math inline">\(~\)</span></p>
<p>Results:</p>
<ol style="list-style-type: decimal">
<li><p>Compare PAM vs. Mclust results: seems that PAM places cluster centers at the most densely distributed region. and on the other hand, Mclust places cluster centers at the region that is the “center” of the points assigned to the clusters.</p></li>
<li><p>Select subset of cells: using PAM results, I compute silhouette index for all samples and the choose the top 25 samples in each cluster for each individual. These samples are used in some analysis to evaluate model fit on less noisy data, such as the analysis applying cellcycleR to both imaging data and sequencing data (<a href="https://jdblischak.github.io/fucci-seq/cellcycler-images-seqdata.html">here</a>).</p></li>
</ol>
<p>Let <span class="math inline">\(s(i)\)</span> denotes the silhouette index of sample <span class="math inline">\(i\)</span>. <span class="math inline">\(s(i)\)</span> ranges between -1 to 1. A large value of <span class="math inline">\(s(i)\)</span> indicates that sample <span class="math inline">\(i\)</span> is more similar to samples belonged to its own cluster than any other clusters, and a small value of <span class="math inline">\(s(i)\)</span> indicates that sample <span class="math inline">\(i\)</span> is dissimilar to samples belonged to its own clusters and matches better to samples belonged in some other clusters.</p>
<p><span class="math inline">\(a(i)\)</span>: Average distance of <span class="math inline">\(i\)</span> with all other samples within the same cluster.</p>
<p><span class="math inline">\(b(i)\)</span>: Lowest average distance of <span class="math inline">\(i\)</span> to all samples in any other cluster, of which <span class="math inline">\(i\)</span> is not a member. In other words, the average distance of sample <span class="math inline">\(i\)</span> to all samples in the neighboring cluster.</p>
<p><span class="math display">\[
s(i) = \frac{b(i)-a(i)}{max\{ a(i), b(i)\}}
\]</span></p>
<hr />
</div>
<div id="data-and-packages" class="section level2">
<h2>Data and packages</h2>
<p>Packages</p>
<pre class="r"><code>library(Biobase)
library(ggplot2)
library(cowplot)
library(cluster)
library(mclust)
library(data.table)
library(tidyr)</code></pre>
<p>Load data</p>
<pre class="r"><code>df <- readRDS(file="../data/eset-filtered.rds")
pdata <- pData(df)
fdata <- fData(df)
# select endogeneous genes
counts <- exprs(df)[grep("ERCC", rownames(df), invert=TRUE), ]
pdata.adj <- readRDS("../output/images-normalize-anova.Rmd/pdata.adj.rds")</code></pre>
<hr />
</div>
<div id="raw-data" class="section level2">
<h2>Raw data</h2>
<p>Fitting PAM for cells using only green and red intensity measurements.</p>
<pre class="r"><code>ints <- with(pdata, data.frame(rfp.median.log10sum,
gfp.median.log10sum))
ints <- data.frame(ints,
chip_id = as.factor(pdata$chip_id))
k=3
pam_fit <- lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i],
select = -c(chip_id))
fit_sub <- pam(df_sub, k=k, diss=F)
return(fit_sub)
})
names(pam_fit) <- unique(ints$chip_id)
mclust_fit <- lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i],
select = -c(chip_id))
fit_sub <- Mclust(df_sub, G=k)
return(fit_sub)
})
names(mclust_fit) <- unique(ints$chip_id)
# get centers of each cluster
pam_centers <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
fit <- pam_fit[[i]]
ints_sub <- ints[ints$chip_id == unique(ints$chip_id)[i],]
tmp <- fit$id.med
tmp <- data.frame(ints_sub[fit$id.med,],
center=c(1:3))
return(tmp) }) )
mclust_centers <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
fit <- mclust_fit[[i]]
tmp <- fit$parameters$mean
tmp <- t(tmp)
tmp <- data.frame(tmp,center=c(1:3),
