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<h1 class="title toc-ignore">CONFESS image classification: preliminary analysis</h1>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
shared across the workflowr files. -->
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<!-- Insert the date the file was last updated -->
<p><strong>Last updated:</strong> 2017-10-12</p>
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package git2r is installed -->
<p><strong>Code version:</strong> 8e822f0</p>
<div id="background-and-goals" class="section level2">
<h2>Background and goals</h2>
<p>Here we use <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/CONFESS/inst/doc/vignette.html#data-inspection-by-batch-chip">CONFESS</a> to perform FUCCI and DAPI image analysis. CONFESS is built on EBImage and has been previously used to quantify cell cycle phase for 200+ HeLa cells. Their results are described in a bioRxiv paper (<a href="http://dx.doi.org/10.1101/088500" class="uri">http://dx.doi.org/10.1101/088500</a>).</p>
<p>In this document, I report results for four different FUCCI plates (18855_18511, 18870_18855, 18870_19101, 19101_19098). Additional results are reported for 18855_18511 comparing analysis on two sets of images differing in crop sizes.</p>
<hr />
</div>
<div id="loading-data-and-packages" class="section level2">
<h2>Loading data and packages</h2>
<p>Load RDS.</p>
<pre class="r"><code>confess_18855_18511_crop1 <- readRDS(file = "../data/18855_18511_crop_09052017.rds")
confess_18855_18511_crop2 <- readRDS(file = "../data/18855_18511_crop_09072017.rds")
confess_18870_18855 <- readRDS(file = "../data/18870_18855_crop_09072017.rds")
confess_18870_19101 <- readRDS(file = "../data/18870_19101_crop_09122017.rds")
confess_19101_19098 <- readRDS(file = "../data/19101_19098_crop_09122017.rds")</code></pre>
<p>Functions for exploratory data analysis.</p>
<pre class="r"><code># make three plots
# 1. log2 foreground versus log2 background intensity for Red channel
# 2. log2 foreground versus log2 background intensity for Green channel
# 3. signal-to-noise ratio of green versus red
eda <- function(data, plot_title) {
with(data, {
xlim_red <- ylim_red <- range(c(log2(back_Red), log2(fore_Red)))
xlim_green <- ylim_green <- range(c(log2(back_Green), log2(fore_Green)))
par(mfrow = c(2,2))
plot(x = log2(back_Red), y = log2(fore_Red), pch = 16, cex = .7,
xlim = xlim_red, ylim = ylim_red); abline(0, 1)
plot(x = log2(back_Green), y = log2(fore_Green), pch = 16, cex = .7,
xlim = xlim_green, ylim = ylim_green); abline(0, 1)
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
plot(x = StN.green.norm, y = StN.red.norm,
pch = 16, cex = .7); abline(v=.5, h = .5)
title(main = plot_title, outer = TRUE, line = -1)
})
}</code></pre>
</div>
<div id="results" class="section level2">
<h2>Results</h2>
<p>In summary,</p>
<ol style="list-style-type: decimal">
<li>There’s little variation in the background intensity in either Green or Red channel images.</li>
<li>The intensity range for the Red channel is more narrow than the Green channel.</li>
<li>I computed signal-to-noise ratio by taking the background correction approach: substracting log2 background intensity from log2 foreground intensity. Then, for each channel, I normalized the signal-to-noise ratio by the range of the values.</li>
</ol>
<ul>
<li>18870_18855</li>
</ul>
<pre class="r"><code>eda(confess_18870_18855, "18870_18855")
with(confess_18870_18855, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
green_pos <- StN.green.norm > .5
red_pos <- StN.red.norm > .5
table(red_pos, green_pos)
})</code></pre>
<pre><code> green_pos
red_pos FALSE TRUE
FALSE 28 48
TRUE 0 20</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-3-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>18870_19101</li>
</ul>
