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.
# 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)
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)
MCIA_train <- nipals_multiblock(data_blocks = data_blocks_train_mae,
col_preproc_method = "colprofile", num_PCs = 10,
plots = "none", tol = 1e-9)
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()
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)
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
sessionInfo()
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_1.31.0 SummarizedExperiment_1.35.0
## [3] Biobase_2.65.0 GenomicRanges_1.57.0
## [5] GenomeInfoDb_1.41.0 IRanges_2.39.0
## [7] S4Vectors_0.43.0 BiocGenerics_0.51.0
## [9] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [11] Seurat_5.0.3 SeuratObject_5.0.1
## [13] sp_2.1-4 piggyback_0.1.5
## [15] BiocFileCache_2.13.0 dbplyr_2.5.0
## [17] stringr_1.5.1 nipalsMCIA_1.3.0
## [19] ggpubr_0.6.0 ggplot2_3.5.1
## [21] fgsea_1.31.0 dplyr_1.1.4
## [23] ComplexHeatmap_2.21.0 BiocStyle_2.33.0
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.4.0 later_1.3.2
## [4] filelock_1.0.3 tibble_3.2.1 polyclip_1.10-6
## [7] fastDummies_1.7.3 httr2_1.0.1 lifecycle_1.0.4
## [10] rstatix_0.7.2 doParallel_1.0.17 globals_0.16.3
## [13] lattice_0.22-6 MASS_7.3-60.2 backports_1.4.1
## [16] magrittr_2.0.3 plotly_4.10.4 sass_0.4.9
## [19] rmarkdown_2.26 jquerylib_0.1.4 yaml_2.3.8
## [22] httpuv_1.6.15 sctransform_0.4.1 spam_2.10-0
## [25] spatstat.sparse_3.0-3 reticulate_1.36.1 cowplot_1.1.3
## [28] pbapply_1.7-2 DBI_1.2.2 RColorBrewer_1.1-3
## [31] lubridate_1.9.3 abind_1.4-5 zlibbioc_1.51.0
## [34] Rtsne_0.17 purrr_1.0.2 pracma_2.4.4
## [37] rappdirs_0.3.3 circlize_0.4.16 GenomeInfoDbData_1.2.12
## [40] ggrepel_0.9.5 irlba_2.3.5.1 gitcreds_0.1.2
## [43] spatstat.utils_3.0-4 listenv_0.9.1 goftest_1.2-3
## [46] RSpectra_0.16-1 spatstat.random_3.2-3 fitdistrplus_1.1-11
## [49] parallelly_1.37.1 leiden_0.4.3.1 codetools_0.2-20
## [52] DelayedArray_0.31.0 tidyselect_1.2.1 shape_1.4.6.1
## [55] UCSC.utils_1.1.0 farver_2.1.1 spatstat.explore_3.2-7
## [58] jsonlite_1.8.8 GetoptLong_1.0.5 progressr_0.14.0
## [61] ggridges_0.5.6 survival_3.6-4 iterators_1.0.14
## [64] foreach_1.5.2 tools_4.4.0 ica_1.0-3
## [67] Rcpp_1.0.12 glue_1.7.0 gridExtra_2.3
## [70] SparseArray_1.5.0 BiocBaseUtils_1.7.0 xfun_0.43
## [73] withr_3.0.0 BiocManager_1.30.22 fastmap_1.1.1
## [76] fansi_1.0.6 digest_0.6.35 timechange_0.3.0
## [79] R6_2.5.1 mime_0.12 colorspace_2.1-0
## [82] scattermore_1.2 Cairo_1.6-2 tensor_1.5
## [85] spatstat.data_3.0-4 RSQLite_2.3.6 utf8_1.2.4
## [88] tidyr_1.3.1 generics_0.1.3 data.table_1.15.4
## [91] htmlwidgets_1.6.4 httr_1.4.7 S4Arrays_1.5.0
## [94] uwot_0.2.2 pkgconfig_2.0.3 gtable_0.3.5
## [97] blob_1.2.4 lmtest_0.9-40 XVector_0.45.0
## [100] htmltools_0.5.8.1 carData_3.0-5 dotCall64_1.1-1
## [103] bookdown_0.39 clue_0.3-65 scales_1.3.0
## [106] png_0.1-8 knitr_1.46 reshape2_1.4.4
## [109] rjson_0.2.21 nlme_3.1-164 curl_5.2.1
## [112] cachem_1.0.8 zoo_1.8-12 GlobalOptions_0.1.2
## [115] KernSmooth_2.23-22 vipor_0.4.7 parallel_4.4.0
## [118] miniUI_0.1.1.1 ggrastr_1.0.2 pillar_1.9.0
## [121] vctrs_0.6.5 RANN_2.6.1 promises_1.3.0
## [124] car_3.1-2 xtable_1.8-4 cluster_2.1.6
## [127] beeswarm_0.4.0 evaluate_0.23 tinytex_0.50
## [130] magick_2.8.3 cli_3.6.2 compiler_4.4.0
## [133] rlang_1.1.3 crayon_1.5.2 future.apply_1.11.2
## [136] ggsignif_0.6.4 labeling_0.4.3 ggbeeswarm_0.7.2
## [139] plyr_1.8.9 stringi_1.8.3 deldir_2.0-4
## [142] viridisLite_0.4.2 BiocParallel_1.39.0 munsell_0.5.1
## [145] gh_1.4.1 lazyeval_0.2.2 spatstat.geom_3.2-9
## [148] Matrix_1.7-0 RcppHNSW_0.6.0 patchwork_1.2.0
## [151] bit64_4.0.5 future_1.33.2 shiny_1.8.1.1
## [154] highr_0.10 ROCR_1.0-11 igraph_2.0.3
## [157] broom_1.0.5 memoise_2.0.1 bslib_0.7.0
## [160] fastmatch_1.1-4 bit_4.0.5