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<h1 class="title toc-ignore">Normalize intensities across batches and positions</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> b299bc0</p>
<hr />
<div id="introductionsummary" class="section level2">
<h2>Introduction/summary</h2>
<p>In notations,</p>
<p><span class="math display">\[
y_{ij} = \mu + \tau_i + \beta_j + \gamma_k + \epsilon_{ij}
\]</span> where <span class="math inline">\(i = 1,2,..., I\)</span> and <span class="math inline">\(j = 1,2,..., J\)</span>. The parameters are estimated under sum-to-zero constraints <span class="math inline">\(\sum \tau_i = 0\)</span> and <span class="math inline">\(\sum \beta_j = 0\)</span>.</p>
<p>Note that in this model 1) not all <span class="math inline">\(y_{ij.}\)</span> exists due to the incompleteness of the design, 2) the effects of individual and block are nonorthogonal, 3) the effects are additive due to the block design.</p>
<p><strong>TO DO: Apply batch correction prior to background correction??</strong></p>
<hr />
</div>
<div id="data-and-packages" class="section level2">
<h2>Data and packages</h2>
<p><span class="math inline">\(~\)</span></p>
<pre class="r"><code>library(data.table)
library(dplyr)
library(ggplot2)
library(cowplot)
library(RColorBrewer)
library(Biobase)
library(scales)
library(car)
library(ashr)
library(lsmeans)</code></pre>
<p>Read in filtered data.</p>
<pre class="r"><code>df <- readRDS(file="../data/eset-filtered.rds")
pdata <- pData(df)
fdata <- fData(df)</code></pre>
<hr />
</div>
<div id="source-of-variation" class="section level2">
<h2>Source of variation</h2>
<p>Statistical tests show that for GFP, there’s significant individual effect, plate effect and position effect, and that for RFP and DAPI, there’s no signficant individual effect or position effect but there’s significant plate effect (all at P<.01).</p>
<pre class="r"><code>lm.rfp <- lm(rfp.median.log10sum~factor(chip_id)+factor(experiment) + factor(image_label),
data = pdata)
lm.gfp <- lm(gfp.median.log10sum~factor(chip_id)+factor(experiment) + factor(image_label),
data = pdata)
lm.dapi <- lm(dapi.median.log10sum~factor(chip_id)+factor(experiment) + factor(image_label),
data = pdata)
aov.lm.rfp <- Anova(lm.rfp, type = "III")
aov.lm.gfp <- Anova(lm.gfp, type = "III")
aov.lm.dapi <- Anova(lm.dapi, type = "III")
aov.lm.rfp</code></pre>
<pre><code>Anova Table (Type III tests)
Response: rfp.median.log10sum
Sum Sq Df F value Pr(>F)
(Intercept) 44.799 1 193.7809 < 2.2e-16 ***
factor(chip_id) 2.061 5 1.7830 0.113731
factor(experiment) 8.836 15 2.5480 0.001004 **
factor(image_label) 28.830 95 1.3127 0.029625 *
Residuals 202.056 874
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>aov.lm.gfp</code></pre>
<pre><code>Anova Table (Type III tests)
Response: gfp.median.log10sum
Sum Sq Df F value Pr(>F)
(Intercept) 60.082 1 569.5986 < 2.2e-16 ***
factor(chip_id) 1.608 5 3.0492 0.009779 **
factor(experiment) 12.174 15 7.6944 2.293e-16 ***
factor(image_label) 14.688 95 1.4658 0.003756 **
Residuals 92.191 874
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>aov.lm.dapi</code></pre>
<pre><code>Anova Table (Type III tests)
Response: dapi.median.log10sum
Sum Sq Df F value Pr(>F)
(Intercept) 57.257 1 1474.3536 < 2e-16 ***
factor(chip_id) 0.568 5 2.9233 0.01262 *
factor(experiment) 12.118 15 20.8019 < 2e-16 ***
factor(image_label) 3.333 95 0.9035 0.73028
Residuals 33.942 874
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<p>Indivdual and plate variation</p>
<p><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-4-1.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-4-2.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-4-3.png" width="1152" style="display: block; margin: auto;" /></p>
<p>Position variation</p>
<pre class="r"><code>well.gfp.median <- pdata %>% group_by(image_label) %>% summarize(., median(gfp.median.log10sum))
well.rfp.median <- pdata %>% group_by(image_label) %>% summarize(., median(rfp.median.log10sum))
well.dapi.median <- pdata %>% group_by(image_label) %>% summarize(., median(dapi.median.log10sum))
well.pp <- data.frame(well=pdata$well, image_label=pdata$image_label)
well.pp <- well.pp[!duplicated(well.pp),]
colbrew <- brewer.pal(9, "Set1")
well.pp$cols <- rep(colbrew[9], 96)
well.pp$cols[which(well.pp$well %in% c("A03", "A02", "A01", "A09", "A08", "A07"))] <- colbrew[1]
well.pp$cols[which(well.pp$well %in% c("H03", "H02", "H01", "H09", "H08", "H07"))] <- colbrew[2]
well.pp$cols[which(well.pp$well %in% c("A06", "A05", "A04", "A12", "A11", "A10"))] <- colbrew[3]
well.pp$cols[which(well.pp$well %in% c("H06", "H05", "H04", "H12", "H11", "H10"))] <- colbrew[4]
