covery, but for further analysis.

Table 6.5. A design matrix for the data of Table 6.4.

id

dex

celltype

geo_id

SRR1039508

control

N61311

GSM1275862

SRR1039509

treated

N61311

GSM1275863

SRR1039512

control

N052611

GSM1275866

SRR1039513

treated

N052611

GSM1275867

SRR1039516

control

N080611

GSM1275870

SRR1039517

treated

N080611

GSM1275871

SRR1039520

control

N061011

GSM1275874

SRR1039521

treated

N061011

GSM1275875

ose a count matrix shown in Table 6.4 was saved in X and a design

own in Table 6.5 was saved in Z. A working data was generated

qDataSetFromMatrix of DESeqs. The parameter tidy

ched on because the first column of the count matrix data was the

mes.

qDataSetFromMatrix(countData=X,colData=Z,

design=~class,tidy=TRUE)

data structure of Q was a DESeqDataSet data structure. The

ng count matrix had 38,694 genes and eight samples. The assay

s the sequencing counts. The prefix of gene IDs was

000000. The prefix of sample names was SRR10395. The sample

tion ID was dex. Q was an S4 object in R and its entries can be

sing slotNames. Based on this working data Q, a DESeq model

nstructed using the following code,

model=DESeq(Q)

er to obtain statistics such as the p values, the results function

called based on a DESeq model. This generates a table of results

Table 6.6.

res.table=results(model)