Differential expression analysis

Question

[雨]

I am studying transcriptome analysis across species, such as the muscle tissue transcriptome of birds at different altitudes, and I have obtained transcriptome data for 10 species in total, including 3 low-altitude, 3 mid-altitude, and 4 high-altitude species bird species. Using the R package DESeq2, edegR, one-to-one differentially expressed gene analysis, I have learned.

But for example, I want to study the differential genes of all low-altitude and high-altitude species, can this differentially expressed gene analysis be done?or what i can do?can you share me the R code?

Thanks a lot

[雨]

Yes, I am using the gene expression matrix constructed from the orthologous single-copy genes of their species.Also,I am working on part of my master's project.

[Matthias Zepper]

If you already have the data for all 10 species in one unified count matrix, then you can indeed just include the altitude as a design factor in the formula like Trivas suggested.

[Matthias Zepper]

Yes, such analyses can be done. However, it is way more complicated than a single shareable R script - that is easily a master thesis, if comprehensively put in relation to existing other data, a relevant part of a PhD project.

How closely related are those ten bird species and are there good quality reference genomes available for all of them? The crucial part here is not the DE gene calling, but the ortholog assignment and if not yet available also the reconstruction of the phylogenetic tree with tools like BEAST.

[Lada]

Hi,

I don't know if you have resolved your problem but I am also a beginner in the interspecies transcriptomic so I am reading all the posts in this regard.

This is what I got so far:

1. I made de novo assemblies using Trinity for each species (I don't have reference genomes !) and I mapped my reads to estimate the expression level
2. Using a Transdecoder I generated the coding sequence datasets (PEP/FASTA).
3. I run Orthofinder to detect single-copy orthologues between each species pair (you have to be sure to compare the same genes). did you identify your orthologues between species? I guess you did since you mentioned that you made a gene expression matrix from single-copy orthologues (btw - how do you do that?)

This is where I stopped because now I have an excel table of orthologues and I have to figure out how to assign my read counts to each gene for each species. For the DE analysis, I found this method that was published last year which accounts for some normalisations that are needed for the cross-species comparisons: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441199/

So - do you guys think DESeq2 can be applied to the orthologue matrix?

I hope it helps and please let me know how did it go for you.

Lada

[ATpoint]

Please open a new question, don't ask in existing ones, thank you.

Answer 1

[Trivas]

For DESeq2, you can add a column to your colData by doing dds$altitude <- factor(c(x,y,z,a,b,c....)) Then you can change your design formula to be ~ altitude

[雨]

just change the dds$altitude <- factor(c(rep("HIGH",4),rep("LOW",3),rep("MID",3)) ？then，tmp <- results(dds,contrast=c("altitude","HIGH","LOW")) Can it work?

[Trivas]

You'll have to change the design formula, rerun the DESeq() function, then you can use the results function as written.

[雨]

sorry,I am new man for DESeq2,Can your more detail?I used DESeqDataSetFromMatrix function