chip_id=names(mclust_fit)[i])
return(tmp) }) )
# make data read for ggplot
pam_fit_plot <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i])
df_sub <- data.frame(df_sub, cluster=as.factor(pam_fit[[i]]$clustering))
return(df_sub)
}))
mclust_fit_plot <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i])
df_sub <- data.frame(df_sub, cluster=as.factor(mclust_fit[[i]]$classification))
return(df_sub)
}))</code></pre>
<div id="compare-pam-vs.mclust" class="section level3">
<h3>Compare PAM vs. Mclust</h3>
<p><strong>k=3</strong></p>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-4-1.png" width="960" style="display: block; margin: auto;" /></p>
</div>
<div id="compute-silhouette" class="section level3">
<h3>Compute silhouette</h3>
<p>Combine silhouette data with the intensity data.</p>
<pre class="r"><code>si_pam <- vector("list", length(pam_fit))
for (i in 1:length(si_pam)) {
si_tmp <- silhouette(pam_fit[[i]])
si_out <- lapply(1:3, function(cl) {
ord <- order(si_tmp[si_tmp[,1]==cl, 3], decreasing=TRUE)
ii <- as.numeric(rownames(si_tmp)[si_tmp[,1]==cl])
ii <- ii[ord]
df_sub <- data.frame(ints[ii,],
unique_id = rownames(pdata)[ii],
cluster=cl,
si=si_tmp[si_tmp[,1]==cl, 3])
})
si_out <- do.call(rbind, si_out)
si_pam[[i]] <- si_out
}
si_pam_long <- do.call(rbind, si_pam)</code></pre>
<p>Choose the top 25 in each cluster.</p>
<pre class="r"><code>cutoff_nsamples <- 20
foo <- lapply(1:uniqueN(si_pam_long$chip_id), function(i) {
foo2 <- lapply(1:uniqueN(si_pam_long$cluster), function(j) {
df_tmp <- subset(si_pam_long, chip_id == unique(si_pam_long$chip_id)[i] & cluster == unique(si_pam_long$cluster)[j])
if (cutoff_nsamples > dim(df_tmp)[1]) {
df_tmp_sub <- df_tmp
} else {
df_tmp_sub <- df_tmp[which(order(df_tmp$si, decreasing = T) %in% c(1:cutoff_nsamples)),]
}
return(df_tmp_sub)
})
foo2 <- do.call(rbind, foo2)
return(foo2)
})
foo <- do.call(rbind, foo)
si_pam_25 <- foo</code></pre>
<p>Compare full set versus subset of cells.</p>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-7-1.png" width="960" style="display: block; margin: auto;" /></p>
<p>hard code cluster labels. default in ggplot is 1 orange, 2 green, 3 blue</p>
<pre class="r"><code># NA18511
# orange 1 to 3, green 2 to 2, blue 3 to 1,
# NA18855
# orange 1 to 2, green 2 to 3, blue 3 to 1,
# NA18870
# orange 1 to 2, green 2 to 1, blue 3 to 3
# NA19098
# orange 1 to 1, green 2 to 3, blue 3 to 2
# NA19101
# orange 1 to 3, green 2 to 2, blue 3 to 1
# NA19160
# orange 1 to 2, green 2 to 3, blue 3 to 1
tmp <- si_pam_long
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==1,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==2,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==2,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==3,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==1,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==3,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==1,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==2,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==3,]$cluster <- 1
plot_grid(
ggplot(data=si_pam_long) +
geom_point(aes(x=gfp.median.log10sum,
y=rfp.median.log10sum, col=as.factor(cluster)),
alpha = .5, cex = .7) +
geom_point(data=pam_centers,
aes(x=gfp.median.log10sum,
y=rfp.median.log10sum), shape=4, size=3) +
labs(title = "K=3, PAM",
x="GFP intensity (log10 pixel sum)",
y = "RFP intensity (log10 pixel sum)") +
facet_wrap(~as.factor(chip_id), ncol=3) +
theme_gray() + theme(legend.position="none"),
ggplot(data=si_pam_25) +
geom_point(aes(x=gfp.median.log10sum,
y=rfp.median.log10sum, col=as.factor(cluster)),
alpha = .5, cex = .7) +
geom_point(data=pam_centers,
aes(x=gfp.median.log10sum,
y=rfp.median.log10sum), shape=4, size=3) +
