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README.Rmd

@@ -26,7 +26,7 @@ Recent technological advances have enabled the simultaneous targeting of multipl
 
 We introduce the fuseMLR package for late integration modeling in R. This package allows users to define studies with multiple layers, data entities, and layer-specific machine learning methods. FuseMLR is user-friendly, enabling the training of different models across layers and automatically conducting meta-analysis once layer-specific training is completed. Additionally, fuseMLR allows for variable selection at the layer level and makes predictions for new data entities.
 
-`fuseMLR` object oriented based on the `R6` package version 2.5.1.
+`fuseMLR` is an object-oriented package based on `R6` version 2.5.1.
 
 ### Installation
 

README.md

@@ -1,3 +1,11 @@
+---
+title: "fuseMLR"
+author: Cesaire J. K. Fouodo
+output: 
+  md_document:
+    variant: gfm
+    preserve_yaml: true
+---
 
 <!-- badges: start -->
 
@@ -38,7 +46,7 @@ and automatically conducting meta-analysis once layer-specific training
 is completed. Additionally, fuseMLR allows for variable selection at the
 layer level and makes predictions for new data entities.
 
-`fuseMLR` object oriented based on the `R6` package version 2.5.1.
+`fuseMLR` is an object-oriented package based on `R6` version 2.5.1.
 
 ### Installation
 
@@ -224,49 +232,49 @@ print(var_sel_res)
 
     ##          Layer        variable
     ## 1     geneexpr           ACACA
-    ## 2     geneexpr            ASNS
-    ## 3     geneexpr            BAP1
-    ## 4     geneexpr           CHEK2
-    ## 5     geneexpr           EIF4E
-    ## 6     geneexpr          MAP2K1
-    ## 7     geneexpr          MAPK14
-    ## 8     geneexpr            PCNA
-    ## 9     geneexpr           YWHAE
-    ## 10 proteinexpr        Bap1.c.4
-    ## 11 proteinexpr             Bid
-    ## 12 proteinexpr       Cyclin_E2
-    ## 13 proteinexpr      P.Cadherin
-    ## 14 proteinexpr            Chk1
-    ## 15 proteinexpr      Chk1_pS345
-    ## 16 proteinexpr            EGFR
-    ## 17 proteinexpr     EGFR_pY1173
-    ## 18 proteinexpr     HER3_pY1289
-    ## 19 proteinexpr           MIG.6
-    ## 20 proteinexpr           ETS.1
-    ## 21 proteinexpr MEK1_pS217_S221
-    ## 22 proteinexpr        p38_MAPK
-    ## 23 proteinexpr    c.Met_pY1235
-    ## 24 proteinexpr           N.Ras
-    ## 25 proteinexpr     PEA15_pS116
-    ## 26 proteinexpr PKC.delta_pS664
-    ## 27 proteinexpr           Rad50
-    ## 28 proteinexpr     C.Raf_pS338
-    ## 29 proteinexpr          p70S6K
-    ## 30 proteinexpr    p70S6K_pT389
-    ## 31 proteinexpr           Smad4
-    ## 32 proteinexpr     STAT3_pY705
-    ## 33 proteinexpr  14.3.3_epsilon
-    ## 34 methylation      cg20139214
-    ## 35 methylation      cg18457775
-    ## 36 methylation      cg24747396
-    ## 37 methylation      cg01306510
-    ## 38 methylation      cg02412050
-    ## 39 methylation      cg07566050
-    ## 40 methylation      cg02630105
-    ## 41 methylation      cg20849549
-    ## 42 methylation      cg25539131
-    ## 43 methylation      cg07064406
-    ## 44 methylation      cg11816577
+    ## 2     geneexpr            BAP1
+    ## 3     geneexpr           CHEK2
+    ## 4     geneexpr           EIF4E
+    ## 5     geneexpr          MAP2K1
+    ## 6     geneexpr          MAPK14
+    ## 7     geneexpr            PCNA
+    ## 8     geneexpr           SMAD4
+    ## 9     geneexpr          SQSTM1
+    ## 10    geneexpr           YWHAE
+    ## 11 proteinexpr        Bap1.c.4
+    ## 12 proteinexpr             Bid
+    ## 13 proteinexpr       Cyclin_E2
+    ## 14 proteinexpr      P.Cadherin
+    ## 15 proteinexpr            Chk1
+    ## 16 proteinexpr      Chk1_pS345
+    ## 17 proteinexpr            EGFR
+    ## 18 proteinexpr     EGFR_pY1173
+    ## 19 proteinexpr     HER3_pY1289
+    ## 20 proteinexpr           MIG.6
+    ## 21 proteinexpr           ETS.1
+    ## 22 proteinexpr MEK1_pS217_S221
+    ## 23 proteinexpr        p38_MAPK
+    ## 24 proteinexpr    c.Met_pY1235
+    ## 25 proteinexpr           N.Ras
+    ## 26 proteinexpr            PCNA
+    ## 27 proteinexpr     PEA15_pS116
+    ## 28 proteinexpr PKC.delta_pS664
+    ## 29 proteinexpr           Rad50
+    ## 30 proteinexpr     C.Raf_pS338
+    ## 31 proteinexpr          p70S6K
+    ## 32 proteinexpr    p70S6K_pT389
+    ## 33 proteinexpr           Smad4
+    ## 34 proteinexpr     STAT3_pY705
+    ## 35 proteinexpr  14.3.3_epsilon
+    ## 36 methylation      cg20139214
+    ## 37 methylation      cg18457775
+    ## 38 methylation      cg01306510
+    ## 39 methylation      cg02412050
+    ## 40 methylation      cg07566050
+    ## 41 methylation      cg02630105
+    ## 42 methylation      cg20849549
+    ## 43 methylation      cg25539131
+    ## 44 methylation      cg07064406
 
