Practical Bioinformatics Mark Voorhies 4/28/2017 Mark Voorhies - - PowerPoint PPT Presentation

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Mar 25, 2023 213 likes •502 views

Practical Bioinformatics Mark Voorhies 4/28/2017 Mark Voorhies Practical Bioinformatics Pearson distances Pearson similarity N i ( x i x offset )( y i y offset ) s ( x , y ) = N N i ( x i x offset ) 2 i ( y i

SLIDE 1

Practical Bioinformatics

Mark Voorhies 4/28/2017

Mark Voorhies Practical Bioinformatics

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Pearson distances

Pearson similarity s(x, y) = N

i (xi − xoffset)(yi − yoffset)

N

i (xi − xoffset)2

N

i (yi − yoffset)2

Mark Voorhies Practical Bioinformatics

SLIDE 3

Pearson distances

Pearson similarity s(x, y) = N

i (xi − xoffset)(yi − yoffset)

N

i (xi − xoffset)2

N

i (yi − yoffset)2

Pearson distance d(x, y) = 1 − s(x, y)

Mark Voorhies Practical Bioinformatics

SLIDE 4

Pearson distances

Pearson similarity s(x, y) = N

i (xi − xoffset)(yi − yoffset)

N

i (xi − xoffset)2

N

i (yi − yoffset)2

Pearson distance d(x, y) = 1 − s(x, y) Euclidean distance N

i (xi − yi)2

N

Mark Voorhies Practical Bioinformatics

SLIDE 5

Comparing all measurements for two genes

−5

5 −5 5

Comparing two expression profiles (r = 0.97)

TLC1 log2 relative expression YFG1 log2 relative expression

Mark Voorhies Practical Bioinformatics

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Comparing all genes for two measurements

−10

−5 5 10 −10 −5 5 Array 1, log2 relative expression Array 2, log2 relative expression

Mark Voorhies

Practical Bioinformatics

SLIDE 7

Comparing all genes for two measurements

−10

−5 5 10 −10 −5 5

Euclidean Distance

Array 1, log2 relative expression Array 2, log2 relative expression

Mark Voorhies

Practical Bioinformatics

SLIDE 8

Comparing all genes for two measurements

−10

−5 5 10 −10 −5 5

Uncentered Pearson

Array 1, log2 relative expression Array 2, log2 relative expression

Mark Voorhies

Practical Bioinformatics

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PLoS Pathogens 12:e1005910, Naomi Phillip et al

Lower inflamatory response for Ripk3-/-//Casp8-/- transplanted macrophages in mice infected with Yersinia pestis Lower inflamatory response at the mRNA level in Ripk3-/-//Casp8-/- macrophages stimulated with lipopolysaccharide (LPS) 1) in vivo 2) ex vivo Mark Voorhies Practical Bioinformatics

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Measure all pairwise distances under distance metric

Mark Voorhies Practical Bioinformatics

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Hierarchical Clustering

Mark Voorhies Practical Bioinformatics

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Hierarchical Clustering

Mark Voorhies Practical Bioinformatics

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Hierarchical Clustering

Mark Voorhies Practical Bioinformatics

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Hierarchical Clustering

Mark Voorhies Practical Bioinformatics

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Hierarchical Clustering

Mark Voorhies Practical Bioinformatics

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It’s hard work at times, but you have to be realistic. If you have a large database with many variables and your goal is to get a good understanding of the interrelationships, then, unless you get lucky, this complex structure is bound to require some hard work to understand. Bill Cleveland and Rick Becker http://stat.bell-labs.com/project/trellis/interview.html

Mark Voorhies Practical Bioinformatics

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Using JavaTreeView

Mark Voorhies Practical Bioinformatics

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Adjust pixel settings for global view

Mark Voorhies Practical Bioinformatics

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Adjust pixel settings for global view

Mark Voorhies Practical Bioinformatics

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Select annotation columns

Mark Voorhies Practical Bioinformatics

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Select annotation columns

Mark Voorhies Practical Bioinformatics

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Select URL for gene annotations

Mark Voorhies Practical Bioinformatics

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http://www.ensembl.org/Mus musculus/Gene/Summary?g=HEADER

Mark Voorhies Practical Bioinformatics

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Select URL for gene annotations

Mark Voorhies Practical Bioinformatics

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Activate and detach annotation window

Mark Voorhies Practical Bioinformatics

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Activate and detach annotation window

Mark Voorhies Practical Bioinformatics

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Activate and detach annotation window

Mark Voorhies Practical Bioinformatics

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Homework

1 For a small expression profiling matrix ( 1000 genes):

Cluster the genes Calculate the correlation matrix Write a CDT file of the clustered gene matrix with the correlation matrix appended Visualize the CDT+GTR files in JavaTreeView – how well did the clustering work?

2 Repeat the previous exercise, exploring difference clustering

methods and/or distance methods

3 Read the supplemental RnaSeq methods for PLoS Pathogens

12:e1005910 (Text S2, exported from RStudio). To what extent is this a reproducible method? Is there additional data that would make it more reproducible?

Mark Voorhies Practical Bioinformatics