well.pp <- well.pp[order(well.pp$image_label),]
ord.gfp <- as.character(well.gfp.median$image_label[order(well.gfp.median$`median(gfp.median.log10sum)`)])
ord.rfp <- as.character(well.rfp.median$image_label[order(well.rfp.median$`median(rfp.median.log10sum)`)])
ord.dapi <- as.character(well.dapi.median$image_label[order(well.dapi.median$`median(dapi.median.log10sum)`)])</code></pre>
<p>These are four corners previously found more likely to have high gene expression values in sequencing data.</p>
<pre class="r"><code>par(mfrow=c(1,1))
plot(1:7, 1:7, pch="", axes=F, ann=F)
legend("center", legend = c("A_a", "H_a", "A_b", "H_b"), col=colbrew[c(1,2,3,4)],
pch=16)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfrow=c(3,1))
boxplot(rfp.median.log10sum ~ factor(image_label, levels=ord.rfp),
data=pdata, ylab = "RFP",
col=well.pp$cols[as.numeric(ord.rfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(gfp.median.log10sum ~ factor(image_label, levels=ord.gfp),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.gfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(dapi.median.log10sum ~ factor(image_label, levels=ord.dapi),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.dapi)])
abline(h=0, lwd=2, col="royalblue")
title("Position variation", outer=TRUE, line = -1)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-7-1.png" width="960" style="display: block; margin: auto;" /></p>
<hr />
</div>
<div id="estimate-effects" class="section level2">
<h2>Estimate effects</h2>
<p>Contrast test to estimate effects for for plate and position ID.</p>
<pre class="r"><code># make contrast matrix for plates
# each plate is compared to the average
n_plates <- uniqueN(pdata$experiment)
contrast_plates <- matrix(-1, nrow=n_plates, ncol=n_plates)
diag(contrast_plates) <- n_plates-1
# make contrast matrix for individuals
# each individual is compared to the average
n_pos <- uniqueN(pdata$image_label)
contrast_pos <- matrix(-1, nrow=n_pos, ncol=n_pos)
diag(contrast_pos) <- n_pos-1</code></pre>
<pre class="r"><code>gfp.plates <- summary(lsmeans(lm.gfp, specs="experiment", contrast=contrast_plates))
gfp.pos <- summary(lsmeans(lm.gfp, specs="image_label", contrast=contrast_pos))
rfp.plates <- summary(lsmeans(lm.rfp, specs="experiment", contrast=contrast_plates))
rfp.pos <- summary(lsmeans(lm.rfp, specs="image_label", contrast=contrast_pos))
dapi.plates <- summary(lsmeans(lm.dapi, specs="experiment", contrast=contrast_plates))
dapi.pos <- summary(lsmeans(lm.dapi, specs="image_label", contrast=contrast_pos))</code></pre>
<p>Substract plate effect from the raw estimates.</p>
<pre class="r"><code>## RFP
pdata$rfp.median.log10sum.adjust <- pdata$rfp.median.log10sum
rfp.plates$experiment <- as.character(rfp.plates$experiment)
rfp.pos$experiment <- as.character(rfp.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- rfp.plates$lsmean[which(rfp.plates$experiment==exp)]
pdata$rfp.median.log10sum.adjust[ii_exp] <- (pdata$rfp.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- rfp.pos$lsmean[which(rfp.pos$image_label==p)]
pdata$rfp.median.log10sum.adjust[ii_pos] <- (pdata$rfp.median.log10sum[ii_pos] - est_pos)
}
## GFP
pdata$gfp.median.log10sum.adjust <- pdata$gfp.median.log10sum
gfp.plates$experiment <- as.character(gfp.plates$experiment)
gfp.pos$experiment <- as.character(gfp.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- gfp.plates$lsmean[which(gfp.plates$experiment==exp)]
pdata$gfp.median.log10sum.adjust[ii_exp] <- (pdata$gfp.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- gfp.pos$lsmean[which(gfp.pos$image_label==p)]
pdata$gfp.median.log10sum.adjust[ii_pos] <- (pdata$gfp.median.log10sum[ii_pos] - est_pos)
}
## DAPI
pdata$dapi.median.log10sum.adjust <- pdata$dapi.median.log10sum
dapi.plates$experiment <- as.character(dapi.plates$experiment)
dapi.pos$experiment <- as.character(dapi.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- dapi.plates$lsmean[which(dapi.plates$experiment==exp)]
pdata$dapi.median.log10sum.adjust[ii_exp] <- (pdata$dapi.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- dapi.pos$lsmean[which(dapi.pos$image_label==p)]
pdata$dapi.median.log10sum.adjust[ii_pos] <- (pdata$dapi.median.log10sum[ii_pos] - est_pos)
}</code></pre>
<p>After adjustment</p>
<p><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-1.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-2.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-3.png" width="1152" style="display: block; margin: auto;" /></p>
<pre class="r"><code>## These are four corners previously found more likely to have high gene expression values in sequencing data.