 For each layer, the variable selection results show the chosen
 variables. In this example, we perform variable selection on the entire
@@ -371,9 +379,9 @@ print(model_ge)
     ## Layer     : geneexpr
     ## ind. id.  : IDS
     ## target    : disease
-    ## n         : 25
+    ## n         : 30
     ## Missing   : 0
-    ## p         : 10
+    ## p         : 11
 
 #### C) Predicting
 
@@ -422,29 +430,29 @@ print(new_predictions)
     ## 
     ## $predicted_values
     ##          IDS  geneexpr proteinexpr methylation meta_layer
-    ## 1   subject4 0.3486429   0.7254452  0.39821270  0.5065696
-    ## 2   subject7 0.6563234   0.1913813  0.60077143  0.4633997
-    ## 3   subject8 0.7697984   0.8252417  0.72493373  0.7749673
-    ## 4  subject10 0.6770103   0.8271147  0.75742738  0.7609400
-    ## 5  subject13 0.4164071   0.2048258  0.09259405  0.2251955
-    ## 6  subject15 0.5557313   0.9379647  0.36718214  0.6331977
-    ## 7  subject16 0.5701992   0.3528000  0.26069683  0.3817718
-    ## 8  subject18 0.6497238   0.2932206  0.02371310  0.2996893
-    ## 9  subject23 0.6612794   0.2485218  0.60419841  0.4873196
-    ## 10 subject24 0.3336988   0.5391282  0.46320079  0.4552991
-    ## 11 subject27 0.2748909   0.2293552  0.55319563  0.3542913
-    ## 12 subject31 0.4272877   0.8598790  0.59272540  0.6461933
-    ## 13 subject32 0.4388905   0.8694258  0.76771111  0.7136272
-    ## 14 subject35 0.2083437   0.7910008  0.56550437  0.5497232
-    ## 15 subject36 0.6707909   0.1662433  0.56591627  0.4459862
-    ## 16 subject50 0.7561679   0.6132381  0.79458849  0.7160860
-    ## 17 subject54 0.5675159   0.7214897  0.78051508  0.6988200
-    ## 18 subject55 0.5815325   0.2319464  0.58723532  0.4529200
-    ## 19 subject59 0.4597877   0.2770992  0.50990794  0.4089069
-    ## 20 subject62 0.2760032   0.2478115  0.41481548  0.3135595
-    ## 21 subject63 0.5786218   0.8238242  0.89740000  0.7806484
-    ## 22 subject66 0.5528552   0.6825976  0.94541984  0.7373127
-    ## 23 subject70 0.2690111   0.3042032  0.27120873  0.2829184
+    ## 1   subject4 0.4284929   0.6236087  0.54089762  0.5366185
+    ## 2   subject7 0.3945603   0.1347798  0.39843810  0.3031986
+    ## 3   subject8 0.7740583   0.7649012  0.72124563  0.7526983
+    ## 4  subject10 0.8138460   0.7197560  0.68419048  0.7358568
+    ## 5  subject13 0.3990690   0.1749690  0.04975873  0.1994421
+    ## 6  subject15 0.6226012   0.8045405  0.53131865  0.6562456
+    ## 7  subject16 0.7231187   0.2866393  0.18147381  0.3818238
+    ## 8  subject18 0.4848552   0.1877734  0.02350040  0.2208036
+    ## 9  subject23 0.7821425   0.2133167  0.52539960  0.4912287
+    ## 10 subject24 0.3355567   0.6086540  0.55799802  0.5092029
+    ## 11 subject27 0.5103222   0.1461369  0.48094762  0.3703170
+    ## 12 subject31 0.2733171   0.7369151  0.60301230  0.5516776
+    ## 13 subject32 0.6264103   0.7453123  0.67544286  0.6856325
+    ## 14 subject35 0.3754468   0.7400325  0.55803492  0.5680832
+    ## 15 subject36 0.5930313   0.1646988  0.46518294  0.3964029
+    ## 16 subject50 0.7730298   0.5586726  0.83126032  0.7164955
+    ## 17 subject54 0.6100532   0.6796512  0.87089802  0.7242468
+    ## 18 subject55 0.5157575   0.1851198  0.46629841  0.3808416
+    ## 19 subject59 0.4551202   0.2035905  0.46137937  0.3675182
+    ## 20 subject62 0.3280798   0.2101849  0.25384048  0.2605816
+    ## 21 subject63 0.3223774   0.7519175  0.92001825  0.6803795
+    ## 22 subject66 0.7428627   0.6247083  0.98381746  0.7832520
+    ## 23 subject70 0.2834738   0.2386155  0.31945357  0.2797943
 
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