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2 hours ago by

I have a dataset with two factors: Stage and Form, 4 replicates associated with each combination of the factors:

         Stage Form

DS1_Wr60  Wr60   DS
DS2_Wr60  Wr60   DS
DS3_Wr60  Wr60   DS
DS4_Wr60  Wr60   DS
WS1_Wr60  Wr60   WS
WS2_Wr60  Wr60   WS
WS3_Wr60  Wr60   WS
WS4_Wr60  Wr60   WS
DS1_PP50  PP50   DS
DS2_PP50  PP50   DS
DS3_PP50  PP50   DS
DS4_PP50  PP50   DS
WS1_PP50  PP50   WS
WS2_PP50  PP50   WS
WS3_PP50  PP50   WS
WS4_PP50  PP50   WS
DS1_P15    P15   DS
DS2_P15    P15   DS
DS3_P15    P15   DS
DS4_P15    P15   DS
WS1_P15    P15   WS
WS2_P15    P15   WS
WS3_P15    P15   WS
WS4_P15    P15   WS
DS1_P50    P50   DS
DS2_P50    P50   DS
DS3_P50    P50   DS
DS4_P50    P50   DS
WS1_P50    P50   WS
WS2_P50    P50   WS
WS3_P50    P50   WS
WS4_P50    P50   WS

The comparisons I hope to make are:

  1. DE genes across all four stages

  2. DE genes between a specified pair of stages

  3. DE genes between two forms of a specified stage

My first question is how do I create the initial dds that is possible to cover all these comparisons?
That is what I tried:

count_data = dataframe of count matrix of all samples
Stage = list of Stages for all samples
Form = list of Forms for all samples
col_data <- data.frame(row.names = colnames(count_data),Stage,Form)
dds <- DESeqDataSetFromMatrix(countData = count_data,colData = col_data,design = ~ Stage)

Based on my understanding, this will generate DE genes between different stages. And I can use 'contrast' to specify the result of DE genes of a specified pair of stages.
Then how I can get the list of DE among all four stages?
And what about the 'Form'? Do I also put it as 'design' as well?

I know that a more straightforward way is to selectively input the samples that I want to investigate and do multiple comparisons seperately. But is it possible to do it in the whole matrix?
Sorry I am totally lost how to design the dds and reading the help page of deseq2 does not help a lot. Hope someone could clarify this.

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modified 1 hour ago

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2 hours ago
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tianshenbio20



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