par(mfrow=c(1,1))
plot(1:7, 1:7, pch="", axes=F, ann=F)
legend("center", legend = c("A_a", "H_a", "A_b", "H_b"), col=colbrew[c(1,2,3,4)],
pch=16)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfrow=c(3,1))
boxplot(rfp.median.log10sum.adjust ~ factor(image_label, levels=ord.rfp),
data=pdata, ylab = "RFP",
col=well.pp$cols[as.numeric(ord.rfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(gfp.median.log10sum.adjust ~ factor(image_label, levels=ord.gfp),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.gfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(dapi.median.log10sum.adjust ~ factor(image_label, levels=ord.dapi),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.dapi)])
abline(h=0, lwd=2, col="royalblue")
title("Position variation", outer=TRUE, line = -1)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/plot-position-adjusted-1.png" width="960" style="display: block; margin: auto;" /></p>
<hr />
</div>
<div id="output-results" class="section level2">
<h2>Output results</h2>
<p>Save corrected data to a temporary output folder.</p>
<pre class="r"><code>saveRDS(pdata, file = "../output/images-normalize-anova.Rmd/pdata.adj.rds")</code></pre>
<hr />
</div>
<div id="ash" class="section level2">
<h2>ash</h2>
<p>apply shrinkage to position estimates</p>
<pre class="r"><code># # apply limma ebayes to shrink variances
# library(limma)
# gfp.pos.var <- squeezeVar(gfp.pos$SE^2, df = gfp.pos$df)$var.post
# gfp.pos.df <- squeezeVar(gfp.pos$SE^2, df = gfp.pos$df)$df.prior + gfp.pos$df
gfp.pos.ash <- ash(gfp.pos$lsmean, gfp.pos$SE, mixcompdist = "uniform",
lik = lik_t(df=gfp.pos$df[1]), mode = "estimate" )
# gfp.pos.ash.varpost <- ash(gfp.pos$lsmean, gfp.pos.var, mixcompdist = "uniform",
# lik = lik_t(df=gfp.pos.df), mode = "estimate" )
# rfp.pos.var <- squeezeVar(rfp.pos$SE^2, df = rfp.pos$df)$var.post
# rfp.pos.df <- squeezeVar(rfp.pos$SE^2, df = rfp.pos$df)$df.prior + gfp.pos$df
rfp.pos.ash <- ash(rfp.pos$lsmean, rfp.pos$SE, mixcompdist = "uniform",
lik = lik_t(df=rfp.pos$df[1]), mode = "estimate" )
# rfp.pos.ash.varpost <- ash(rfp.pos$lsmean, rfp.pos.var, mixcompdist = "uniform",
# lik = lik_t(df=rfp.pos.df), mode = "estimate" )
# dapi.pos.var <- squeezeVar(dapi.pos$SE^2, df = dapi.pos$df)$var.post
# dapi.pos.df <- squeezeVar(dapi.pos$SE^2, df = dapi.pos$df)$df.prior + gfp.pos$df
dapi.pos.ash <- ash(dapi.pos$lsmean, dapi.pos$SE, mixcompdist = "uniform",
lik = lik_t(df=dapi.pos$df[1]), mode = "estimate" )
# dapi.pos.ash.varpost <- ash(dapi.pos$lsmean, dapi.pos.var, mixcompdist = "uniform",
# lik = lik_t(df=dapi.pos.df), mode = "estimate" )
#
par(mfrow=c(2,2))
plot(gfp.pos.ash$result$betahat, gfp.pos.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "GFP")
abline(0,1, col = "royalblue")
plot(rfp.pos.ash$result$betahat, rfp.pos.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "RFP")
abline(0,1, col = "royalblue")
plot(dapi.pos.ash$result$betahat, dapi.pos.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "DAPI")
abline(0,1, col = "royalblue")
par(mfrow=c(2,2))</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/ash-position-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(gfp.pos.ash$result$sebetahat, gfp.pos.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "GFP")
abline(0,1, col = "royalblue")
plot(rfp.pos.ash$result$sebetahat, rfp.pos.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "RFP")
abline(0,1, col = "royalblue")
plot(dapi.pos.ash$result$sebetahat, dapi.pos.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "DAPI")
abline(0,1, col = "royalblue")</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/ash-position-2.png" width="672" style="display: block; margin: auto;" /></p>
<p>Plate effect.</p>
<pre class="r"><code>library(ashr)
gfp.plates.ash <- ash(gfp.plates$lsmean, gfp.plates$SE, mixcompdist = "uniform",
lik = lik_t(df=gfp.plates$df[1]), mode = "estimate")
rfp.plates.ash <- ash(rfp.plates$lsmean, rfp.plates$SE, mixcompdist = "uniform",
