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<h1 class="title toc-ignore">Home</h1>
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<ul>
<li><a href="#overview">Overview</a></li>
<li><a href="#microscopy-image-analysis">Microscopy image analysis</a></li>
<li><a href="#rna-seq-data-preprcessing">RNA-seq data preprcessing</a></li>
<li><a href="#cell-cycle-signal-in-gene-expression-data">Cell cycle signal in gene expression data</a></li>
<li><a href="#model-fitting">Model fitting</a></li>
<li><a href="#one-time-investigations">One-time investigations</a></li>
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<hr />
<div id="overview" class="section level2">
<h2>Overview</h2>
<ul>
<li><a href="data-overview.html">Data description</a></li>
</ul>
<hr />
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<div id="microscopy-image-analysis" class="section level2">
<h2>Microscopy image analysis</h2>
<ul>
<li><a href="images-process.html">Processing images - from images to intensities</a></li>
</ul>
<p>We evaluated and pre-processed the results of image analysis as follows:</p>
<ol style="list-style-type: decimal">
<li>We visually inspect images deteced to have none or more than one nucleus. For cases that are inconsistent with visual inspection, we correct the number of nuclei detected.
<ul>
<li><a href="images-multiple-nuclei.html">Inspect images with multiple nuclei</a></li>
<li><a href="images-zero-nuclei.html">Inspect images with no nucleus</a></li>
</ul></li>
<li>We applied background correction to the intensity measurements of GFP, RFP and DAPI based on the following analyses.
<ul>
<li><a href="confess-prelim.html">CONFESS results</a></li>
<li><a href="images-qc.html">QC analysis including no. nuclei detected, DAPI, and intensity variation</a></li>
<li><a href="images-qc-followup.html">Explore using log10 sum pixel intensity for signal metrics</a></li>
<li><a href="images-metrics.html">Compare correction approaches using median versus mean background</a></li>
<li><a href="images-metrics-cell-shape.html">Explore associations between nucleus shape metrics vs intensities</a></li>
</ul></li>
<li>We analyzed intensity variation across individuals and batches and determined on an approach that removes batch effect in the data.
<ul>
<li><a href="images-qc-labels.html">Visualize signal variation by plate and individual identity</a></li>
<li><a href="images-qc-variation.html">Visualize the structure of signal variation by individual identity</a></li>
<li><a href="images-normalize-quantile.html">Quantile normalization for GFP, RFP and DAPI</a></li>
<li><a href="images-normalize-anova.html">Estimate variance explained in IBD and correct for batch effects in intensities</a></li>
</ul></li>
</ol>
<hr />
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<div id="rna-seq-data-preprcessing" class="section level2">
<h2>RNA-seq data preprcessing</h2>
<ol style="list-style-type: decimal">
<li>The first step in preprocessing RNA-seq data consists of QC and filtering.
<ul>
<li>Sample QC and filtering
<ul>
<li><a href="sampleqc.html">Sample QC criteria</a><br />
</li>
<li><a href="totals.html">Sequencing depth</a><br />
</li>
<li><a href="reads-v-molecules.html">Reads versus molecules</a></li>
</ul></li>
<li>Gene QC and filtering
<ul>
<li><a href="gene-filtering.html">gene filtering</a></li>
<li><a href="pca-tf.html">PCA with technical fators</a></li>
</ul></li>
</ul></li>
<li>We then analyzed and corrected for batch effect due to C1 plate in the sequencing data
<ul>
<li><a href="seqdata-batch-correction.html">Estimate variance explained in IBD and correct for batch effects</a></li>
</ul></li>
</ol>
<hr />
</div>
<div id="cell-cycle-signal-in-gene-expression-data" class="section level2">
<h2>Cell cycle signal in gene expression data</h2>
<ol style="list-style-type: decimal">
<li>We investigated cell cycle signals in the sequencing data alone.</li>
</ol>
<ul>
<li>Consider <a href="images-transgene.html">transgene count in sequencing data</a></li>
<li>Compute linear correlation between gene expression levels <a href="images-seq-correlation.html">with DAPI and FUCCI intensities</a></li>
</ul>
<ol start="2" style="list-style-type: decimal">
<li>We then assign categorical labels of cell cycle and explored the expresson profiles of these categories.</li>
</ol>
<ul>
<li>Cluster samples by <a href="images-pam.html">Partition around medoids(PAM)</a></li>
<li>Cluster samples by <a href="images-mclust.html">Guassian mixture modeling</a></li>
<li>Select a subset of samples that are closet to the cluster centers (cluster representives) <a href="images-subset-silhouette.html">using silhouette index</a></li>
<li>Examine gene pression scores defined by the Macosko paper in the selected cluster representatives <a href="images-classify-fucci.html">before confounding correction</a> and <a href="images-classify-fucci-adjusted.html">after confounding correction</a></li>
<li>Examine gene expression scores defined by the Macosk paer <a href="images-classify-leng.html">in the sorted cells of the Leng et al. 2015 paper</a></li>
</ul>
<ol start="3" style="list-style-type: decimal">
<li>We ordered cells on a circle using FUCCI intensities alone.</li>
</ol>
<ul>
<li>First, I used GFP and RFP intensities to estimate a <a href="images-circle-ordering.html">least-square fit of unit circle which approximate the relative ordering of cells on cell cycle</a></li>
<li>I computed the PCs of GFP and RFP and used these to infer the relative ordering on a circle (i.e., polar coordinates), and <a href="images-circle-ordering-eval.html">to evaluate the circle fit, I computed circular-circular and circular-linear correlation with DAPI and gene expression</a></li>
<li>Here I put together lists of genes identified as signfiicantly correlatd with the PC-based fit <a href="images-circle-ordering-sigcorgenes.html">by linear correlation and circular-linear correlation, also cyclical genes by smash</a></li>
</ul>
<hr />
</div>
<div id="model-fitting" class="section level2">
<h2>Model fitting</h2>
<ul>
<li>CellcycleR 0.1.6
<ul>
<li><a href="images-cellcycleR-convergence.html">Model convergence assessment</a></li>
<li><a href="images-cellcycleR.html">Fitting on intensities across plates and individuals</a></li>
<li><a href="cellcycler-seqdata-leng.html">Fitting on Leng data)</a></li>
<li><a href="cellcycler-seqdata-fucci.html">Fitting on fucci-seq RNA-seq data)</a></li>
</ul></li>
<li><p>Correlation for circular response variable: some simulations to check <a href="circ-correlation-simulation.html">its property and also relations with Fisher’s z transformation</a></p></li>
<li>Nonparametric circular regression
<ul>
<li>First, evaluate smash for smoothing gene expression data and compare <a href="images-circle-ordering-npreg.html">results of smash with NPCirc</a></li>
</ul></li>
</ul>
<hr />
</div>
<div id="one-time-investigations" class="section level2">
<h2>One-time investigations</h2>
<ul>
<li><p>Why some gene symbols (genes) correspond to <a href="ensembl.html">multiple Ensembl IDs?</a></p></li>
<li><p>I selected a set of cell cycle <a href="seqdata-select-cellcyclegenes.html">genes that belong to GO term Cell Cycle and looked at the overlap with the detected genes in our data.</a></p></li>
</ul>
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