Author: Max MattessichJoaquin ReynaEdel AronAnna Konstorum

# Predicting New MCIA scores

# 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 4.6.0 (2026-04-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    grid      stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] MultiAssayExperiment_1.39.0 SummarizedExperiment_1.43.0
    ##  [3] Biobase_2.73.1              GenomicRanges_1.65.0
    ##  [5] Seqinfo_1.3.0               IRanges_2.47.2
    ##  [7] S4Vectors_0.51.3            BiocGenerics_0.59.6
    ##  [9] generics_0.1.4              MatrixGenerics_1.25.0
    ## [11] matrixStats_1.5.0           stringr_1.6.0
    ## [13] nipalsMCIA_1.11.0           ggpubr_0.6.3
    ## [15] ggplot2_4.0.3               fgsea_1.39.0
    ## [17] dplyr_1.2.1                 ComplexHeatmap_2.29.0
    ## [19] BiocStyle_2.41.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1     viridisLite_0.4.3    farver_2.1.2
    ##  [4] S7_0.2.2             fastmap_1.2.0        pracma_2.4.6
    ##  [7] digest_0.6.39        lifecycle_1.0.5      cluster_2.1.8.2
    ## [10] magrittr_2.0.5       compiler_4.6.0       rlang_1.2.0
    ## [13] sass_0.4.10          tools_4.6.0          yaml_2.3.12
    ## [16] data.table_1.18.4    knitr_1.51           ggsignif_0.6.4
    ## [19] labeling_0.4.3       S4Arrays_1.13.0      DelayedArray_0.39.3
    ## [22] RColorBrewer_1.1-3   abind_1.4-8          BiocParallel_1.47.0
    ## [25] withr_3.0.2          purrr_1.2.2          sys_3.4.3
    ## [28] colorspace_2.1-2     scales_1.4.0         iterators_1.0.14
    ## [31] cli_3.6.6            rmarkdown_2.31       crayon_1.5.3
    ## [34] otel_0.2.0           RSpectra_0.16-2      rjson_0.2.23
    ## [37] BiocBaseUtils_1.15.1 cachem_1.1.0         parallel_4.6.0
    ## [40] XVector_0.53.0       BiocManager_1.30.27  vctrs_0.7.3
    ## [43] Matrix_1.7-5         jsonlite_2.0.0       carData_3.0-6
    ## [46] car_3.1-5            GetoptLong_1.1.1     rstatix_0.7.3
    ## [49] Formula_1.2-5        clue_0.3-68          maketools_1.3.2
    ## [52] foreach_1.5.2        tidyr_1.3.2          jquerylib_0.1.4
    ## [55] glue_1.8.1           codetools_0.2-20     cowplot_1.2.0
    ## [58] stringi_1.8.7        shape_1.4.6.1        gtable_0.3.6
    ## [61] tibble_3.3.1         pillar_1.11.1        htmltools_0.5.9
    ## [64] circlize_0.4.18      R6_2.6.1             doParallel_1.0.17
    ## [67] evaluate_1.0.5       lattice_0.22-9       png_0.1-9
    ## [70] backports_1.5.1      broom_1.0.13         bslib_0.11.0
    ## [73] Rcpp_1.1.1-1.1       fastmatch_1.1-8      SparseArray_1.13.2
    ## [76] xfun_0.57            buildtools_1.0.0     pkgconfig_2.0.3
    ## [79] GlobalOptions_0.1.4