<?xml version='1.0' encoding='utf-8'?>
<scheme description="In this workflow, we show how to calculate cell-cycle phase scores based on canonical markers, and regressing these out of the data during pre-processing. The data used in this examples consists of murine hematopoietic progenitors." title="Regressing out batch effects" version="2.0">
	<nodes>
		<node id="0" name="Single Cell Datasets" position="(198.0, 211.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owscdatasets.OWscDataSets" title="Single Cell Datasets" version="" />
		<node id="1" name="Marker Genes" position="(322.7450980392157, 450.4117647058823)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owmarkergenes.OWMarkerGenes" title="Marker Genes (G1/S phase)" version="" />
		<node id="2" name="Marker Genes" position="(436.0, 544.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owmarkergenes.OWMarkerGenes" title="Marker Genes (G2M phase)" version="" />
		<node id="3" name="Single Cell Preprocess" position="(329.0, 282.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owscpreprocess.OWscPreprocess" title="Single Cell Preprocess" version="" />
		<node id="4" name="Score Cells" position="(446.0, 369.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owscorecells.OWScoreCells" title="Score Cells (G1/S)" version="" />
		<node id="5" name="Score Cells" position="(558.0, 462.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owscorecells.OWScoreCells" title="Score Cells (G2M)" version="" />
		<node id="6" name="Batch Effect Removal" position="(710.0, 463.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owbatchnorm.OWBatchNorm" title="Batch Effect Removal" version="" />
		<node id="7" name="Scatter Plot" position="(708.962962962963, 561.4444444444443)" project_name="Orange3" qualified_name="Orange.widgets.visualize.owscatterplot.OWScatterPlot" title="Scatter Plot" version="" />
		<node id="8" name="t-SNE" position="(707.0, 376.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owtsne.OWtSNE" title="t-SNE (original)" version="" />
		<node id="9" name="t-SNE" position="(838.0, 463.0)" project_name="Orange3-SingleCell" qualified_name="orangecontrib.single_cell.widgets.owtsne.OWtSNE" title="t-SNE (batch effect removal)" version="" />
	</nodes>
	<links>
		<link enabled="true" id="0" sink_channel="Data" sink_node_id="4" source_channel="Preprocessed Data" source_node_id="3" />
		<link enabled="true" id="1" sink_channel="Data" sink_node_id="6" source_channel="Data" source_node_id="5" />
		<link enabled="true" id="2" sink_channel="Data" sink_node_id="3" source_channel="Data" source_node_id="0" />
		<link enabled="true" id="3" sink_channel="Data" sink_node_id="7" source_channel="Data" source_node_id="5" />
		<link enabled="true" id="4" sink_channel="Data" sink_node_id="5" source_channel="Data" source_node_id="4" />
		<link enabled="true" id="5" sink_channel="Data" sink_node_id="9" source_channel="Data" source_node_id="6" />
		<link enabled="true" id="6" sink_channel="Data" sink_node_id="8" source_channel="Data" source_node_id="5" />
		<link enabled="true" id="7" sink_channel="Genes" sink_node_id="5" source_channel="Genes" source_node_id="2" />
		<link enabled="true" id="8" sink_channel="Genes" sink_node_id="4" source_channel="Genes" source_node_id="1" />
	</links>
	<annotations>
		<text font-family="Helvetica" font-size="16" id="0" rect="(-30.0, -69.0, 535.0, 221.0)" type="text/html">&lt;b&gt;Regressing out cell-cycle (batch) effects&lt;/b&gt;
&lt;br/&gt;
&lt;br/&gt;
In this workflow, we show how to calculate cell cycle phase scores based on canonical markers, and regressing these out of the data during pre-processing. The data used in this examples consists of murine hematopoietic progenitors.
&lt;br/&gt;
&lt;br/&gt;
See also &lt;a href="https://satijalab.org/seurat/cell_cycle_vignette.html"&gt;Seurat workflow&lt;/a&gt;.</text>
		<text font-family="Helvetica" font-size="14" id="1" rect="(293.37037037037044, 74.62962962962962, 218.0, 140.0)" type="text/html">Load the data on murine hematopoetic progenitors and use &lt;i&gt;Single Cell Preprocess&lt;/i&gt; to normalize raw data and produce counts-per-million reads (CPM). Select highly variable genes, log-normalize and scale the expression data.</text>
		<text font-family="Helvetica" font-size="14" id="2" rect="(83.48148148148147, 543.3333333333333, 280.0, 166.0)" type="text/html">Use &lt;i&gt;Score Cells&lt;/i&gt; twice to assign a score to each cell based on known cell cycle markers. Use markers from Satija et al. (2018), associated to the G1/S and G2M cycle phases.

The resulting data has two additional meta columns, associated to the respective scores.

</text>
		<text font-family="Helvetica" font-size="14" id="3" rect="(790.0, 275.0, 237.0, 141.0)" type="text/html">Draw a &lt;i&gt;t-SNE&lt;/i&gt; plot. Observe strong influence of cell cycle on the overall gene expression patterns. This is seen as a string source of variance in the projection - cells of the same cycle phase cluster together.</text>
		<text font-family="Helvetica" font-size="14" id="4" rect="(896.0, 545.0, 310.0, 145.0)" type="text/html">Using &lt;i&gt;Batch Effect Removal&lt;/i&gt;, we can eliminate the cell cycle-associated variance and obtain corrected expression values. 