<pre class="r"><code>eda(confess_18870_19101, "18870_19101")
with(confess_18870_19101, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
green_pos <- StN.green.norm > .5
red_pos <- StN.red.norm > .5
table(red_pos, green_pos)
})</code></pre>
<pre><code> green_pos
red_pos FALSE TRUE
FALSE 33 39
TRUE 0 24</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>19101_19098</li>
</ul>
<pre class="r"><code>eda(confess_19101_19098, "19101_19098")
with(confess_19101_19098, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
green_pos <- StN.green.norm > .5
red_pos <- StN.red.norm > .5
table(red_pos, green_pos)
})</code></pre>
<pre><code> green_pos
red_pos FALSE TRUE
FALSE 32 49
TRUE 0 15</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>18855_18511, crop1</li>
</ul>
<pre class="r"><code>eda(confess_18855_18511_crop1, "18855_18511, crop1")
with(confess_18855_18511_crop1, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
green_pos <- StN.green.norm > .5
red_pos <- StN.red.norm > .5
table(red_pos, green_pos)
})</code></pre>
<pre><code> green_pos
red_pos FALSE TRUE
FALSE 28 46
TRUE 1 21</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>18855_18511, crop2</li>
</ul>
<pre class="r"><code>eda(confess_18855_18511_crop2, "18855_18511, crop2")
with(confess_18855_18511_crop2, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
green_pos <- StN.green.norm > .5
red_pos <- StN.red.norm > .5
table(red_pos, green_pos)
})</code></pre>
<pre><code> green_pos
red_pos FALSE TRUE
FALSE 25 47
TRUE 2 22</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="exploratory-analysis" class="section level2">
<h2>Exploratory analysis</h2>
<div id="case-1" class="section level3">
<h3>Case 1</h3>
<p>Take 18870_18855. Let’s look at the log2fore_red verus log2back_red, which ones are very similar?</p>
<pre class="r"><code>with(confess_18870_18855, {
# xlim_red <- ylim_red <- range(c(log2(back_Red), log2(fore_Red)))
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
which_cell <- StN.green.norm < .5 & StN.red.norm < .2
xy <- cbind(log2(back_Red), log2(fore_Red))
# xy <- xy[which(abs(log2(fore_Red)-log2(back_Red)) < .05),]
xy <- xy[which_cell,]
par(mfrow = c(1,1))
plot(xy,
xlim = c(14.35,14.6), ylim = c(14.35,14.6),
xlab = "log2(Red background)",
ylab = "log2(Red foreground)",
pch = 16,
# pch = as.character(1:96)[which(log(back_Red) < 14.5)],
cex = .8); abline(0, 1)
})</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Consider the cells which foreground Red and Background Red are very similar. Of these 28 cells, about half have no DAPI signals and the ones with DAPI signal exhibit green signal.</p>
<pre class="r"><code>with(confess_18870_18855, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
print(which(StN.green.norm < .5 & StN.red.norm < .2))
})</code></pre>
<pre><code> [1] 14 16 20 21 22 28 30 33 35 39 41 43 44 46 48 49 54 58 60 62 63 64 68
[24] 71 72 74 75 95</code></pre>
<p>DAPI/Red/Green 14: Y/N/Y 16: N/N/N 20: ?/N/N 21: Y/N/Y 22: Y/?/Y 28: Y/?/Y 30: Y/N/Y 33: ?/N/Y 35: ?/N/? 39: ?/N/? 41: ?/N/? 43: N/N/? 44: N/?/N 46: Y/N/Y 48: N/N/N 49: N/N/? 54: Y/N/Y 58: Y/?/Y 60: Y/N/Y 62: N/N/N 63: Y/N/Y 64: Y/N/Y 68: N/N/? 71: N/N/? 72: Y/N/Y 74: ?/N/Y 75: Y/Y/Y 95: ?/N/?</p>
</div>
<div id="case-2" class="section level3">
<h3>Case 2</h3>
<p>Consider 18870_19101. Results are similar to 18870_18855.</p>
<pre class="r"><code>with(confess_18870_19101, {
xlim_red <- ylim_red <- range(c(log2(back_Red), log2(fore_Red)))
xlim_green <- ylim_green <- range(c(log2(back_Green), log2(fore_Green)))
par(mfrow = c(2,2))
plot(x = log2(back_Red), y = log2(fore_Red),
xlim = xlim_red, ylim = ylim_red); abline(0, 1)
plot(x = log2(back_Green), y = log2(fore_Green),
xlim = xlim_green, ylim = ylim_green); abline(0, 1)
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
plot(x = StN.green.norm,
