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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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_1.31.5 SummarizedExperiment_1.35.5
## [3] Biobase_2.67.0 GenomicRanges_1.57.2
## [5] GenomeInfoDb_1.41.2 IRanges_2.39.2
## [7] S4Vectors_0.43.2 BiocGenerics_0.53.0
## [9] MatrixGenerics_1.17.1 matrixStats_1.4.1
## [11] stringr_1.5.1 nipalsMCIA_1.5.0
## [13] ggpubr_0.6.0 ggplot2_3.5.1
## [15] fgsea_1.31.6 dplyr_1.1.4
## [17] ComplexHeatmap_2.23.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] rlang_1.1.4 magrittr_2.0.3 clue_0.3-65
## [4] GetoptLong_1.0.5 compiler_4.4.1 png_0.1-8
## [7] vctrs_0.6.5 pkgconfig_2.0.3 shape_1.4.6.1
## [10] crayon_1.5.3 fastmap_1.2.0 backports_1.5.0
## [13] XVector_0.45.0 labeling_0.4.3 utf8_1.2.4
## [16] rmarkdown_2.28 pracma_2.4.4 UCSC.utils_1.1.0
## [19] purrr_1.0.2 xfun_0.48 zlibbioc_1.51.2
## [22] cachem_1.1.0 jsonlite_1.8.9 highr_0.11
## [25] DelayedArray_0.33.1 BiocParallel_1.41.0 broom_1.0.7
## [28] parallel_4.4.1 cluster_2.1.6 R6_2.5.1
## [31] stringi_1.8.4 bslib_0.8.0 RColorBrewer_1.1-3
## [34] car_3.1-3 jquerylib_0.1.4 Rcpp_1.0.13
## [37] iterators_1.0.14 knitr_1.48 BiocBaseUtils_1.9.0
## [40] Matrix_1.7-1 tidyselect_1.2.1 abind_1.4-8
## [43] yaml_2.3.10 doParallel_1.0.17 codetools_0.2-20
## [46] lattice_0.22-6 tibble_3.2.1 withr_3.0.2
## [49] evaluate_1.0.1 circlize_0.4.16 pillar_1.9.0
## [52] BiocManager_1.30.25 carData_3.0-5 foreach_1.5.2
## [55] generics_0.1.3 munsell_0.5.1 scales_1.3.0
## [58] glue_1.8.0 maketools_1.3.1 tools_4.4.1
## [61] sys_3.4.3 data.table_1.16.2 RSpectra_0.16-2
## [64] ggsignif_0.6.4 buildtools_1.0.0 fastmatch_1.1-4
## [67] cowplot_1.1.3 tidyr_1.3.1 colorspace_2.1-1
## [70] GenomeInfoDbData_1.2.13 Formula_1.2-5 cli_3.6.3
## [73] fansi_1.0.6 viridisLite_0.4.2 S4Arrays_1.5.11
## [76] gtable_0.3.6 rstatix_0.7.2 sass_0.4.9
## [79] digest_0.6.37 SparseArray_1.5.45 farver_2.1.2
## [82] rjson_0.2.23 htmltools_0.5.8.1 lifecycle_1.0.4
## [85] httr_1.4.7 GlobalOptions_0.1.2