I have 2 RNA-Seq datasets(tumors) coming from different countries. total samples n1=35(having tumors and normals) that is in-house and n2=99 external(only tumors). When I plot the log normalized data via glMDSplot I see 3 distinct clusters( one of in-house and 2 from the external). I used SVA to correct them at sva=5  which handles not only the differences of the lab but also the difference of the external data that was within them since it was not apparent why they had a huge difference. As I downloaded them from a consortium where meta-information is not present from which centers each of the data was coming so could not decide apriori a batch information. So the batch correction proceeded with SVA. 

Having corrected via SVA, my interest is to just find DEGs within the n2=99 samples which are external data having some classification as 2 different tumor classes(T1 and T2). I am only interested to find T2 vs T1 now using also the information of the SVA values as factors in the design and the information of the tumors being Solid and also liquid. So I created a design matrix.

cond<-factor(batch$tumorType[1:99]) # this is the main condition on which I want to perform DE
gr<-factor(batch.$dataType[1:99]) # this is another factor which the tumor has like some are solid and others are liquid tumors
sv.5<-sv$sv[1:99,] # using the SVA values as factors in the additive model
design <- model.matrix(~sv.5+condition+group)
design
(Intercept)   sv.51    sv.52      sv.53  sv.54  sv.55   condT2     grSol

1            1 -0.068  8.472e-02 -0.032  0.020  0.026   0           0
2            1 -0.051  6.310e-02  0.154 -0.069 -0.080   0           1
3            1 -0.094  9.266e-02  0.054 -0.082  0.068   0           1
4            1 -0.059  6.381e-02  0.046  0.126 -0.059   0           1
5            1 -0.076  4.110e-03 -0.182 -0.121 -0.065   0           1
6            1 -0.102  6.418e-02  0.064 -0.291 -0.085   0           1
. . .
. . .
93           1 -0.078  7.718e-02  0.164 -0.031 -0.066   1           1
94           1 -0.074 -6.784e-03 -0.061  0.120 -0.138   1           1
95           1 -0.077  6.435e-02 -0.035  0.067 -0.100   1           0
96           1 -0.069  8.491e-02  0.344  0.032 -0.106   1           1
97           1 -0.081  4.205e-02  0.302 -0.048 -0.021   1           1
98           1 -0.065  1.633e-02  0.055  0.049 -0.093   1           1
99           1 -0.064  7.848e-02 -0.029  0.024 -0.025   1           0

nf <- calcNormFactors(counts, method = "TMM")
dge <- DGEList(counts =counts, group = cond, norm.factors = nf)
v <- voom(dge, design, plot=TRUE) 
fit <- lmFit(v, design)
fit <- eBayes(fit)
top <- topTable(fit,coef=ncol(design),n = nrow(counts)) #20k genes in topTable
sum(top$adj.P.Val<0.05) # 6646 genes

#recalcluating FDR since I did only filtering of 0 counts so re-esitmating based on FC and expression

ww <- which(abs(top$logFC)>log2(2) & top$AveExpr > 0)
top$fdr <- 1
top$fdr[ww] <- p.adjust(top$P.Value[ww],method="fdr")
dim(top)
length(ww)
degs <- (top[top$fdr < 0.05,])
degs2.cond <- degs[abs(degs$logFC)>1, ]
dim(degs2.cond) # 1293 degs

Now the above DEGs are only taking into account the factor level group(solid vs liquid) and masking the condition(T1 vs T2 tumor types). If I remove the group from the design and proceed with just condition=tumorType(T1 vs T2) then I find only 120 degs of which half of them show the trend of the condition. Rest DEGs are not very clean when projected in a heatmap. I am not getting a perfect clustering but still I see some differences between the tumorTypes. How can I improve the design? I am sure am not giving the perfect model matrix and should I include some blocking or constrast so that I can still preserve the effect of tumorTypes(perform T1 vs T2) and not let the dataType (solid vs liquid) mask the DE analysis. Is there a way to proceed for this using voom? Can anyone give me pointers about 

Now the above DEGs are only taking into account the factor level group(solid vs liquid) and masking the condition(T1 vs T2 tumor types).

Well, obviously. You've specified coef=ncol(design), and your design matrix suggests that the last coefficient should represent the group effect, i.e., of solid vs liquid tumours. So this will test for DE between solid and liquid tumours, blocking on all the other factors. If you want to test for differences between T1 and T2, you should start by specifying the correct coef in the topTable call.

I remove the group from the design and proceed with just condition=tumorType(T1 vs T2) then I find only 120 degs of which half of them show the trend of the condition.

This is directly caused by inflated variances when you have factors of variation that are not captured in your design matrix. Also, I don't understand what you mean by "trend of the condition", nor do I understand what "Rest DEGs" are in the next sentence.

I understand what you say but if you consider of genes that are still significant with low fold changes coming as better FDR estimates...

I don't know what you mean by this, but let me assure you, Ryan is right and your filtering strategy is wrong. You cannot apply the BH correction on genes that have been filtered by log-fold change. This is guaranteed to enrich for false positives as the log-fold change is not independent of the p-value under the null hypothesis. Consider this simple scenario:

set.seed(1000)
y <- matrix(rnorm(100000), ncol=10)
design <- model.matrix(~gl(2,5))
fit <- lmFit(y, design)
fit <- eBayes(fit)
res <- topTable(fit, coef=2)
sum(res$adj.P.Val <= 0.05) # 0
ww <- abs(res$logFC) > 1
new.fdr <- p.adjust(res$P.Value[ww], method="BH")
sum(new.fdr <= 0.05) # 10

Congratulations; you've just detected 10 genes in a data set with no true differences. Further investigation will reveal that your filtering approach will distort the p-value distribution under the null hypothesis, rendering it non-uniform, and thereby break the BH correction. Proper filtering should be independent of the test statistic, and there is some literature about this - check it out.

P.S. I just realized that you are trying to merge an in-house data set with an external data set. This is rarely a good idea for DE analyses; for starters, the mean-variance relationship is unlikely to be the same between batches, which reduces the accuracy of the modelling. The batch effect may also distort TMM normalization by causing genes to become "DE".  Statistics aside, common sense should prevail here; do you really want to compare your in-house normal samples to tumour samples collected and sequenced by someone else at a different time and in a different lab? A much safer approach is to perform DE analyses separately within each batch and to perform a meta-analysis to extract the features of interest; see Merge different datasets regarding a common number of DE genes in R.