ADSP PCA - Revision history

Bradley Monk at 01:53, 12 June 2018

2018-06-12T01:53:15Z

← Older revision		Revision as of 18:53, 11 June 2018
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	5. [[ADSP Stats\|Descriptive Statistics]]<br>		5. [[ADSP Stats\|Descriptive Statistics]]<br>
	}}		}}

	~~Again, this code can be downloaded from the: [https://github.com/subroutines/genos GENOS GIT]~~

	<br> <br> <br>		<br> <br> <br>

Bradley Monk at 12:38, 6 February 2018

2018-02-06T12:38:09Z

← Older revision		Revision as of 05:38, 6 February 2018
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	~~t-Distributed Stochastic Neighbor Embedding (tSNE)~~ is a ~~technique like PCA~~ that ~~allows one perform~~ dimensionality reduction for visualization purposes. ~~Supposedly~~ tSNE ~~does better than~~ PCA at revealing clusters in high-dimensional data. ~~Whereas~~ PCA ~~only~~ allows you to visualize two or three components directly against each at the same time ~~-- tSNE uses math magic to coerce a high-dimensional dataset into either a 2D or 3D array~~.		PCA is a numerical transformation that performs dimensionality reduction, often for data visualization purposes. Like tSNE, PCA helps revealing clusters in high-dimensional data. PCA allows you to visualize two or three components directly against each at the same time.

	{{SmallBox\|float=right\|clear=none\|margin=0px 0px 8px 18px\|width=170px\|font-size=13px\|Other Analyses\|txt-size=11px\|		{{SmallBox\|float=right\|clear=none\|margin=0px 0px 8px 18px\|width=170px\|font-size=13px\|Other Analyses\|txt-size=11px\|
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	5. [[ADSP Stats\|Descriptive Statistics]]<br>		5. [[ADSP Stats\|Descriptive Statistics]]<br>
	}}		}}

	t-SNE models each multi-dim object against a point on a euclidean surface in such a way that similar features are modeled by nearby point functions and dissimilar features are modeled by distant point functions. It then projects these points onto the plane allowing you visualize, what would effectively be, all the interesting principal component combinations - the ones that yield unique clusters - simultaneously.

	Again, this code can be downloaded from the: [https://github.com/subroutines/genos GENOS GIT]		Again, this code can be downloaded from the: [https://github.com/subroutines/genos GENOS GIT]

Bradley Monk: /* STUDY COHORT */

2018-02-06T12:35:57Z

STUDY COHORT

← Older revision		Revision as of 05:35, 6 February 2018
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	<big>Top 50 variants</big>		<big>Top 50 variants</big>
	[[File: PCA 2D 3D ~~COHORT~~ 50vars.png\|800px]]		[[File: PCA 2D 3D Cohort 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: PCA 2D 3D ~~COHORT~~ 2000vars.png\|800px]]		[[File: PCA 2D 3D Cohort 2000vars.png\|800px]]










	====AGE====		====AGE====

Bradley Monk: /* PCA Plots */

2018-02-06T12:34:18Z

PCA Plots

← Older revision		Revision as of 05:34, 6 February 2018
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	</syntaxhighlight>		</syntaxhighlight>

	<big>Top ~~100~~ variants</big>		<big>Top 50 variants</big>
	[[File: ~~TSNE Case Control~~.png\|800px]]		[[File: PCA 1D 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: ~~TSNE Case Control 2kvars~~.png\|800px]]		[[File: PCA 1D 2000vars.png\|800px]]


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	</syntaxhighlight>		</syntaxhighlight>

	<big>Top ~~100~~ variants</big>		<big>Top 50 variants</big>
	[[File: ~~TSNE Case Control~~.png\|800px]]		[[File: PCA 2D 3D CASE CTRL 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: ~~TSNE Case Control 2kvars~~.png\|800px]]		[[File: PCA 2D 3D CASE CTRL 2000vars.png\|800px]]