y = StN.red.norm); abline(v=.5, h = .5)
})</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Look at the log2fore_red verus log2back_red, which ones are very similar?</p>
<pre class="r"><code>with(confess_18870_19101, {
# xlim_red <- ylim_red <- range(c(log2(back_Red), log2(fore_Red)))
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
which_cell <- StN.green.norm < .3 & StN.red.norm < .3
xy <- cbind(log2(back_Red), log2(fore_Red))
# xy <- xy[which(abs(log2(fore_Red)-log2(back_Red)) < .05),]
xy <- xy[which_cell,]
par(mfrow = c(1,1))
plot(xy,
xlim = c(14.28,14.6), ylim = c(14.28,14.6),
xlab = "log2(Red background)",
ylab = "log2(Red foreground)",
pch = 16,
# pch = as.character(1:96)[which(log(back_Red) < 14.5)],
cex = .8); abline(0, 1)
})</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Consider the cells which foreground Red and Background Red are very similar. Of these 28 cells, about half have no DAPI signals and the ones with DAPI signal exhibit green signal.</p>
<pre class="r"><code>with(confess_18870_19101, {
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
print(which(StN.green.norm < .3 & StN.red.norm < .3))
})</code></pre>
<pre><code> [1] 2 9 11 12 16 18 21 23 28 30 34 35 36 37 38 45 47 55 61 66 72 73 75
[24] 76 78 87</code></pre>
</div>
<div id="digging-in-confess" class="section level3">
<h3>Digging in CONFESS</h3>
<p>Observe that 19 out of 28 cells that are called as both Green and Red negative were estimated using BF method. There are other two methods: Both.Channels and One.channel. What’s the difference between these? TBD.</p>
<pre class="r"><code>with(confess_18870_18855, {
xlim_red <- ylim_red <- range(c(log2(back_Red), log2(fore_Red)))
xlim_green <- ylim_green <- range(c(log2(back_Green), log2(fore_Green)))
par(mfrow = c(2,2))
plot(x = log2(back_Red), y = log2(fore_Red),
xlim = xlim_red, ylim = ylim_red); abline(0, 1)
plot(x = log2(back_Green), y = log2(fore_Green),
xlim = xlim_green, ylim = ylim_green); abline(0, 1)
StN.red <- log2(fore_Red) - min(log2(back_Red))
StN.green <- log2(fore_Green) - min(log2(back_Green))
StN.red.norm <- (StN.red-min(StN.red))/(max(StN.red)-min(StN.red))
StN.green.norm <- (StN.green-min(StN.green))/(max(StN.green)-min(StN.green))
plot(x = StN.green.norm,
y = StN.red.norm); abline(v=.5, h = .5)
which_cell <- StN.green.norm < .5 & StN.red.norm < .5
print(table(which_cell, Estimation.Type))
print(which(which_cell))
})</code></pre>
<pre><code> Estimation.Type
which_cell BF Both.Channels One.Channel
FALSE 1 46 21
TRUE 19 4 5
[1] 14 16 20 21 22 28 30 33 35 39 41 43 44 46 48 49 54 58 60 62 63 64 68
[24] 71 72 74 75 95</code></pre>
<p><img src="figure/confess-prelim.Rmd/unnamed-chunk-13-1.png" width="672" style="display: block; margin: auto;" /></p>
<hr />
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.04
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.7.0
LAPACK: /usr/lib/lapack/liblapack.so.3.7.0
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.2 backports_1.1.1 magrittr_1.5 rprojroot_1.2
[5] tools_3.4.2 htmltools_0.3.6 yaml_2.1.14 Rcpp_0.12.11
[9] stringi_1.1.5 rmarkdown_1.6 knitr_1.17 git2r_0.19.0
[13] stringr_1.2.0 digest_0.6.12 evaluate_0.10.1</code></pre>
</div>
<div id="session-information-1" class="section level2">
<h2>Session information</h2>
<!-- Insert the session information into the document -->
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.04
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.7.0
LAPACK: /usr/lib/lapack/liblapack.so.3.7.0
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.2 backports_1.1.1 magrittr_1.5 rprojroot_1.2
[5] tools_3.4.2 htmltools_0.3.6 yaml_2.1.14 Rcpp_0.12.11
[9] stringi_1.1.5 rmarkdown_1.6 knitr_1.17 git2r_0.19.0
[13] stringr_1.2.0 digest_0.6.12 evaluate_0.10.1</code></pre>
</div>
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