lik = lik_t(df=rfp.plates$df[1]), mode = "estimate")
dapi.plates.ash <- ash(dapi.plates$lsmean, dapi.plates$SE, mixcompdist = "uniform",
lik = lik_t(df=dapi.plates$df[1]), mode = "estimate")
par(mfrow=c(2,2))
plot(gfp.plates.ash$result$betahat, gfp.plates.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "GFP")
abline(0,1, col = "royalblue")
plot(rfp.plates.ash$result$betahat, rfp.plates.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "RFP")
abline(0,1, col = "royalblue")
plot(dapi.plates.ash$result$betahat, dapi.plates.ash$result$PosteriorMean,
xlab = "beta hat", ylab = "Shrunken estimate", main = "DAPI")
abline(0,1, col = "royalblue")
par(mfrow=c(2,2))</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/ash-plate-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot(gfp.plates.ash$result$sebetahat, gfp.plates.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "GFP")
abline(0,1, col = "royalblue")
plot(rfp.plates.ash$result$sebetahat, rfp.plates.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "RFP")
abline(0,1, col = "royalblue")
plot(dapi.plates.ash$result$sebetahat, dapi.plates.ash$result$PosteriorSD,
xlab = "Standard Error", ylab = "Shrunken estimate", main = "DAPI")
abline(0,1, col = "royalblue")</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/ash-plate-2.png" width="672" style="display: block; margin: auto;" /></p>
<p>Substract plate effect from the raw estimates.</p>
<pre class="r"><code>## RFP
pdata$rfp.median.log10sum.adjust.ash <- pdata$rfp.median.log10sum
rfp.plates$experiment <- as.character(rfp.plates$experiment)
rfp.pos$experiment <- as.character(rfp.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- rfp.plates.ash$result$PosteriorMean[which(rfp.plates$experiment==exp)]
pdata$rfp.median.log10sum.adjust.ash[ii_exp] <- (pdata$rfp.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- rfp.pos.ash$result$PosteriorMean[which(rfp.pos$image_label==p)]
pdata$rfp.median.log10sum.adjust.ash[ii_pos] <- (pdata$rfp.median.log10sum[ii_pos] - est_pos)
}
## GFP
pdata$gfp.median.log10sum.adjust.ash <- pdata$gfp.median.log10sum
gfp.plates$experiment <- as.character(gfp.plates$experiment)
gfp.pos$experiment <- as.character(gfp.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- gfp.plates.ash$result$PosteriorMean[which(gfp.plates$experiment==exp)]
pdata$gfp.median.log10sum.adjust.ash[ii_exp] <- (pdata$gfp.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- gfp.pos.ash$result$PosteriorMean[which(gfp.pos$image_label==p)]
pdata$gfp.median.log10sum.adjust.ash[ii_pos] <- (pdata$gfp.median.log10sum[ii_pos] - est_pos)
}
## DAPI
pdata$dapi.median.log10sum.adjust.ash <- pdata$dapi.median.log10sum
dapi.plates$experiment <- as.character(dapi.plates$experiment)
dapi.pos$experiment <- as.character(dapi.pos$image_label)
pdata$experiment <- as.character(pdata$experiment)
exps <- unique(pdata$experiment)
for (i in 1:uniqueN(exps)) {
exp <- exps[i]
ii_exp <- which(pdata$experiment == exp)
est_exp <- dapi.plates.ash$result$PosteriorMean[which(dapi.plates$experiment==exp)]
pdata$dapi.median.log10sum.adjust.ash[ii_exp] <- (pdata$dapi.median.log10sum[ii_exp] - est_exp)
}
pos <- unique(pdata$image_label)
for (i in 1:uniqueN(pos)) {
p <- pos[i]
ii_pos <- which(pdata$image_label == p)
est_pos <- dapi.pos.ash$result$PosteriorMean[which(dapi.pos$image_label==p)]
pdata$dapi.median.log10sum.adjust.ash[ii_pos] <- (pdata$dapi.median.log10sum[ii_pos] - est_pos)
}</code></pre>
<p>After adjustment</p>
<p><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-ash-1.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-ash-2.png" width="1152" style="display: block; margin: auto;" /><img src="figure/images-normalize-anova.Rmd/boxplot-adjusted-ash-3.png" width="1152" style="display: block; margin: auto;" /></p>
<pre class="r"><code>## These are four corners previously found more likely to have high gene expression values in sequencing data.
