I have two technical replicates for RNASeq data. They have fair correlation(~0.6). But upon comparing the differentially expressed genes, the genes which are UP in one replicate are down in other replicate, giving biasness towards the biology. I have used TPM to get normalized gene expression value in both the cases. To check in details, first I calculated TPM for entire gene list and picked 6 genes (table1) showing contrasting DE pattern. Then I selected only these 6 genes to calculate TPM(table2). From table2 it can be seen that the log2FC of rep1 and rep2 are in agreement as against the table1. This says that there are some genes whose value affect the calculation at entire gene list level (in table1). My question is how to overcome the biasness in entire calculation due to some of the genes which affect the calculation. Are there any packages available to deal such data?
Firstly 0.6 is not a good correlation.
Secondly, think about the normalisation you want:
if you are looking at changes between conditions (and it appears you are because you are talking about genes which are up and genes which are down), you need to do between sample normalisation, but between gene normalisation (which is what TPM is). EdgeR, deseq and limma all implement between sample normalisation on counts (not TPM).
if you are looking for between gene effects, (like correlation of expression levels, not correlation of fold changes), then TPM is suitable, but pearsons correlation coefficient is not suitable. Proportionality has recently be suggested as a better alternative for exactly the reason you outline in your question. See this paper. by Lovell et al.