imirage.RdFunction to impute miRNA expression profile from protein coding expression dataset
imirage(train_pcg, train_mir, my_pcg, gene_index, method = "KNN", num = 50, target = "none", ...)
| train_pcg | training protein coding dataset. a numeric matrix with with row names indicating samples, and column names indicating protein coding gene IDs. |
|---|---|
| train_mir | training miRNA expression dataset. a numeric matrix with row names indicating samples, and column names indicating miRNA IDs. |
| my_pcg | test protein coding expression dataset. a numeric matrix with row names indicating samples, and column names indicating protein coding gene IDs. |
| gene_index | either gene name (character) or index (column number) of miRNA to be imputed. |
| method | method for imputation, either "RF" for random forests, "KNN" for K-nearest neighbor or "SVM" for support vector machines. Uses KNN by default. |
| num | number of informative protein coding genes to be used in constructing imputation model. Default is 50 genes. |
| target | "none" (default), "ts.pairs", or dataframe/matrix/list. this argument accepts character strings to indicate the use of all candidate genes as predictors ("none), or use built-in TargetScan miRNA-gene pairs ("ts.pairs"). also accepts a dataframe , matrix or list object containing a column with names of miRNA and a column with the names of target genes. |
| ... | optional parameters that can be passed on to the machine-learning method: RF (randomForest), KNN (knn.reg) or SVM(svm) |
a numeric vector with imputed expression levels of the miRNA
data(iMIRAGE.datasets) imirage(GA.pcg, GA.mir, HS.pcg, gene_index="hsa-let-7c", method="KNN", num=50)#> [1] 2019.3563 1498.5680 1771.0472 2525.3074 3406.9625 1813.6320 3009.6051 #> [8] 2198.6933 3072.8239 1307.7639 2962.8586 1277.5310 2258.6073 2110.7417 #> [15] 5194.3184 2963.7463 1765.5263 3669.1882 1952.3878 3368.4370 1316.7190 #> [22] 2017.3545 1671.8867 5657.6180 1037.8135 3214.5502 5665.0500 1815.9127 #> [29] 2612.9871 2567.6086 2341.5214 2787.6283 2312.1913 1230.4309 2020.4602 #> [36] 969.7257 1145.1208 958.9258 1043.2435 1181.7525 1092.2694 2585.8381 #> [43] 4762.9124 1914.3931 1659.7709 2286.6204 1590.2248 987.3592 1146.6056 #> [50] 1086.8198imirage(GA.pcg, GA.mir, HS.pcg, gene_index=25, method="KNN", num=50)#> [1] 493.5657 423.0274 425.6345 675.1162 874.6808 425.8818 764.3591 #> [8] 496.1472 772.2783 349.8015 710.3138 363.3681 570.3462 429.7653 #> [15] 1275.3751 761.7610 429.0182 924.8438 488.8303 889.6338 323.1427 #> [22] 497.5615 341.6435 1649.1141 275.6821 812.9858 1655.2335 459.7502 #> [29] 643.1109 685.3456 550.1771 672.6437 556.0599 339.5751 455.9140 #> [36] 265.0739 304.1595 204.7350 287.0775 300.1527 252.7089 604.5987 #> [43] 1492.3055 508.2830 387.6598 616.8432 425.9734 211.1994 349.9980 #> [50] 280.1517