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<scheme description="Here, we introduce a transfer learning scenario. We have two data sets of cells, potentially measured with different protocols or assays. The difference in methods is expected to be the main source of variation, occluding signal of interest.&#10;&#10;The goal is to infer a model - using the first data set - to classify each cell into one of the 3 cell cycle stages.  &#10;Then, using the inferred model, we can transfer the information to the second data set - predict the phases of the cell cycle. As all cells are labelled with known cell cycle phases, we can evaluate the performance of our model with common metrics.&#10;&#10;The workflow includes: Loading, gene filtering and preprocessing, data set alignment, visualization and performance evaluation." title="Cell cycle phase prediction (transfer learning)" version="2.0">
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		<text font-family="Helvetica" font-size="16" id="0" rect="(50.00000000000006, -325.55555555555566, 460.0, 316.0)" type="text/html">&lt;b&gt;Cell cycle phase transfer learning&lt;/b&gt;
&lt;br/&gt; &lt;br/&gt; 
Here, we introduce a transfer learning scenario. We have two data sets of cells, potentially measured with different protocols or assays. The difference in methods is expected to be the main source of variation, occluding signal of interest.
&lt;br/&gt; &lt;br/&gt; 
The goal is to infer a model - using the &lt;i&gt;first&lt;/i&gt; data set - to classify each cell into one of the 3 cell cycle stages.  
Then, using the inferred model, we can transfer the information to the &lt;i&gt;second&lt;/I&gt; data set - predict the phases of the cell cycle. As all cells are labelled with known cell cycle phases, we can evaluate the performance of our model with common metrics.
&lt;br/&gt; &lt;br/&gt; 
The workflow includes: Loading, gene filtering and preprocessing, data set alignment, visualization and performance evaluation.

</text>
		<text font-family="Helvetica" font-size="16" id="1" rect="(52.22222222222223, 269.9999999999999, 227.7777777777778, 94.44444444444457)" type="text/plain">Load data sets on cell cyle phases in Mouse Embryonic stem cells (mESCs). Filter the genes based on selected min/max detection thresholds.</text>
		<text font-family="Helvetica" font-size="16" id="2" rect="(317.77777777777766, 221.1111111111112, 237.77777777777777, 207.33333333333331)" type="text/html">Concatenate the two datasets by retaining the intersection of available genes; The expression values are processed as follows:

&lt;ol&gt;
  &lt;li&gt;Normalize cell profiles (CPM),&lt;/li&gt;
  &lt;li&gt;select highly-variable genes,&lt;/li&gt;
  &lt;li&gt;log-transform,&lt;/li&gt;
  &lt;li&gt;scaling (z-score).&lt;/li&gt;
&lt;/ol&gt; </text>
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		<text font-family="Helvetica" font-size="16" id="5" rect="(701.1111111111113, -318.8888888888889, 237.77777777777783, 176.66666666666669)" type="text/html">Apply &lt;i&gt;Align datasets&lt;/i&gt; to search for shared variation between the two data sets. 

Use 10 canonical correlation (CCA) components, quantile normalization and dynamic time warping.
&lt;br/&gt;&lt;br/&gt;
Compare the &lt;i&gt;t-SNE&lt;/i&gt; plots before and after alignment.
</text>
		<text font-family="Helvetica" font-size="16" id="6" rect="(768.8888888888891, 376.66666666666697, 312.22222222222206, 252.0)" type="text/html">Finally, let's quantitatively evaluate the projection.
&lt;br/&gt;&lt;br/&gt;
Use &lt;i&gt;Select Rows&lt;/i&gt; to split the data on Source ID; imagine that cell cycle phases are known only for the &lt;i&gt;mESC Fluidigm&lt;/i&gt; portion. 
&lt;br/&gt;&lt;br/&gt;
The &lt;i&gt;Test &amp; Score&lt;/i&gt; widget will feed the training data to the &lt;i&gt;Logistic Regression&lt;/i&gt; model and test it on separate (test) data.
</text>
		<text font-family="Helvetica" font-size="16" id="7" rect="(1067.7777777777785, 137.77777777777794, 231.1111111111111, 94.44444444444446)" type="text/html">Compare common classification metrics;  &lt;i&gt;Logistic Regression&lt;/i&gt;  does a singificanlty better job on aligned data!</text>
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