labs(title = "Top 25 samples within clusters",
x="GFP intensity (log10 pixel sum)",
y = "RFP intensity (log10 pixel sum)") +
facet_wrap(~as.factor(chip_id), ncol=3) +
theme_gray() + theme(legend.position="none")
)</code></pre>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>save subset of cells to rds.</p>
<pre class="r"><code>save(si_pam_long, si_pam_25,
file = "../output/images-subset-silhouette.Rmd/si_pam.rda")</code></pre>
<hr />
</div>
</div>
<div id="adjusted-data" class="section level2">
<h2>Adjusted data</h2>
<p>Fitting PAM for cells using only green and red intensity measurements.</p>
<pre class="r"><code>ints <- with(pdata.adj, data.frame(rfp.median.log10sum.adjust.ash,
gfp.median.log10sum.adjust.ash))
ints <- data.frame(ints,
chip_id = as.factor(pdata$chip_id))
k=3
pam_fit <- lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i],
select = -c(chip_id))
fit_sub <- pam(df_sub, k=k, diss=F)
return(fit_sub)
})
names(pam_fit) <- unique(ints$chip_id)
mclust_fit <- lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i],
select = -c(chip_id))
fit_sub <- Mclust(df_sub, G=k)
return(fit_sub)
})
names(mclust_fit) <- unique(ints$chip_id)
# get centers of each cluster
pam_centers <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
fit <- pam_fit[[i]]
ints_sub <- ints[ints$chip_id == unique(ints$chip_id)[i],]
tmp <- fit$id.med
tmp <- data.frame(ints_sub[fit$id.med,],
center=c(1:3))
return(tmp) }) )
mclust_centers <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
fit <- mclust_fit[[i]]
tmp <- fit$parameters$mean
tmp <- t(tmp)
tmp <- data.frame(tmp,center=c(1:3),
chip_id=names(mclust_fit)[i])
return(tmp) }) )
# make data read for ggplot
pam_fit_plot <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i])
df_sub <- data.frame(df_sub, cluster=as.factor(pam_fit[[i]]$clustering))
return(df_sub)
}))
mclust_fit_plot <- do.call(rbind, lapply(1:uniqueN(ints$chip_id), function(i) {
df_sub <- subset(ints, chip_id==unique(chip_id)[i])
df_sub <- data.frame(df_sub, cluster=as.factor(mclust_fit[[i]]$classification))
return(df_sub)
}))</code></pre>
<div id="compare-pam-vs.mclust-1" class="section level3">
<h3>Compare PAM vs. Mclust</h3>
<p><strong>k=3</strong></p>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-11-1.png" width="960" style="display: block; margin: auto;" /></p>
</div>
<div id="compute-silhouette-1" class="section level3">
<h3>Compute silhouette</h3>
<p>Combine silhouette data with the intensity data.</p>
<pre class="r"><code>si_pam <- vector("list", length(pam_fit))
for (i in 1:length(si_pam)) {
si_tmp <- silhouette(pam_fit[[i]])
si_out <- lapply(1:3, function(cl) {
ord <- order(si_tmp[si_tmp[,1]==cl, 3], decreasing=TRUE)
ii <- as.numeric(rownames(si_tmp)[si_tmp[,1]==cl])
ii <- ii[ord]
df_sub <- data.frame(ints[ii,],
unique_id = rownames(pdata)[ii],
cluster=cl,
si=si_tmp[si_tmp[,1]==cl, 3])
})
si_out <- do.call(rbind, si_out)
si_pam[[i]] <- si_out
}
si_pam_long <- do.call(rbind, si_pam)</code></pre>
<p>Choose the top 25 in each cluster.</p>
<pre class="r"><code>cutoff_nsamples <- 20
foo <- lapply(1:uniqueN(si_pam_long$chip_id), function(i) {
foo2 <- lapply(1:uniqueN(si_pam_long$cluster), function(j) {
df_tmp <- subset(si_pam_long, chip_id == unique(si_pam_long$chip_id)[i] & cluster == unique(si_pam_long$cluster)[j])
if (cutoff_nsamples > dim(df_tmp)[1]) {
df_tmp_sub <- df_tmp
} else {
df_tmp_sub <- df_tmp[which(order(df_tmp$si, decreasing = T) %in% c(1:cutoff_nsamples)),]
}
return(df_tmp_sub)
})
foo2 <- do.call(rbind, foo2)
return(foo2)
})
foo <- do.call(rbind, foo)
si_pam_25 <- foo</code></pre>
<p>Compare full set versus subset of cells.</p>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-14-1.png" width="960" style="display: block; margin: auto;" /></p>
