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<h1 class="title toc-ignore">parametric cellcycleR for image intensities</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> 2017-12-21</p>
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<p><strong>Code version:</strong> 9c7a72f</p>
<hr />
<div id="introductionsummary" class="section level2">
<h2>Introduction/Summary</h2>
<p><span class="math inline">\(~\)</span></p>
<p>In this document, we assess the fit of <code>cellcycleR</code> across all samples and all batches. The idea is to assess the deviations from model fit for each individual and each batch. From the structure of deviation from the model fit, we can learn major sources of variation and develop strategies for removing unwanted variation accordingly.</p>
<p><span class="math inline">\(~\)</span></p>
<div id="data-preparation" class="section level3">
<h3>Data Preparation</h3>
<p><span class="math inline">\(~\)</span></p>
<p><strong>Step 1: Correct for backgorund noise</strong>:</p>
<p>We correct for background intensity by substracting from each pixel the background median intensity. Then we sum up the background-corrected intensities and then take log transformation of the sum. log transformation is frequently used in image transformation for increasing contrast of the image, especially for regions with low pixel intensity.</p>
<p><span class="math inline">\(F\)</span>: Pixel intensity matrix for in each FUCCI image.</p>
<p><span class="math inline">\(F^{f}\)</span>: Fluorescent intensity values located inside the defined forground area. Set to be <span class="math inline">\(100 \times 100\)</span> area centered at the nucleus.</p>
<p><span class="math inline">\(F^{b}\)</span>: Fluorescent intensity values for pixels in the background area.</p>
<p><span class="math inline">\(N^f\)</span> and <span class="math inline">\(N^b\)</span>: Mumber of pixels located in the foreground and background area, respectively.</p>
<p><span class="math display">\[
I = log10 \sum_{pixels \,\,\in \,\,foreground} \big( F^{f} - median (F^{b}) \big)
\]</span></p>
<p><span class="math inline">\(~\)</span></p>
<p><strong>Step 2: Standardize background-corrected intensities</strong>:</p>
<p>For GFP, RFP, and DAPI separatley, standardize background-corrected fluorescent intensity measurements as follows.</p>
<p><span class="math inline">\(I_{nj}\)</span>: Background-correctd intensity values in single cell sample <span class="math inline">\(n\)</span> and batch <span class="math inline">\(j\)</span>.</p>
<p><span class="math inline">\(\bar{I}\)</span>: Mean background-correctd intensity values across sample <span class="math inline">\(n=1,\dots, N\)</span> and batches <span class="math inline">\(j=1,\dots,J\)</span>.</p>
<p><span class="math inline">\(S\)</span>: Standard deviation of background-corrected intensity values <span class="math inline">\(I_{nj}\)</span>.</p>
<p><span class="math display">\[
Z = \frac{I_{nj} - \bar{I}}{S}
\]</span></p>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="results" class="section level3">
<h3>Results</h3>
<ol style="list-style-type: decimal">
<li><p>Fit 200 interations. loglik = -3369.</p></li>
<li><p>Fit appears to be better for RFP than for GFP and DAPI.</p></li>
<li><p>Considering clustering of samples in the bivariate distribution of Red and Gree intensities. The sample clusters appear to coincide with the estimated cell times.</p></li>
<li><p>Consider residual intensities at the individual level, variation around the mean for all three channles is smaller than the observed intensity.</p></li>
<li><p>Consider residual intensity at the batch level within each individual, standard deviations are smaller for Red than for Green and DAPI. Moreover, for the most part, means of the residuals center at the mean for all channels, and more so for Red than for Green across the batches. These results suggest that there’s no need to fit a batch-level model for intensities.</p></li>
<li><p>Consider residual intensity at the batch level. For RFP, there’s not much variation between batches. For GFP, the variation of residual intensities is the largest for plate <code>18511_18855</code> and plate <code>19160_18870</code>. The plate <code>18511_18855</code> was the first one done in imaging, and the plate <code>19160_18870</code> contains the most bubbles on the plates based on imaging results.</p></li>
</ol>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="next-steps" class="section level3">
<h3>Next steps</h3>
<ol style="list-style-type: decimal">
<li>Assess model convergence conditions: run 10 times on 500 iterations; compare model convergences between different starting points (instead of random time, assign fixed time).</li>
</ol>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
</div>
<div id="load-data" class="section level2">
<h2>Load data</h2>
<p><span class="math inline">\(~\)</span></p>
<p>Import combined intensity data (see <a href="/project2/gilad/joycehsiao/fucci-seq/code/combine-intensity-data.R">combine-intensity-data.R</a>).</p>
<p><span class="math inline">\(~\)</span></p>
<pre class="r"><code>ints <- readRDS(file="../data/intensity.rds")</code></pre>
<p><span class="math inline">\(~\)</span></p>
<hr />
</div>
<div id="estimated-cell-time" class="section level2">
<h2>Estimated cell time</h2>
<p><span class="math inline">\(~\)</span></p>
<div id="observed-intensity-by-time" class="section level3">
<h3>Observed intensity by time</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-6-1.png" width="864" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="residuals-by-cell-time" class="section level3">
<h3>Residuals by cell time</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-7-1.png" width="864" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="redgreen-by-cell-time" class="section level3">
<h3>Red/Green by cell time</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
<hr />
</div>
</div>
<div id="residuals-distributions" class="section level2">
<h2>Residuals distributions</h2>
<p><span class="math inline">\(~\)</span></p>
<div id="by-individual" class="section level3">
<h3>By individual</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-9-1.png" width="864" style="display: block; margin: auto;" /><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-9-2.png" width="864" style="display: block; margin: auto;" /><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-9-3.png" width="864" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="by-individual-and-batch" class="section level3">
<h3>By individual and batch</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-10-1.png" width="768" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-11-1.png" width="768" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-12-1.png" width="768" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
</div>
<div id="by-batch" class="section level3">
<h3>By batch</h3>
<p><span class="math inline">\(~\)</span></p>
<p><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-13-1.png" width="960" style="display: block; margin: auto;" /><img src="figure/images-cellcycleR.Rmd/unnamed-chunk-13-2.png" width="960" style="display: block; margin: auto;" /></p>
<p><span class="math inline">\(~\)</span></p>
<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-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.2 (Nitrogen)
Matrix products: default
BLAS: /home/joycehsiao/miniconda3/envs/fucci-seq/lib/R/lib/libRblas.so
LAPACK: /home/joycehsiao/miniconda3/envs/fucci-seq/lib/R/lib/libRlapack.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_0.3.2 cowplot_0.8.0 dplyr_0.7.0
[7] data.table_1.10.4 cellcycleR_0.1.2 zoo_1.8-0
[10] binhf_1.0-1 adlift_1.3-3 EbayesThresh_1.4-12
[13] wavethresh_4.6.8 MASS_7.3-47 ggplot2_2.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.14 compiler_3.4.1 git2r_0.19.0 plyr_1.8.4
[5] tools_3.4.1 digest_0.6.12 evaluate_0.10.1 tibble_1.3.3
[9] gtable_0.2.0 lattice_0.20-34 rlang_0.1.4.9000 yaml_2.1.16
[13] stringr_1.2.0 knitr_1.17 rprojroot_1.2 grid_3.4.1
[17] glue_1.1.1 R6_2.2.2 rmarkdown_1.8 magrittr_1.5
[21] backports_1.0.5 scales_0.4.1 htmltools_0.3.6 assertthat_0.2.0
[25] colorspace_1.3-2 labeling_0.3 stringi_1.1.2 lazyeval_0.2.0
[29] munsell_0.4.3 </code></pre>
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