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<h1 class="title toc-ignore">Intensities by individuals</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
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<p><strong>Last updated:</strong> 2017-11-29</p>
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<p><strong>Code version:</strong> ee0de70</p>
<hr />
<div id="summary" class="section level2">
<h2>Summary</h2>
<p>Previously, we saw a wide range of variability across plates in DAPI intensities, and to a lesser extent, in Green and Red intensities. Here we look at variation between individuals and see that there’s significantly smaller variation between individuals in all three measurements. In addition, in the across-plate results, the DAPI distributions have similar shape across plates and a possible mean-shift between distributions. While, the shape of the green/red distributions are not consistent across plates, possibly reflecting differences in proprotion of samples expressing red/green fluorescent proteins.</p>
<p>For normalization approach, we can use mean correction approaches for DAPI. While, for Green/Red, it’s less obvious what would be a good approach. Let me try <code>qsmooth</code> and see….</p>
</div>
<div id="load-packages-and-data" class="section level2">
<h2>Load packages and data</h2>
<pre class="r"><code>library(data.table)
library(dplyr)
library(ggplot2)
library(cowplot)
library(wesanderson)
library(RColorBrewer)
library(Biobase)</code></pre>
<p>Name all plates.</p>
<pre class="r"><code>plates <- c("18511_18855","18855_19101","18855_19160","18870_18511",
"18870_18855","18870_19101","18870_19160","19098_18511",
"19098_18870","19098_19160","19101_18511","19101_19098",
"19160_18870","19101_19160","19160_18511", "18855_19098")</code></pre>
<p>Combine intensity stats from different plates.</p>
<pre class="r"><code># make the negative ones be the samllest one within its own plate
ints <- do.call(rbind, lapply(1:length(plates), function(index) {
tmp <- readRDS(paste0("/project2/gilad/fucci-seq/intensities_stats/",plates[index],".stats.rds"))
tmp <- data.frame(plate=plates[index],
well=as.character(droplevels(tmp$wellID)),
rfp.sum.zoom.mean.log10=log10(tmp$rfp.sum.zoom.mean),
gfp.sum.zoom.mean.log10=log10(tmp$gfp.sum.zoom.mean),
dapi.sum.zoom.mean.log10=log10(tmp$dapi.sum.zoom.mean))
tmp$rfp.sum.zoom.mean.log10[which(tmp$rfp.sum.zoom.mean.log10 == "NaN")] <- min(tmp$rfp.sum.zoom.mean.log10, na.rm=TRUE)
tmp$gfp.sum.zoom.mean.log10[which(tmp$gfp.sum.zoom.mean.log10 == "NaN")] <- min(tmp$gfp.sum.zoom.mean.log10, na.rm=TRUE)
tmp$dapi.sum.zoom.mean.log10[which(tmp$dapi.sum.zoom.mean.log10 == "NaN")] <- min(tmp$dapi.sum.zoom.mean.log10, na.rm=TRUE)
return(tmp)
}) )</code></pre>
<pre><code>Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced
Warning in data.frame(plate = plates[index], well =
as.character(droplevels(tmp$wellID)), : NaNs produced</code></pre>
<pre class="r"><code>ints <- ints %>% mutate(dapi_4quant=ntile(dapi.sum.zoom.mean.log10,4),
dapi_3quant=ntile(dapi.sum.zoom.mean.log10,3))
saveRDS(ints, file = "/project2/gilad/joycehsiao/fucci-seq/output/ints.long.rds")</code></pre>
<p>Load the expression set info.</p>
<pre class="r"><code>eset_fls <- list.files("../data/eset", full.names=TRUE)
anno <- do.call(rbind, lapply(1:length(eset_fls), function(index) {
eset_index <- readRDS(eset_fls[[index]])
pdata_index <- pData(eset_index)
return(pdata_index)
}))
# make unique id in both
ints$unique <- paste0(ints$plate,"_",as.numeric(ints$well))
anno$unique <- paste0(anno$image_individual,"_",anno$image_label)
subset_index1 <- which(anno$unique %in% ints$unique)
anno_subset <- anno[subset_index1,]
subset_index2 <- match(anno_subset$unique, ints$unique)
ints_tmp <- ints[subset_index2,]
all.equal(ints_tmp$unique, anno_subset$unique)</code></pre>
<pre><code>[1] TRUE</code></pre>
<pre class="r"><code>ints_tmp$chip_id <- anno_subset$chip_id
# compute plate specific DAPI quantiles
ints_tmp2 <- ints_tmp %>% group_by(plate) %>% mutate(dapi_4quant_plate=ntile(dapi.sum.zoom.mean.log10,4),
dapi_3quant_plate=ntile(dapi.sum.zoom.mean.log10,3))</code></pre>
</div>
<div id="by-plate" class="section level2">
<h2>By plate</h2>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=gfp.sum.zoom.mean.log10,col = as.factor(plate))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "Green (log10 pixel sum) by plate",
x="Green channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-5-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=rfp.sum.zoom.mean.log10,col = as.factor(plate))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "Red (log10 pixel sum) by plate",
x="Red channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-5-2.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=dapi.sum.zoom.mean.log10,col = as.factor(plate))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "DAPI (log10 pixel sum) by plate",
x="DAPI channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-5-3.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="by-individual" class="section level2">
<h2>By individual</h2>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=gfp.sum.zoom.mean.log10,col = as.factor(chip_id))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "Green (log10 pixel sum) by individual",
x="Green channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=rfp.sum.zoom.mean.log10,col = as.factor(chip_id))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "Red (log10 pixel sum) by individual",
x="Red channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-6-2.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(ints_tmp2, aes(x=dapi.sum.zoom.mean.log10,col = as.factor(chip_id))) +
geom_density(alpha = .5, cex = .7) +
labs(title = "DAPI (log10 pixel sum) by individual",
x="DAPI channel log10 pixel sum", y = "Density") + theme_gray() </code></pre>
<p><img src="figure/images-qc-labels.Rmd/unnamed-chunk-6-3.png" width="672" style="display: block; margin: auto;" /></p>
<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] Biobase_2.38.0 BiocGenerics_0.24.0 RColorBrewer_1.1-2
[4] wesanderson_0.3.4 cowplot_0.9.1 ggplot2_2.2.1
[7] dplyr_0.7.4 data.table_1.10.4-3
loaded via a namespace (and not attached):
[1] Rcpp_0.12.14 knitr_1.17 bindr_0.1 magrittr_1.5
[5] munsell_0.4.3 colorspace_1.3-2 R6_2.2.2 rlang_0.1.4
[9] plyr_1.8.4 stringr_1.2.0 tools_3.4.1 grid_3.4.1
[13] gtable_0.2.0 git2r_0.19.0 htmltools_0.3.6 lazyeval_0.2.1
[17] yaml_2.1.14 rprojroot_1.2 digest_0.6.12 assertthat_0.2.0
[21] tibble_1.3.4 bindrcpp_0.2 glue_1.2.0 evaluate_0.10.1
[25] rmarkdown_1.8 labeling_0.3 stringi_1.1.6 compiler_3.4.1
[29] scales_0.5.0 backports_1.1.1 pkgconfig_2.0.1 </code></pre>
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