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Could you be more precise on :
Do you have reads, counts, gene lists, DE gene lists ?
thanks for asking. I have RNA seq data with treatments at 6 different time points. I want to perform gene enrichment analysis for all of them. i.e TPM matrix of 6 Columns, gene names as row names.
TPM is not a good method to look at gene expression across experiments. I strongly suggest you to get raw count then read DESeq2 vignette and follow the tutorial to get DE genes. DESeq2 will normalize your counts with RLE method, which deal with the biais of RNA population composition for each condition (DESeq2 paper) RNA-seq, why normalize for library size?
Once you got DE genes you can do pathway analysis with GSEA or R scripts, like this : https://bioconductor.org/packages/release/bioc/vignettes/ReactomePA/inst/doc/ReactomePA.html