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<scheme description="We align single-cell data from two different experimental conditions and recover shared subpopulations, thus removing batch effects. We use the data from Kang et al. (2018), where authors stimulate the peripheral blood mononuclear cells (PMBCs) with interferon-alpha and compare them to a control sample. Dataset alignment reduces treatment-specific variation that may occlude the differences among cell subpopulations shared between the two data sets." title="Data set preprocessing and alignment" version="2.0">
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		<text font-family="Helvetica" font-size="16" id="0" rect="(-90.0, -19.0, 386.0, 412.0)" type="text/html">&lt;b&gt;Data Set Preprocessing and Alignment&lt;/b&gt;
&lt;br/&gt;
&lt;br/&gt;
In Kang et al. (2018), the authors &lt;b&gt;stimulate&lt;/b&gt; the
peripheral blood mononuclear cells (PMBCs) with interferon-alpha (stimulated) and compare them to a control sample (control). The differences in treatment are expected to occlude the differences between different cell subpopulations, shared between the two data sets.
&lt;br/&gt;
&lt;br/&gt;

This workflows shows how to &lt;b&gt;align&lt;/b&gt; single cells from two different experimental conditions and recover shared subpopulations. The data and the methods stem from the &lt;a href="https://satijalab.org/seurat/immune_alignment.html"&gt;Seurat workflow&lt;/a&gt;.</text>
		<text font-family="Helvetica" font-size="14" id="1" rect="(-63.0, 631.0, 203.0, 63.0)" type="text/plain">Filter genes and cells in each data set based on minimal detection rate.</text>
		<text font-family="Helvetica" font-size="14" id="2" rect="(248.0, 224.0, 212.0, 124.0)" type="text/plain">Concatenate the two data sets. Then do per-cell normalization (CPM), select most variable genes, do log-transform and perform per-gene standardization (z-score).</text>
		<text font-family="Helvetica" font-size="14" id="3" rect="(656.0, 141.0, 193.0, 140.0)" type="text/plain">Align the cells coming from two different experiments and perform clustering on aligned data.

Select well-clustered cells, belogning to the largest clusters (e.g. C1-C12). </text>
		<text font-family="Helvetica" font-size="14" id="4" rect="(491.0, 187.0, 130.0, 76.0)" type="text/plain">The t-SNE plot reveals dataset-specific (batch) effects.</text>
		<text font-family="Helvetica" font-size="14" id="5" rect="(530.0, 672.0, 185.0, 83.0)" type="text/plain">Score cells using known cell type markers. Filter markers on 'Human' and 'Satija et. al' and then choose marker genes for subpopulation (e.g.,  markers for NK cells).</text>
		<text font-family="Helvetica" font-size="14" id="6" rect="(777.0, 629.0, 200.0, 156.0)" type="text/plain">Subpopulations that are shared between the two data sets and are detectable by known markers (see e.g. CD16 cells).</text>
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		<text font-family="Helvetica" font-size="14" id="13" rect="(861.0, 306.0, 196.0, 100.0)" type="text/plain">Clusters contain mixtures of cells from both data sets,
indicating sucessful alignment.</text>
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