We performed differential metabolite analysis comparing ccRCC tissue versus adjacent normal tissue of the patient's subset of young patient's (age <42 years) using median normalised data from the supplementary table 2 of Hakimi et. al.(="Tissue_Norm"). metabolite values used as input with row names being metabolite trivial names. doi:10.1016/j.ccell.2015.12.004
Format
An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 570 rows and 17 columns.
Examples
data(tissue_dma_young)
head(tissue_dma_young)
#> # A tibble: 6 × 17
#> Metabolite Log2FC AveExpr t.val p.val p.adj B SUPER_PATHWAY
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1-arachidonoylgly… -0.251 0.257 -0.332 0.745 0.902 -6.45 Lipid
#> 2 1-arachidonoylgly… -1.39 0.170 -4.07 0.00102 0.00763 -0.753 Lipid
#> 3 1-arachidonoylgly… -1.16 0.109 -3.24 0.00550 0.0257 -2.38 Lipid
#> 4 1-arachidonylglyc… -0.0922 -1.31 -0.252 0.804 0.942 -6.48 Lipid
#> 5 1-docosahexaenoyl… -1.29 -0.421 -1.80 0.0929 0.216 -4.99 Lipid
#> 6 1-heptadecanoylgl… -1.93 0.0472 -2.68 0.0172 0.0631 -3.46 Lipid
#> # ℹ 9 more variables: SUB_PATHWAY <chr>, COMP_ID <dbl>, PLATFORM <chr>,
#> # RI <dbl>, MASS <dbl>, CAS <chr>, PUBCHEM <dbl>, KEGG <chr>,
#> # Group_HMDB <chr>