# Multifactorial Design for DESEQ2

Multifactorial Design for DESEQ2

1

@b4de4377

Last seen 3 days ago

Netherlands

Hello community,

I’m interested in conducting a DEG analysis to obtain differentially expressed genes based on treatment per cell line. In other words, determine the effect of condition per cell line and extract DEGs based on treatment.

## Overview

Experimental Design:
This consists of a two-factorial design with factors: cell line (A vs B) and treatment (control vs treatment).

Research Question:
Does the treatment have a different given effect per cell line?

Goal:
Compare the effect of treatment per cell line (A vs B)

## Experimental Design:

``````sample  cell_line condition
A_Ctr_1    A     control
A_Ctr_2    A     control
A_Ctr_3    A     control
A_Met_1    A     treatment
A_Met_2    A     treatment
A_Met_3    A     treatment
B_Ctr_1    B     control
B_Ctr_2    B     control
B_Ctr_3    B     control
B_Met_1    B     treatment
B_Met_2    B     treatment
B_Met_3    B     treatment
``````

## DESeq2 Analysis

``````#Check multi-factorial design for experimental design
print(model.matrix(~cell_line + condition, expDesign))

# Constructing the DESeq2 object (using two design factor)
dds <- DESeqDataSetFromMatrix(countData = geneCountsMat,
colData = expDesign,
design = ~ cell_line + condition + cell_line:condition)

# Filter out lowly expressed genes, here the rowSums(counts(dds)) >= 10 filters out low-count genes
# i.e. keep rows that have at least 10 reads
dds <- dds[ rowSums(counts(dds)) >= 10, ]

#select the reference level for comparing cell lines (set the factor level)
#dds\$cell_line <- relevel(dds\$cell_line, ref = "A")

"Running DESeq"
# Estimate size factors and dispersion
dds <- DESeq(dds)

# see all comparisons (here there are two given we want to compare conditions and cel_lines)
resultsNames(dds)
``````

## Questions:

1. Is the design here enough and how can I obtain genes per cell line,
would this be done with contrasts in results?
2. Do I need to relevel the baseline per cell line in this case?
3. Should I instead use the interactions instead to obtain genes per cell line?
4. vst normalization also necessary here?

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