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<h1 class="title toc-ignore">Data overview</h1>
<h4 class="author"><em>Joyce Hsiao</em></h4>
</div>
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<p><strong>Last updated:</strong> 2018-01-31</p>
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<p><strong>Code version:</strong> a1584da</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>Raw RNA-seq data: <code>data/eset-raw.rds</code></li>
</ul>
<p>The filtered data are stored as follows:</p>
<ul>
<li>combine intensity and RNA-seq: <code>data/eset-filtered.rds</code><br />
</li>
<li>FUCCI intensity data: <code>data/intensity.rds</code><br />
</li>
<li>RNA-seq data: <code>output/gene-filtering.Rmd/eset-filtered.rdata</code></li>
</ul>
<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>
<pre class="r"><code>ints <- readRDS(file="../data/intensity.rds")
colnames(ints)</code></pre>
<pre><code> [1] "plate" "image"
[3] "size" "perimeter"
[5] "eccentricity" "rfp.fore.zoom.mean"
[7] "rfp.fore.zoom.median" "gfp.fore.zoom.mean"
[9] "gfp.fore.zoom.median" "dapi.fore.zoom.mean"
[11] "dapi.fore.zoom.median" "rfp.back.zoom.mean"
[13] "rfp.back.zoom.median" "gfp.back.zoom.mean"
[15] "gfp.back.zoom.median" "dapi.back.zoom.mean"
[17] "dapi.back.zoom.median" "rfp.mean.log10sum"
[19] "gfp.mean.log10sum" "dapi.mean.log10sum"
[21] "rfp.median.log10sum" "gfp.median.log10sum"
[23] "dapi.median.log10sum" "unique"
[25] "chip_id" </code></pre>
<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 are stored in <code>output/gene-filtering.Rmd/eset-filtering.rdata</code> (both eSet and CPM data.table). These are filtered for genes and high quality samples using sequencing data results.</p></li>
</ul>
<pre class="r"><code>load(file="../output/gene-filtering.Rmd/eset-filtered.rdata")
eset_filtered</code></pre>
<pre><code>ExpressionSet (storageMode: lockedEnvironment)
assayData: 11489 features, 1025 samples
element names: exprs
protocolData: none
phenoData
sampleNames: 20170905-A01 20170905-A02 ... 20170924-H12 (1025
total)
varLabels: experiment well ... filter_all (43 total)
varMetadata: labelDescription
featureData
featureNames: EGFP ENSG00000000003 ... mCherry (11489 total)
fvarLabels: chr start ... source (6 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation: </code></pre>
<pre class="r"><code>str(cpm_filtered)</code></pre>
<pre><code> num [1:11489, 1:1025] 231 214 0 181 0 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:11489] "EGFP" "ENSG00000000003" "ENSG00000000005" "ENSG00000000419" ...
..$ : chr [1:1025] "20170905-A01" "20170905-A02" "20170905-A03" "20170905-A06" ...</code></pre>
<hr />
</div>
<div id="match-intensity-with-sequencing-data" class="section level2">
<h2>Match intensity with sequencing data</h2>
<p>We match labels in the intensity data and in the sequencing data. 990 samples are quantified in both datasets.</p>
<pre class="r"><code>pdata <- pData(eset_filtered)
pdata$unique <- paste(pdata$image_individual, sprintf("%05d", pdata$image_label), sep="_")
sample_include_bothdata <- intersect(ints$unique, pdata$unique)
length(sample_include_bothdata)</code></pre>
<pre><code>[1] 990</code></pre>
<p>Make a combined eSet object include both FUCCI intensity data and RNA-seq data.</p>
<pre class="r"><code>ints_combo <- ints[which(ints$unique %in% sample_include_bothdata), ]
eset_combo <- new("ExpressionSet",
exprs = exprs(eset_filtered)[,which(pdata$unique %in% sample_include_bothdata)],
phenoData = phenoData(eset_filtered)[which(pdata$unique %in% sample_include_bothdata), ],
featureData = featureData(eset_filtered))
pdata_combo <- pData(eset_combo)
pdata_combo$unique <- paste(pdata_combo$image_individual,
sprintf("%05d", pdata_combo$image_label), sep="_")
all.equal(ints_combo$unique, pdata_combo$unique)
pdata_table <- rbind(varMetadata(phenoData(eset_combo)),
"mCherry background-corrected intensity (log10sum)",
"EGFP background-corrected intensity (log10sum)",
"DAPI background-corrected intensity (log10sum)",
"nucleus size",
"nucleus perimeter",
"nucleus eccentricity")
rownames(pdata_table) <- c(rownames(varMetadata(phenoData(eset_combo))),
"rfp.median.log10sum",
"gfp.median.log10sum",
"dapi.median.log10sum",
"size", "perimeter", "eccentricity")
phenoData(eset_combo) <- new("AnnotatedDataFrame",
data = data.frame(pData(eset_combo),
rfp.median.log10sum=ints_combo$rfp.median.log10sum,
gfp.median.log10sum=ints_combo$gfp.median.log10sum,
dapi.median.log10sum=ints_combo$dapi.median.log10sum,
size=ints_combo$size,
perimeter=ints_combo$perimeter,
eccentricity=ints_combo$eccentricity),
varMetadata = pdata_table)
saveRDS(eset_combo, file = "../data/eset-filtered.rds")</code></pre>
<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] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Biobase_2.38.0 BiocGenerics_0.24.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.14 digest_0.6.13 rprojroot_1.3-1 backports_1.1.2
[5] git2r_0.20.0 magrittr_1.5 evaluate_0.10.1 stringi_1.1.6
[9] rmarkdown_1.8 tools_3.4.1 stringr_1.2.0 yaml_2.1.16
[13] compiler_3.4.1 htmltools_0.3.6 knitr_1.17 </code></pre>
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