Set row names and remove all non-numeric columns. This is useful for
reformatting data objects in MotrpacRatTraining6moData,
e.g., MotrpacRatTraining6moData::PROT_HEART_NORM_DATA
Examples
df = MotrpacRatTraining6moData::PROT_HEART_NORM_DATA
df[1:5,1:8]
#> feature feature_ID tissue assay 90237015805 90245015805 90441015805
#> 1 <NA> XP_017456475.1 HEART PROT 0.00769 -0.05952 0.10992
#> 2 <NA> XP_017447817.1 HEART PROT 0.09326 -0.08879 0.31763
#> 3 <NA> NP_446341.1 HEART PROT -0.11469 0.10623 0.08138
#> 4 <NA> NP_071796.2 HEART PROT 0.05903 -0.06490 0.05673
#> 5 <NA> NP_001157776.1 HEART PROT 1.24616 0.63626 0.15487
#> 90420015805
#> 1 -0.00229
#> 2 0.07739
#> 3 0.08464
#> 4 -0.07494
#> 5 -0.39231
df_to_numeric(df)[1:5,1:4]
#> 90237015805 90245015805 90441015805 90420015805
#> XP_017456475.1 0.00769 -0.05952 0.10992 -0.00229
#> XP_017447817.1 0.09326 -0.08879 0.31763 0.07739
#> NP_446341.1 -0.11469 0.10623 0.08138 0.08464
#> NP_071796.2 0.05903 -0.06490 0.05673 -0.07494
#> NP_001157776.1 1.24616 0.63626 0.15487 -0.39231
df = load_sample_data("SKM-GN", "TRNSCRPT")
#> TRNSCRPT_SKMGN_NORM_DATA
df[1:5,1:8]
#> feature feature_ID tissue assay 90560015512 90581015512
#> 1 <NA> ENSRNOG00000000008 SKM-GN TRNSCRPT 0.04044 -0.09760
#> 2 <NA> ENSRNOG00000000012 SKM-GN TRNSCRPT 2.61487 2.78841
#> 3 <NA> ENSRNOG00000000021 SKM-GN TRNSCRPT 2.17049 1.70091
#> 4 <NA> ENSRNOG00000000024 SKM-GN TRNSCRPT 1.79255 1.26161
#> 5 <NA> ENSRNOG00000000033 SKM-GN TRNSCRPT 5.39651 5.50703
#> 90406015512 90449015512
#> 1 -0.70731 0.13853
#> 2 2.95085 2.46788
#> 3 1.69314 1.90443
#> 4 1.67725 1.66870
#> 5 5.50525 5.57206
df_to_numeric(df)[1:5,1:4]
#> 90560015512 90581015512 90406015512 90449015512
#> ENSRNOG00000000008 0.04044 -0.09760 -0.70731 0.13853
#> ENSRNOG00000000012 2.61487 2.78841 2.95085 2.46788
#> ENSRNOG00000000021 2.17049 1.70091 1.69314 1.90443
#> ENSRNOG00000000024 1.79255 1.26161 1.67725 1.66870
#> ENSRNOG00000000033 5.39651 5.50703 5.50525 5.57206
df = MotrpacRatTraining6moData::METAB_NORM_DATA_FLAT
df[1:5,1:8]
#> feature feature_ID tissue
#> 1 <NA> 1-Methylhistidine SKM-GN
#> 2 METAB;SKM-GN;3-Methylhistidine 3-Methylhistidine SKM-GN
#> 3 <NA> Alanine SKM-GN
#> 4 METAB;SKM-GN;alpha-Amino-N-butyric-acid alpha-Amino-N-butyric-acid SKM-GN
#> 5 METAB;SKM-GN;alpha-Aminoadipic-acid alpha-Aminoadipic-acid SKM-GN
#> assay dataset 10023259 10024735 10025626
#> 1 METAB metab-t-amines -2.021833 -2.079719 -3.436179
#> 2 METAB metab-t-amines -5.701379 -5.434643 -3.936401
#> 3 METAB metab-t-amines 3.187501 3.215838 2.607263
#> 4 METAB metab-t-amines -4.377759 -4.316324 -5.241371
#> 5 METAB metab-t-amines -7.332883 -7.265484 -7.700365
rn = paste(df$assay, df$tissue, df$feature_ID, df$dataset, sep=";")
df_to_numeric(df, rownames = rn)[1:5,1:3]
#> 10023259 10024735
#> METAB;SKM-GN;1-Methylhistidine;metab-t-amines -2.021833 -2.079719
#> METAB;SKM-GN;3-Methylhistidine;metab-t-amines -5.701379 -5.434643
#> METAB;SKM-GN;Alanine;metab-t-amines 3.187501 3.215838
#> METAB;SKM-GN;alpha-Amino-N-butyric-acid;metab-t-amines -4.377759 -4.316324
#> METAB;SKM-GN;alpha-Aminoadipic-acid;metab-t-amines -7.332883 -7.265484
#> 10025626
#> METAB;SKM-GN;1-Methylhistidine;metab-t-amines -3.436179
#> METAB;SKM-GN;3-Methylhistidine;metab-t-amines -3.936401
#> METAB;SKM-GN;Alanine;metab-t-amines 2.607263
#> METAB;SKM-GN;alpha-Amino-N-butyric-acid;metab-t-amines -5.241371
#> METAB;SKM-GN;alpha-Aminoadipic-acid;metab-t-amines -7.700365