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	</syntaxhighlight>		</syntaxhighlight>

	<big>Top ~~100~~ variants</big>		<big>Top 50 variants</big>
	[[File: ~~TSNE Study Cohort~~.png\|800px]]		[[File: PCA 2D 3D COHORT 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: ~~TSNE Study Cohort 2kvars~~.png\|800px]]		[[File: PCA 2D 3D COHORT 2000vars.png\|800px]]



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	</syntaxhighlight>		</syntaxhighlight>

	<big>Top ~~100~~ variants</big>		<big>Top 50 variants</big>
	[[File: ~~TSNE Age~~.png\|800px]]		[[File: PCA 2D 3D AGE 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: ~~TSNE Age 2kvars~~.png\|800px]]		[[File: PCA 2D 3D AGE 2000vars.png\|800px]]


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	</syntaxhighlight>		</syntaxhighlight>

	<big>Top ~~100~~ variants</big>		<big>Top 50 variants</big>
	[[File: ~~TSNE~~ APOE.png\|800px]]		[[File: PCA 2D 3D APOE 50vars.png\|800px]]

	<big>Top 2000 variants</big>		<big>Top 2000 variants</big>
	[[File: ~~TSNE~~ APOE ~~2kvars~~.png\|800px]]		[[File: PCA 2D 3D APOE 2000vars.png\|800px]]

Bradley Monk at 12:28, 6 February 2018

2018-02-06T12:28:59Z

Show changes

Bradley Monk at 12:22, 6 February 2018

2018-02-06T12:22:39Z

Show changes

Bradley Monk: Created page with "The Alzheimer's Disease Sequencing Project ([https://www.niagads.org/adsp/content/home ADSP]) has made available WES data in vcf format. The following is a step-by-step analys..."

2018-02-05T11:42:22Z

Created page with "The Alzheimer's Disease Sequencing Project ([https://www.niagads.org/adsp/content/home ADSP]) has made available WES data in vcf format. The following is a step-by-step analys..."

New page

The Alzheimer's Disease Sequencing Project ([https://www.niagads.org/adsp/content/home ADSP]) has made available WES data in vcf format. The following is a step-by-step analysis walkthrough of this dataset. Here the ultimate goal is to develop a platform for Alzheimer's Disease diagnosis based on genome sequencing information. The data for this analysis can be downloaded here:

: [https://drive.google.com/open?id=1e3tIbQhcDUF1vofAwf4oYVOl8XriC44v ADSP_WES_VCF_LATEST_RELEASE.mat].

Both the dataset and the following analysis code are in MATLAB format. Note however there is an [https://cran.r-project.org/web/packages/R.matlab/index.html R package] for reading .mat files. All MATLAB code and custom functions used in the demo walkthrough below can be downloaded here:

: [https://github.com/subroutines/genos GENOS GITHUB CODE REPO]

{{SmallBox|float=right|clear=none|margin=0px 0px 8px 18px|width=170px|font-size=13px|Other Analyses|txt-size=11px|
1. [[ADSP|Intro]] 
4. [[ADSP Neural Nets|More Neural Nets]] 
2. [[ADSP PCA|PCA]] 
3. [[ADSP t-SNE|t-SNE]] 
5. [[ADSP Stats|Descriptive Statistics]] 
}}

==Additional Genomics Analyses==
----
 
{{SmallBox|float=left|clear=none|margin=0px 0px 8px 18px|width=50%|font-size=18px|Other Analyses|txt-size=14px|
1. [[ADSP|Intro]] 
4. [[ADSP Neural Nets|More Neural Nets]] 
2. [[ADSP PCA|PCA]] 
3. [[ADSP t-SNE|t-SNE]] 
5. [[ADSP Stats|Descriptive Statistics]] 
}}

 

==Notes==
----

[http://www.bradleymonk.com/Category:ADSP Category:ADSP]
[[Category:ADSP]]