Predicting MCIA global (factor) scores for new test samples

It may be of interest to use the embedding that is calculated on a training sample set to predict scores on a test set (or, equivalently, on new data).

After loading the nipalsMCIA library, we randomly split the NCI60 cancer cell line data into training and test sets.

Installation

# devel version

# install.packages("devtools")
devtools::install_github("Muunraker/nipalsMCIA", ref = "devel",
                         force = TRUE, build_vignettes = TRUE) # devel version
# release version
if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("nipalsMCIA")
library(ggplot2)
library(MultiAssayExperiment)
library(nipalsMCIA)

Split the data

data(NCI60)
set.seed(8)

num_samples <- dim(data_blocks[[1]])[1]
num_train <- round(num_samples * 0.7, 0)
train_samples <- sample.int(num_samples, num_train)

data_blocks_train <- data_blocks
data_blocks_test <- data_blocks

for (i in seq_along(data_blocks)) {
  data_blocks_train[[i]] <- data_blocks_train[[i]][train_samples, ]
  data_blocks_test[[i]] <- data_blocks_test[[i]][-train_samples, ]
}

# Split corresponding metadata
metadata_train <- data.frame(metadata_NCI60[train_samples, ],
                             row.names = rownames(data_blocks_train$mrna))
colnames(metadata_train) <- c("cancerType")

metadata_test <- data.frame(metadata_NCI60[-train_samples, ],
                            row.names = rownames(data_blocks_test$mrna))
colnames(metadata_test) <- c("cancerType")

# Create train and test mae objects
data_blocks_train_mae <- simple_mae(data_blocks_train, row_format = "sample",
                                    colData = metadata_train)
data_blocks_test_mae <- simple_mae(data_blocks_test, row_format = "sample",
                                   colData = metadata_test)

Run nipalsMCIA on training data

MCIA_train <- nipals_multiblock(data_blocks = data_blocks_train_mae,
                                col_preproc_method = "colprofile", num_PCs = 10,
                                plots = "none", tol = 1e-9)

Visualize model on training data using metadata on cancer type

The get_metadata_colors() function returns an assignment of a color for the metadata columns. The nmb_get_gs() function returns the global scores from the input NipalsResult object.

meta_colors <- get_metadata_colors(mcia_results = MCIA_train, color_col = 1,
                                   color_pal_params = list(option = "E"))

global_scores <- nmb_get_gs(MCIA_train)
MCIA_out <- data.frame(global_scores[, 1:2])
MCIA_out$cancerType <- nmb_get_metadata(MCIA_train)$cancerType
colnames(MCIA_out) <- c("Factor.1", "Factor.2", "cancerType")

# plot the results
ggplot(data = MCIA_out, aes(x = Factor.1, y = Factor.2, color = cancerType)) +
  geom_point(size = 3) +
  labs(title = "MCIA for NCI60 training data") +
  scale_color_manual(values = meta_colors) +
  theme_bw()

Generate factor scores for test data using the MCIA_train model

We use the function to generate new factor scores on the test data set using the MCIA_train model. The new dataset in the form of an MAE object is input using the parameter test_data.

MCIA_test_scores <- predict_gs(mcia_results = MCIA_train,
                               test_data = data_blocks_test_mae)

Visualize new scores with old

We once again plot the top two factor scores for both the training and test datasets

MCIA_out_test <- data.frame(MCIA_test_scores[, 1:2])
MCIA_out_test$cancerType <-
  MultiAssayExperiment::colData(data_blocks_test_mae)$cancerType

colnames(MCIA_out_test) <- c("Factor.1", "Factor.2", "cancerType")
MCIA_out_test$set <- "test"
MCIA_out$set <- "train"
MCIA_out_full <- rbind(MCIA_out, MCIA_out_test)
rownames(MCIA_out_full) <- NULL

# plot the results
ggplot(data = MCIA_out_full,
       aes(x = Factor.1, y = Factor.2, color = cancerType, shape = set)) +
  geom_point(size = 3) +
  labs(title = "MCIA for NCI60 training and test data") +
  scale_color_manual(values = meta_colors) +
  theme_bw()

Session Info

Session Info

sessionInfo()
## R version 4.5.1 Patched (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    grid      stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] MultiAssayExperiment_1.35.9 SummarizedExperiment_1.39.2
##  [3] Biobase_2.69.1              GenomicRanges_1.61.5       
##  [5] Seqinfo_0.99.2              IRanges_2.43.5             
##  [7] S4Vectors_0.47.4            BiocGenerics_0.55.1        
##  [9] generics_0.1.4              MatrixGenerics_1.21.0      
## [11] matrixStats_1.5.0           stringr_1.5.2              
## [13] nipalsMCIA_1.7.0            ggpubr_0.6.1               
## [15] ggplot2_4.0.0               fgsea_1.35.8               
## [17] dplyr_1.1.4                 ComplexHeatmap_2.25.2      
## [19] BiocStyle_2.37.1           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1     viridisLite_0.4.2    farver_2.1.2        
##  [4] S7_0.2.0             fastmap_1.2.0        pracma_2.4.4        
##  [7] digest_0.6.37        lifecycle_1.0.4      cluster_2.1.8.1     
## [10] Cairo_1.6-5          magrittr_2.0.4       compiler_4.5.1      
## [13] rlang_1.1.6          sass_0.4.10          tools_4.5.1         
## [16] yaml_2.3.10          data.table_1.17.8    knitr_1.50          
## [19] ggsignif_0.6.4       labeling_0.4.3       S4Arrays_1.9.1      
## [22] DelayedArray_0.35.3  RColorBrewer_1.1-3   abind_1.4-8         
## [25] BiocParallel_1.43.4  withr_3.0.2          purrr_1.1.0         
## [28] colorspace_2.1-2     scales_1.4.0         iterators_1.0.14    
## [31] tinytex_0.57         dichromat_2.0-0.1    cli_3.6.5           
## [34] rmarkdown_2.30       crayon_1.5.3         RSpectra_0.16-2     
## [37] rjson_0.2.23         BiocBaseUtils_1.11.2 cachem_1.1.0        
## [40] parallel_4.5.1       BiocManager_1.30.26  XVector_0.49.1      
## [43] vctrs_0.6.5          Matrix_1.7-4         jsonlite_2.0.0      
## [46] carData_3.0-5        bookdown_0.45        car_3.1-3           
## [49] GetoptLong_1.0.5     rstatix_0.7.2        Formula_1.2-5       
## [52] clue_0.3-66          magick_2.9.0         foreach_1.5.2       
## [55] jquerylib_0.1.4      tidyr_1.3.1          glue_1.8.0          
## [58] codetools_0.2-20     cowplot_1.2.0        stringi_1.8.7       
## [61] shape_1.4.6.1        gtable_0.3.6         tibble_3.3.0        
## [64] pillar_1.11.1        htmltools_0.5.8.1    circlize_0.4.16     
## [67] R6_2.6.1             doParallel_1.0.17    evaluate_1.0.5      
## [70] lattice_0.22-7       png_0.1-8            backports_1.5.0     
## [73] broom_1.0.10         bslib_0.9.0          Rcpp_1.1.0          
## [76] fastmatch_1.1-6      SparseArray_1.9.1    xfun_0.53           
## [79] pkgconfig_2.0.3      GlobalOptions_0.1.2