I figured out one way to do it. I used whole_tx_table = texpr(my.humandata, 'all') to extract everything into whole_tx_table then do final_fpkm_table = whole_tx_table[c("gene_name","sample
1","sample 2", ..)] to slice out only the gene_name and fpkm values, then write final_fpkm_table to a .cvs table. However, one problem I found in the final_fpkm_table.cvs table is that the
gene_names are not unique, there can be many rows for the gene 'Btf3l4' like below. What should I do with these values? Should I take sum, average, or max on the duplicate values to generate unique
gene_name-expression matrix? Also, can EdgeR, FPKM_count.py, or RSEM be used to generate unique gene_name-expression matrix?
Thanks for any advice.
Sample 1 Sample 2 Sample 3 Sample 4
Btf3l4 7.267802 7.386622 9.815619 9.739746
Btf3l4 0.941536 1.256349 1.365669 1.3953
Btf3l4 0.897259 0.718018 0.025479 0.168297
Btf3l4 0.823937 0.744246 1.132339 1.020087
Btf3l4 0.42134 0.351375 0.236908 0.517893
Btf3l4 1.219011 1.331794 2.030579 1.207322
Hi did you solve this issue?