User: Sebastian Hesse
Sebastian Hesse • 190
- Reputation:
- 190
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- Location:
- Germany / Munich / Dr. von Hauner Children's Hospital
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- Google Scholar Page
- Last seen:
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- Joined:
- 2 years, 1 month ago
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Physician scientist
Reseach focus: Multiomics, Whole exam sequencing
Clinical focus: Pediatrics, Primary Immunodeficiencies, Oncology, Genetics
Posts by Sebastian Hesse
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... Still nothing? I just used the function again and checked here if anyone had an idea... but seems that not.
Trying to revive the question with a comment. ...
written 6 days ago by
Sebastian Hesse • 190
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... Thanks a lot for your answers!
Im actually using a whole array of normalisations and then then which one stands out as best (eg in numbers of diffEx and clustering algorithms).
Im using:
- clr
- cyclic loess
- VSN
- cubic splines
- quantile normalisation
- median normalisation
So far VSN always ga ...
written 18 days ago by
Sebastian Hesse • 190
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... There are several questions about this topic but none of them received a clear answer, so here is my try to find a solution:
I am working with a proteome dataset that clearly has a batch effect from the date the MS measurement was done.
The dataset is also not normalised yet.
Now, which is the corr ...
written 18 days ago by
Sebastian Hesse • 190
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... I am analyzing the proteome data of cells from patients with different monogenic backgrounds of a specific disease.
For cluster analysis, I applied the k-means algorithm from the stats package and used sum-of-square and silhouette width to determine the optimal amount of clusters. Next, I plotted th ...
written 12 weeks ago by
Sebastian Hesse • 190
• updated
18 days ago by
Papyrus • 540
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... You could simply filter the genes by the significance level before running your k-means analysis:
#extract significant results
get_sigs <- function(df, adj.p, FC){
df <- df[df$adj.P.Val < adj.p & abs(df$logFC) >= FC,]
return(df)
}
This is how I reduce my gene/p ...
written 12 weeks ago by
Sebastian Hesse • 190
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... [ATpoint][1] and [Kevin Blighe][2] brought the solution!
selectLab expects a character vector with the exact labels we are handing to lab, not the corresponding row names.
If your metadata are factors, labs need as.character(), otherwise it prints the factor levels only.
biplot(data_PCA,
...
written 6 months ago by
Sebastian Hesse • 190
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Comment:
C: Usage of selectLab in PCAtools, R
... Ah, ok. This explains it!
It seems that selectLab is expecting the exact labels to include, not the corresponding row names / samples.
If I give selectLab a character vector with all metadata chars to include it works.
Actually it needs a little workaround still with as.character(metadata) as other ...
written 6 months ago by
Sebastian Hesse • 190
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Comment:
C: Usage of selectLab in PCAtools, R
... data_PCA$metadata$genotype contains a character vector that I use as a label.
If I use it without the selectLab option all runs fine but labels are shown for each data point which is a bit overwhelming. Thats why im trying to limit the label just to a few selected points. ...
written 6 months ago by
Sebastian Hesse • 190
0
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Comment:
A: Usage of selectLab in PCAtools, R
... Thanks for your answers, unfortunately it doesn't work.
Maybe I am wrong to assume that I can use the option to label only some of the data-points in the PCA?
If I hand it a character vector with just a few of the row names from my metadata it doesn't work though but instead disables all labels.
I ...
written 6 months ago by
Sebastian Hesse • 190
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... Hi guys
I just can not figure out how to use the selectLab option in the biplot of PCAtools.
I tried creating a logical vector (withF for every sample I don't want to have a lab for) as well as a vector with all the sample_ids that should have a label. But nothing works nd I can't wrap my head aroun ...
written 6 months ago by
Sebastian Hesse • 190
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