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	<title>
	Comments on: How to Normalize and Standardize Data in R for Great Heatmap Visualization	</title>
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	<link>https://www.datanovia.com/en/blog/how-to-normalize-and-standardize-data-in-r-for-great-heatmap-visualization/</link>
	<description>Data Mining and Statistics for Decision Support</description>
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		<title>
		By: kassambara		</title>
		<link>https://www.datanovia.com/en/blog/how-to-normalize-and-standardize-data-in-r-for-great-heatmap-visualization/#comment-19676</link>

		<dc:creator><![CDATA[kassambara]]></dc:creator>
		<pubDate>Tue, 21 Apr 2020 06:54:28 +0000</pubDate>
		<guid isPermaLink="false">https://www.datanovia.com/en/?p=15791#comment-19676</guid>

					<description><![CDATA[In reply to &lt;a href=&quot;https://www.datanovia.com/en/blog/how-to-normalize-and-standardize-data-in-r-for-great-heatmap-visualization/#comment-19675&quot;&gt;Jazmina&lt;/a&gt;.

&lt;div class=&quot;rdoc&quot;&gt;
&lt;p&gt;Thank you Jazmina for your positive feedback, highly appreciated!&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Standardization or Min-Max normalization?&lt;/strong&gt;. There is no obvious answer to this question: it really depends on the application.&lt;/p&gt;
&lt;p&gt;For example, in clustering analyses, &lt;strong&gt;standardization&lt;/strong&gt; may be especially crucial in order to compare similarities between features based on certain distance measures.&lt;/p&gt;
&lt;p&gt;Another example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance.&lt;/p&gt;
&lt;p&gt;In highthroughput gene expression data analyses,people tends to perform clustering on the standardized data (&lt;code&gt;scale(log2(expression_data))&lt;/code&gt;)&lt;/p&gt;
&lt;p&gt;A popular application &lt;strong&gt;of Min-Max scaling&lt;/strong&gt; (or &lt;strong&gt;normalization&lt;/strong&gt;) is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.&lt;/p&gt;
&lt;/div&gt;]]></description>
			<content:encoded><![CDATA[<p>In reply to <a href="https://www.datanovia.com/en/blog/how-to-normalize-and-standardize-data-in-r-for-great-heatmap-visualization/#comment-19675">Jazmina</a>.</p>
<div class="rdoc">
<p>Thank you Jazmina for your positive feedback, highly appreciated!</p>
<p><strong>Standardization or Min-Max normalization?</strong>. There is no obvious answer to this question: it really depends on the application.</p>
<p>For example, in clustering analyses, <strong>standardization</strong> may be especially crucial in order to compare similarities between features based on certain distance measures.</p>
<p>Another example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance.</p>
<p>In highthroughput gene expression data analyses,people tends to perform clustering on the standardized data (<code>scale(log2(expression_data))</code>)</p>
<p>A popular application <strong>of Min-Max scaling</strong> (or <strong>normalization</strong>) is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.</p>
</div>
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		<title>
		By: Jazmina		</title>
		<link>https://www.datanovia.com/en/blog/how-to-normalize-and-standardize-data-in-r-for-great-heatmap-visualization/#comment-19675</link>

		<dc:creator><![CDATA[Jazmina]]></dc:creator>
		<pubDate>Tue, 21 Apr 2020 02:09:16 +0000</pubDate>
		<guid isPermaLink="false">https://www.datanovia.com/en/?p=15791#comment-19675</guid>

					<description><![CDATA[Hi Kassambara,
I absolutely adore your articles. Thank you so so much for your amazing work. Super helpful! 
I always had a question in regards scaling/normalizing data.
Depending on the method you use the clustering of the elements change. Sometimes it can change quite a lot. Then, how do you know which dendrogram is correct? Are all correct? Because, I am biologist and the interpretation of the data can vary substantially.
Again, thank you for your great contribution and help.
Jaz (Australia)]]></description>
			<content:encoded><![CDATA[<p>Hi Kassambara,<br />
I absolutely adore your articles. Thank you so so much for your amazing work. Super helpful!<br />
I always had a question in regards scaling/normalizing data.<br />
Depending on the method you use the clustering of the elements change. Sometimes it can change quite a lot. Then, how do you know which dendrogram is correct? Are all correct? Because, I am biologist and the interpretation of the data can vary substantially.<br />
Again, thank you for your great contribution and help.<br />
Jaz (Australia)</p>
]]></content:encoded>
		
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