Hello I am doing differential gene expression RNA seq data using DESeq2 data is sequenced using two different time different sequencing strategies I used with and without batch correction in my design as mentioned below

My design

Sample  Type    batch
Sample1 WT  1
Sample2 WT  1
Sample3 WT  1
Sample4 KO2 2
Sample5 KO2 2
Sample6 KO1 2
Sample7 KO1 2
Sample8 KO1 1


#with this my qRT-PCR results are matching 
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=condition, design=~Type)
dds$Type <- relevel(dds$Type, ref = "WT")
dds <- DESeq(dds)
res <- results(dds)

#with this my qRT-PCR results are not matching 
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=condition, design=~batch+Type)
dds$Type <- relevel(dds$Type, ref = "WT")
dds <- DESeq(dds)
res <- results(dds)

please help me anything I am missing here not able understand which data to consider

Thank you

This is entirely expected.

If you do not control for variables that influence the outcome (the counts), then the model is mis-specified and can lead to spurious results.

Generally we include all batch variables. If there are many, they are unknown, or correlated, we often recommend to use factor analysis methods. But here you would just include your known batch variable.