Observe how cells of the same cycle phase are now clustered to a much lesser degree.</text>
		<text font-family="Helvetica" font-size="18" id="5" rect="(264.3703703703704, 73.62962962962962, 38.0, 30.0)" type="text/plain">(1)</text>
		<text font-family="Helvetica" font-size="18" id="6" rect="(58.48148148148147, 543.3333333333333, 61.0, 42.0)" type="text/plain">(2)</text>
		<text font-family="Helvetica" font-size="18" id="7" rect="(765.0, 272.0, 79.0, 77.0)" type="text/plain">(4)</text>
		<text font-family="Helvetica" font-size="18" id="8" rect="(868.0, 545.0, 125.0, 89.0)" type="text/plain">(5)</text>
		<text font-family="Helvetica" font-size="18" id="9" rect="(654.0, 636.0, 35.0, 29.0)" type="text/plain">(3)</text>
		<text font-family="Helvetica" font-size="14" id="10" rect="(678.0, 637.0, 174.0, 105.0)" type="text/html">Cell cycle scores are expected to be decorrelated, which is seen in the &lt;i&gt;Scatter Plot&lt;/i&gt;.</text>
	</annotations>
	<thumbnail />
	<node_properties>
		<properties format="literal" node_id="0">{'auto_commit': True, 'controlAreaVisible': False, 'header_state': b'\x00\x00\x00\xff\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\xdd\x00\x00\x00\x08\x01\x01\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00d\xff\xff\xff\xff\x00\x00\x00\x81\x00\x00\x00\x00\x00\x00\x00\x08\x00\x00\x00!\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00d\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x03\xe8\x00\x00\x00\x00d', 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x02\x00\x00\x00\x00\x04\x08\x00\x00\x01\x98\x00\x00\x06\x88\x00\x00\x03\x96\x00\x00\x04\x08\x00\x00\x01\xae\x00\x00\x06\x88\x00\x00\x03\x96\x00\x00\x00\x00\x00\x00\x00\x00\n\x00', 'selected_id': 'nestorawa_forcellcycle.pkl.gz', 'splitter_state': b'\x00\x00\x00\xff\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\xa4\x00\x00\x00\xec\x01\xff\xff\xff\xff\x01\x00\x00\x00\x02\x00', '__version__': 1}</properties>
		<properties format="pickle" node_id="1">gAN9cQAoWBIAAABjb250cm9sQXJlYVZpc2libGVxAYhYCwAAAGZpbHRlcl90ZXh0cQJYBwAAAFNh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</properties>
		<properties format="pickle" node_id="2">gAN9cQAoWBIAAABjb250cm9sQXJlYVZpc2libGVxAYhYCwAAAGZpbHRlcl90ZXh0cQJYBgAAAFNh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</properties>
		<properties format="pickle" node_id="3">gAN9cQAoWAoAAABhdXRvY29tbWl0cQGIWBIAAABjb250cm9sQXJlYVZpc2libGVxAohYEwAAAHNh
[encoded data, 1197350 characters]
cjW7AABLAlgLAAAAU2NvcmUgKEcyTSlyNrsAAEsCdXViZXUu
</properties>
		<properties format="literal" node_id="4">{'aggregation': 'Mean expression', 'auto_apply': True, 'controlAreaVisible': True, 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x02\x00\x00\x00\x00\x03\xbd\x00\x00\x02V\x00\x00\x05\x01\x00\x00\x03\xa3\x00\x00\x03\xbd\x00\x00\x02l\x00\x00\x05\x01\x00\x00\x03\xa3\x00\x00\x00\x00\x00\x00\x00\x00\n\x00', 'score_variable_name': 'Score G1/S', '__version__': 1}</properties>
		<properties format="literal" node_id="5">{'aggregation': 'Mean expression', 'auto_apply': True, 'controlAreaVisible': True, 'savedWidgetGeometry': b'\x01\xd9\xd0\xcb\x00\x02\x00\x00\x00\x00\x03\xb9\x00\x00\x00\xf3\x00\x00\x04\xfe\x00\x00\x02@\x00\x00\x03\xb9\x00\x00\x01\t\x00\x00\x04\xfe\x00\x00\x02@\x00\x00\x00\x00\x00\x00\x00\x00\n\x00', 'score_variable_name': 'Score G2M', '__version__': 1}</properties>
		<properties format="pickle" node_id="6">gAN9cQAoWAsAAABhdXRvX2NvbW1pdHEBiFgSAAAAY29udHJvbEFyZWFWaXNpYmxlcQKIWAsAAABs
[encoded data, 330330 characters]
AGqzFwAAKXViZXUu
</properties>
		<properties format="pickle" node_id="7">gAN9cQAoWAsAAABhdXRvX3NhbXBsZXEBiFgTAAAAYXV0b19zZW5kX3NlbGVjdGlvbnECiFgSAAAA
[encoded data, 179487 characters]
LUcyTS0ycqMdAABLAnV1YmV1Lg==
</properties>
		<properties format="pickle" node_id="8">gAN9cQAoWAsAAABhdXRvX2NvbW1pdHEBiFgSAAAAY29udHJvbEFyZWFWaXNpYmxlcQKJWA0AAABs
[encoded data, 143751 characters]
</properties>
		<properties format="pickle" node_id="9">gAN9cQAoWAsAAABhdXRvX2NvbW1pdHEBiFgSAAAAY29udHJvbEFyZWFWaXNpYmxlcQKJWA0AAABs
[encoded data, 143451 characters]
b3JlLUcyTS0ycrQXAABLAnV1YmV1Lg==
</properties>
	</node_properties>
	<session_state>
		<window_groups />
	</session_state>
</scheme>
