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 versionSplit 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
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.
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
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 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=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: 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_1.36.0 SummarizedExperiment_1.40.0
## [3] Biobase_2.70.0 GenomicRanges_1.62.0
## [5] Seqinfo_1.0.0 IRanges_2.44.0
## [7] S4Vectors_0.48.0 BiocGenerics_0.56.0
## [9] generics_0.1.4 MatrixGenerics_1.22.0
## [11] matrixStats_1.5.0 stringr_1.6.0
## [13] nipalsMCIA_1.8.0 ggpubr_0.6.2
## [15] ggplot2_4.0.1 fgsea_1.36.0
## [17] dplyr_1.1.4 ComplexHeatmap_2.26.0
## [19] BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2
## [4] S7_0.2.1 fastmap_1.2.0 pracma_2.4.6
## [7] digest_0.6.38 lifecycle_1.0.4 cluster_2.1.8.1
## [10] magrittr_2.0.4 compiler_4.5.2 rlang_1.1.6
## [13] sass_0.4.10 tools_4.5.2 yaml_2.3.10
## [16] data.table_1.17.8 knitr_1.50 ggsignif_0.6.4
## [19] labeling_0.4.3 S4Arrays_1.10.0 DelayedArray_0.36.0
## [22] RColorBrewer_1.1-3 abind_1.4-8 BiocParallel_1.44.0
## [25] withr_3.0.2 purrr_1.2.0 sys_3.4.3
## [28] colorspace_2.1-2 scales_1.4.0 iterators_1.0.14
## [31] cli_3.6.5 rmarkdown_2.30 crayon_1.5.3
## [34] RSpectra_0.16-2 rjson_0.2.23 BiocBaseUtils_1.12.0
## [37] cachem_1.1.0 parallel_4.5.2 XVector_0.50.0
## [40] BiocManager_1.30.27 vctrs_0.6.5 Matrix_1.7-4
## [43] jsonlite_2.0.0 carData_3.0-5 car_3.1-3
## [46] GetoptLong_1.0.5 rstatix_0.7.3 Formula_1.2-5
## [49] clue_0.3-66 maketools_1.3.2 foreach_1.5.2
## [52] tidyr_1.3.1 jquerylib_0.1.4 glue_1.8.0
## [55] codetools_0.2-20 cowplot_1.2.0 stringi_1.8.7
## [58] shape_1.4.6.1 gtable_0.3.6 tibble_3.3.0
## [61] pillar_1.11.1 htmltools_0.5.8.1 circlize_0.4.16
## [64] R6_2.6.1 doParallel_1.0.17 evaluate_1.0.5
## [67] lattice_0.22-7 png_0.1-8 backports_1.5.0
## [70] broom_1.0.10 bslib_0.9.0 Rcpp_1.1.0
## [73] fastmatch_1.1-6 SparseArray_1.10.1 xfun_0.54
## [76] buildtools_1.0.0 pkgconfig_2.0.3 GlobalOptions_0.1.2