par(mfrow=c(1,1))
plot(1:7, 1:7, pch="", axes=F, ann=F)
legend("center", legend = c("A_a", "H_a", "A_b", "H_b"), col=colbrew[c(1,2,3,4)],
pch=16)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>par(mfrow=c(3,1))
boxplot(rfp.median.log10sum.adjust.ash ~ factor(image_label, levels=ord.rfp),
data=pdata, ylab = "RFP",
col=well.pp$cols[as.numeric(ord.rfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(gfp.median.log10sum.adjust.ash ~ factor(image_label, levels=ord.gfp),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.gfp)])
abline(h=0, lwd=2, col="royalblue")
boxplot(dapi.median.log10sum.adjust.ash ~ factor(image_label, levels=ord.dapi),
data=pdata, ylab = "GFP",
col=well.pp$cols[as.numeric(ord.dapi)])
abline(h=0, lwd=2, col="royalblue")
title("Position variation", outer=TRUE, line = -1)</code></pre>
<p><img src="figure/images-normalize-anova.Rmd/plot-position-adjusted-ash-1.png" width="960" style="display: block; margin: auto;" /></p>
<hr />
</div>
<div id="output-results-1" class="section level2">
<h2>Output results</h2>
<p>Save corrected data to a temporary output folder.</p>
<pre class="r"><code>saveRDS(pdata, file = "../output/images-normalize-anova.Rmd/pdata.adj.rds")</code></pre>
<hr />
</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] bindrcpp_0.2 lsmeans_2.27-61 ashr_2.2-4
[4] car_2.1-6 scales_0.5.0 Biobase_2.38.0
[7] BiocGenerics_0.24.0 RColorBrewer_1.1-2 cowplot_0.9.2
[10] ggplot2_2.2.1 dplyr_0.7.4 data.table_1.10.4-3
loaded via a namespace (and not attached):
[1] Rcpp_0.12.15 mvtnorm_1.0-7 lattice_0.20-35
[4] Rmosek_7.1.3 zoo_1.8-1 assertthat_0.2.0
[7] rprojroot_1.3-2 digest_0.6.15 foreach_1.4.4
[10] truncnorm_1.0-7 R6_2.2.2 plyr_1.8.4
[13] backports_1.1.2 MatrixModels_0.4-1 evaluate_0.10.1
[16] coda_0.19-1 pillar_1.1.0 rlang_0.2.0
[19] lazyeval_0.2.1 pscl_1.5.2 multcomp_1.4-8
[22] minqa_1.2.4 SparseM_1.77 nloptr_1.0.4
[25] Matrix_1.2-10 rmarkdown_1.8 labeling_0.3
[28] splines_3.4.1 lme4_1.1-15 stringr_1.3.0
[31] REBayes_1.3 munsell_0.4.3 compiler_3.4.1
[34] pkgconfig_2.0.1 etrunct_0.1 SQUAREM_2017.10-1
[37] mgcv_1.8-17 htmltools_0.3.6 nnet_7.3-12
[40] tibble_1.4.2 codetools_0.2-15 MASS_7.3-47
[43] grid_3.4.1 nlme_3.1-131 xtable_1.8-2
[46] gtable_0.2.0 git2r_0.21.0 magrittr_1.5
[49] estimability_1.3 stringi_1.1.6 doParallel_1.0.11
[52] sandwich_2.4-0 TH.data_1.0-8 iterators_1.0.9
[55] tools_3.4.1 glue_1.2.0 pbkrtest_0.4-7
[58] survival_2.41-3 yaml_2.1.16 colorspace_1.3-2
[61] knitr_1.20 bindr_0.1 quantreg_5.35 </code></pre>
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