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<h1 class="title toc-ignore">Data overview</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#overview">Overview</a></li>
<li><a href="#fucci-intensity-data">FUCCI intensity data</a></li>
<li><a href="#sequencing-data">Sequencing data</a></li>
<li><a href="#access-expressionsets">Access expressionSets</a></li>
<li><a href="#session-information">Session information</a></li>
</ul>
</div>
<!-- The file analysis/chunks.R contains chunks that define default settings
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<p><strong>Last updated:</strong> 2018-04-11</p>
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package git2r is installed -->
<p><strong>Code version:</strong> 816dccd</p>
<hr />
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>We collected two types of data for each single cell sample: single-cell RNA-seq using C1 plates and FUCCI image intensity data.</p>
<ul>
<li><p>Raw RNA-seq data: <code>data/eset-raw.rds</code></p></li>
<li><p>Filtered RNA-seq data: <code>data/eset-filtered.rds</code></p></li>
<li><p>FUCCI intensity data: <code>data/intensity.rds</code></p></li>
<li><p>FUCCI intensity data adjusted for batch effect: <code>output/images-normalize-anova.Rmd/pdata.adj.rds</code></p></li>
<li><p>Final data combining filtered intensity and RNA-seq, including 11093 genes and 888 samples: <code>data/eset-final.rds</code></p></li>
</ul>
<p>Code used to generate data from <code>data/eset-raw.rds</code> to <code>data/eset-final.rds</code> is stored in <code>code/output-raw-2-final.R</code>.</p>
<hr />
</div>
<div id="fucci-intensity-data" class="section level2">
<h2>FUCCI intensity data</h2>
<ul>
<li><p>Combined intensity data are stored in <code>data/intensity.rds</code>. These include samples that were identified to have a single nuclei .</p></li>
<li><p>Data generated by [combine-intensity-data.R][data/combine-intensity-data.R]. Combining image analysis output stored in <code>/project2/gilad/fucci-seq/intensities_stats/</code> into one <code>data.frame</code> and computes summary statistics, including background-corrected RFP and GFP intensity measures.</p></li>
</ul>
<hr />
</div>
<div id="sequencing-data" class="section level2">
<h2>Sequencing data</h2>
<ul>
<li><p>Raw data from each C1 plate are stored separatley in <code>data/eset/</code> by experiment (batch) ID.</p></li>
<li><p>Raw data combining C1 plate are stored in <code>data/eset-raw.rds</code>.</p></li>
<li><p>Filtered raw data excluding low-quality sequencing samples and genes that are lowly expressed or overly expressed are stored in <code>data/eset-filtered.rds</code>..</p></li>
</ul>
<hr />
</div>
<div id="access-expressionsets" class="section level2">
<h2>Access expressionSets</h2>
<p>We store feature-level (gene) read count and molecule count in <code>expressionSet</code> (<code>data/eset</code>) objects, which also contain sample metadata (e.g., assigned indivdual ID, cDNA concentraion) and quality filtering criteria (e.g., number of reads mapped to FUCCI transgenes, ERCC conversion rate). Data from different C1 plates are stored in separate <code>eset</code> objects:</p>
<p>To combine <code>eset</code> objects from the different C1 plates:</p>
<p><code>eset <- Reduce(combine, Map(readRDS, Sys.glob("data/eset/*.rds")))</code></p>
<p>To access data stored in <code>expressionSet</code>:</p>
<ul>
<li><p><code>exprs(eset)</code>: access count data, 20,421 features by 1,536 single cell samples.</p></li>
<li><p><code>pData(eset)</code>: access sample metadata. Returns data.frame of 1,536 samples by 43 labels. Use <code>varMetadata(phenoData(eset))</code> to view label descriptions.</p></li>
<li><p><code>fData(eset)</code>: access feature metadata. Returns data.frame of 20,421 features by 6 labels. Use <code>varMetadata(featureData(eset))</code> to view label descriptions.</p></li>
<li><p><code>varMetadata(phenoData(eset))</code>: view the sample metadata labels.</p></li>
<li><p><code>varMetadata(featureData(eset))</code>: view the feature (gene) metadata labels.</p></li>
</ul>
<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] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.1 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
[5] tools_3.4.1 htmltools_0.3.6 yaml_2.1.18 Rcpp_0.12.16
[9] stringi_1.1.7 rmarkdown_1.9 knitr_1.20 git2r_0.21.0
[13] stringr_1.3.0 digest_0.6.15 evaluate_0.10.1</code></pre>
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