Predicting New MCIA scores
Compiled: May 01, 2024
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 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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_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: 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_1.30.0 SummarizedExperiment_1.34.0
## [3] Biobase_2.64.0 GenomicRanges_1.56.0
## [5] GenomeInfoDb_1.40.0 IRanges_2.38.0
## [7] S4Vectors_0.42.0 BiocGenerics_0.50.0
## [9] MatrixGenerics_1.16.0 matrixStats_1.3.0
## [11] Seurat_5.0.3 SeuratObject_5.0.1
## [13] sp_2.1-4 piggyback_0.1.5
## [15] BiocFileCache_2.12.0 dbplyr_2.5.0
## [17] stringr_1.5.1 nipalsMCIA_1.2.0
## [19] ggpubr_0.6.0 ggplot2_3.5.1
## [21] fgsea_1.30.0 dplyr_1.1.4
## [23] ComplexHeatmap_2.20.0 BiocStyle_2.32.0
##
## 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_1.50.0
## [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
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