<p>hard code cluster labels. default in ggplot is 1 orange, 2 green, 3 blue</p>
<pre class="r"><code># NA18511
# orange 1 to 3, green 2 to 2, blue 3 to 1,
# NA18855
# orange 1 to 2, green 2 to 3, blue 3 to 1,
# NA18870
# orange 1 to 2, green 2 to 1, blue 3 to 3
# NA19098
# orange 1 to 1, green 2 to 3, blue 3 to 2
# NA19101
# orange 1 to 3, green 2 to 2, blue 3 to 1
# NA19160
# orange 1 to 2, green 2 to 3, blue 3 to 1
tmp <- si_pam_long
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==1,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==2,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18511" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA18855" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==2,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA18870" & si_pam_long$cluster==3,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==1,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19098" & si_pam_long$cluster==3,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==1,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==2,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19101" & si_pam_long$cluster==3,]$cluster <- 1
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==1,]$cluster <- 2
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==2,]$cluster <- 3
tmp[si_pam_long$chip_id == "NA19160" & si_pam_long$cluster==3,]$cluster <- 1
plot_grid(
ggplot(data=si_pam_long) +
geom_point(aes(x=gfp.median.log10sum.adjust.ash,
y=rfp.median.log10sum.adjust.ash, col=as.factor(cluster)),
alpha = .5, cex = .7) +
geom_point(data=pam_centers,
aes(x=gfp.median.log10sum.adjust.ash,
y=rfp.median.log10sum.adjust.ash), shape=4, size=3) +
labs(title = "K=3, PAM",
x="GFP intensity (log10 pixel sum)",
y = "RFP intensity (log10 pixel sum)") +
facet_wrap(~as.factor(chip_id), ncol=3) +
theme_gray() + theme(legend.position="none"),
ggplot(data=si_pam_25) +
geom_point(aes(x=gfp.median.log10sum.adjust.ash,
y=rfp.median.log10sum.adjust.ash, col=as.factor(cluster)),
alpha = .5, cex = .7) +
geom_point(data=pam_centers,
aes(x=gfp.median.log10sum.adjust.ash,
y=rfp.median.log10sum.adjust.ash), shape=4, size=3) +
labs(title = "Top 25 samples within clusters",
x="GFP intensity (log10 pixel sum)",
y = "RFP intensity (log10 pixel sum)") +
facet_wrap(~as.factor(chip_id), ncol=3) +
theme_gray() + theme(legend.position="none")
)</code></pre>
<p><img src="figure/images-subset-silhouette.Rmd/unnamed-chunk-15-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>save subset of cells to rds.</p>
<pre class="r"><code>save(si_pam_long, si_pam_25,
file = "../output/images-subset-silhouette.Rmd/si_pam.ash.rda")</code></pre>
<hr />
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre><code>R version 3.4.1 (2017-06-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Scientific Linux 7.2 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
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
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] tidyr_0.8.0 data.table_1.10.4-3 mclust_5.4
[4] cluster_2.0.6 cowplot_0.9.2 ggplot2_2.2.1
[7] Biobase_2.38.0 BiocGenerics_0.24.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.15 knitr_1.20 magrittr_1.5 munsell_0.4.3
[5] colorspace_1.3-2 rlang_0.2.0 stringr_1.3.0 plyr_1.8.4
[9] tools_3.4.1 grid_3.4.1 gtable_0.2.0 git2r_0.21.0
[13] htmltools_0.3.6 yaml_2.1.16 lazyeval_0.2.1 rprojroot_1.3-2
[17] digest_0.6.15 tibble_1.4.2 purrr_0.2.4 glue_1.2.0
[21] evaluate_0.10.1 rmarkdown_1.8 labeling_0.3 stringi_1.1.6
[25] compiler_3.4.1 pillar_1.1.0 scales_0.5.0 backports_1.1.2 </code></pre>
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