Olink data on suicide attempt

#Olink-data-on-suicide-attempt

Data are from Swebic I, selected groups by prospective suicide attempts. Matched on litihum treatment, age and sex. The groups are also chose so that the 0 suicide group also have no past attempts.

andreas.goteson@gu.se
UPDATED: 191022

Content

  • Exploring phenotypic data
  • Exploring Olink data
  • Upsupervised clustering
  • 1D-tests
  • Supervised clustering
  • Functional mapping (STRING)

Set up

#Set-up
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Sample meta

#Sample-meta

Inspect data

Define phenotypic metadata

How many suicide attempt samples are there?

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So there are 398 suicide attempts all in all. All suicide attempts mentioned here are prospective, and here we look at number of days between sampling and suicide attempt.

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What does number codec mean in drugs?

Explore the data

#Explore-the-data
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Missing values

#Missing-values

How about missing? Plotting a T/F-matrix

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Two samples have all NA's --> exclude them!

Were they QC=Warning?

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Don't really know what that QC is about...

Distributions

#Distributions

There have been some discussion about LOD's in the group. Let's look at distributions for most every marker we got. May always be good to return to a histogram as well

92 markers

Plain histograms

#Plain-histograms
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Comparing plain histograms with log2 histograms

#Comparing-plain-histograms-with-log2-histograms
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Perhaps I should unit variance scale as well...

Comparing scaled with log2(scaled)

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Scale and log2 produces a left tail(?)

Scaling

#Scaling
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Unsupervised dim. red.

#Unsupervised-dim.-red.

To estimate the variance in the panel, and to find any unsupervised clustering

PCA

#PCA

Make sure this PCA model scales data!

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tSNE

#tSNE
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Umap

#Umap
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PCA

#PCA
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Find most significant hits

#Find-most-significant-hits
  • Filter p-signifikant
  • Violin plot with wrap
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Looking surprisingly interesting!

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Plot the 17 significant markers

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Det är inte mycket signal här!

FDR adjust

#FDR-adjust
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Only two proteins remain!

Supervised dim. red.

#Supervised-dim.-red.

OPLS-DA
A simple supervised clustering method should find some significances here!

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