pathway analysis using tidybulk about tidybulk HOT 8 OPEN

Hello,

a tutorial was meant to go to our blog very long time ago. But we don't have enough forces to keep up.

tidyomics/tidyomicsBlog@0d498f0

Have a look, and if you want to improve and be the one publishing it to the repo, with that you will become a contributor to tidytranscriptomics! In that case, feel free to change the author's name to yours.

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Thank you. I will try that from my end.

Another question:

I have a treatment column that’s made of Control, T1 , T2 and T3 samples.
I am interested in looking at genes that are different between Control and (T1,T2,T3)

For now , I was setting Control as the reference using relevel function and then used test_differential_abundance(~treatment) for DE analysis/ DE methods comparison. I dont need a superset of genes that’s composed of C vs T1 , C vs T2 and C vs T3. I need DE genes that are different between control and treatments (combined). Is this method correct?

One more question, how does one incorporate complicated design matrix in the model? for example, I would like to compare Control Average versus Treatment average in the test_differential_abundance function?
They have given the comparison in the publication here : https://f1000research.com/articles/9-1444
makeContrasts((T1+T2+T3)/3-Control, levels=colnames(design))
How do I incorporate such complicated contrasts/design into the test_differential_abundance function and contrasts argument?

Is there an easy way to extract contrasts comparison made for DESeq2, limma and edgeR easily?
For DESeq2, I was able to figure this out using the code
resultNames(attr(counts_de, “internals”)$DESeq2
Unable to extract such comparisons from edgeR and limma.

Thank you so much for all of your help. I have recently used tidybulk extensively and am looking to incorporate tidyseurat in the future. Keep up the good work! Thanks again,
Aditya

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• If I understood your design, you should have treatment as a binary factor "treated", "untreated".

• there is the contrast argument, for more complex designs

s there an easy way to extract contrasts comparison made for DESeq2, limma and edgeR easily?

I don't understand what you mean by "extract contrasts". Can you please reformulate?

from tidybulk.

Thank you. Good suggestion. Are you suggesting combining treatments into 1 group? Will it not result in issues for DE tools to model dispersion within those treatment groups?

How can I input such complicated contrast in the function?makeContrasts((T1+T2+T3)/3-Control, levels=colnames(design))

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It is not clear how many samples for how many conditions you have. It is hard for me to suggest an analysis design with so little information, and besides, I don't really know what is the biological question, which should dictate the design matrix and contrasts.

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Sure. I will try to make it clear in this comment. I have 12 samples - 3 Controls, 3 T1 , 3 T2 and 3 T3. I am interested in looking at genes that are different between controls and treatment. Also I would like to obtain DE genes that are different between control average and treatment average for which I had provided the contrast above that I want from the analysis (T1 + T2 + T3)/3 - Controls.
I am unable to input such contrasts in the test_differential_abundance function under the contrasts argument.
Can you suggest the design and contrasts arguement for looking at

1. control vs treatments and
2. controls average versus treatment average.

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I see, can you show me your code, and the error. For this can you please open a new issue, because this diverged from the original, and often people look for issue title to solve theirs.

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okay opening a new issue

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