MOFA analysis for EMBL screen data
Junyan Lu
Last updated: 2022-07-12
Checks: 6 1
Knit directory: EMBL2016/analysis/
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Load libraries and datasets
Load packages
Load datasets
load("../output/screenData.RData")
load("~/CLLproject_jlu/packages/mofaCLL/data/gene.RData")
load("~/CLLproject_jlu/packages/mofaCLL/data/meth.RData")
load("~/CLLproject_jlu/packages/mofaCLL/data/rna.RData")
load("~/CLLproject_jlu/packages/mofaCLL/data/encMap.RData")Prepare single omic datasets
Drug screening
viabMat <- filter(screenData, diagnosis == "CLL") %>%
group_by(patientID, Drug) %>%
summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
ungroup() %>%
pivot_wider(names_from = patientID, values_from = viab) %>%
column_to_rownames("Drug") %>% as.matrix()`summarise()` has grouped output by 'patientID'. You can override using the
`.groups` argument.RNA sequencing
rna.vst<-varianceStabilizingTransformation(rna)
exprMat <- assay(rna.vst)
#filter out low variable genes
nTop = 5000
sds <- genefilter::rowSds(exprMat)
#sh <- genefilter::shorth(sds)
exprMat<-exprMat[order(sds, decreasing = T)[1:nTop],]
colnames(exprMat) <- encMap[match(colnames(exprMat),encMap$encID),]$patientIDDNA methylation array
methData <- assays(meth)[["beta"]]
#use top 5000 most variable probes
nTop = 5000
sds <- genefilter::rowSds(methData)
methData <- methData[order(sds, decreasing = T)[1:nTop],]
colnames(methData) <- encMap[match(colnames(methData),encMap$encID),]$patientIDGenetics
gene <- t(gene)
colnames(gene) <- encMap[match(colnames(gene),encMap$encID),]$patientIDAssemble multiAssayExperiment object
Generate object
mofaData <- list(Drugs = viabMat,
mRNA = exprMat,
Mutations = gene,
Methylation = methData)
# Create MultiAssayExperiment object
mofaData <- MultiAssayExperiment(
experiments = mofaData
)Subset for samples with EMBL2016 screen data
useSamples <- colnames(viabMat)
mofaData <- mofaData[,useSamples]Dimensions for each dataset
experiments(mofaData)ExperimentList class object of length 4:
[1] Drugs: matrix with 408 rows and 132 columns
[2] mRNA: matrix with 5000 rows and 128 columns
[3] Mutations: matrix with 39 rows and 129 columns
[4] Methylation: matrix with 5000 rows and 63 columnsHow many samples have the complete datasets
table(table(sampleMap(mofaData)$primary))
1 3 4
3 67 62 Create MOFA object
MOFAobject <- create_mofa_from_MultiAssayExperiment(mofaData)Plot data overview
plot_data_overview(MOFAobject)
Define MOFA options
Data options
data_opts <- get_default_data_options(MOFAobject)
data_opts$scale_views
[1] FALSE
$scale_groups
[1] FALSE
$center_groups
[1] TRUE
$use_float32
[1] FALSE
$views
[1] "Drugs" "mRNA" "Mutations" "Methylation"
$groups
[1] "group1"Model options
model_opts <- get_default_model_options(MOFAobject)
model_opts$num_factors <- 10
model_opts$likelihoods
Drugs mRNA Mutations Methylation
"gaussian" "gaussian" "gaussian" "gaussian"
$num_factors
[1] 10
$spikeslab_factors
[1] FALSE
$spikeslab_weights
[1] TRUE
$ard_factors
[1] FALSE
$ard_weights
[1] TRUEChange the likely hood of Mutations to “bernoulli
model_opts$likelihoods[["Mutations"]] <- "bernoulli"
model_opts$likelihoods Drugs mRNA Mutations Methylation
"gaussian" "gaussian" "bernoulli" "gaussian" Training options
train_opts <- get_default_training_options(MOFAobject)
train_opts$convergence_mode <- "slow"
train_opts$seed <- 2022
train_opts$maxiter <- 10000
train_opts$maxiter
[1] 10000
$convergence_mode
[1] "slow"
$drop_factor_threshold
[1] -1
$verbose
[1] FALSE
$startELBO
[1] 1
$freqELBO
[1] 5
$stochastic
[1] FALSE
$gpu_mode
[1] FALSE
$seed
[1] 2022
$outfile
NULL
$weight_views
[1] FALSE
$save_interrupted
[1] FALSEChange drop threshold to 0.01
train_opts$drop_factor_threshold <-0.01Train the MOFA model
Prepare MOFA object
MOFAobject <- prepare_mofa(MOFAobject,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts
)Checking data options...Checking training options...Checking model options...Training
MOFAobject <- run_mofa(MOFAobject)
saveRDS(MOFAobject,"../output/mofaRes.rds")Preliminary analysis of the results
MOFAobject <- readRDS("../output/mofaRes.rds")Factor correlation matrix
plot_factor_cor(MOFAobject)
Variance explained
plot_variance_explained(MOFAobject, max_r2=15)
Three factors, F1, F2, F3 and F4, explain variance in the drug
response matrix
Total variance explained
plot_variance_explained(MOFAobject, plot_total = T)[[2]]
Factor heatmap
library(pheatmap)
#gene annotation
facMat <- t(get_factors(MOFAobject)[[1]])
seleGenes <- c("IGHV", "trisomy12")
colAnno <- tibble(Name = colnames(gene),
IGHV = gene["IGHV",],
trisomy12 = gene["trisomy12",]) %>%
column_to_rownames("Name") %>% data.frame()
pheatmap(facMat, clustering_method = "complete", annotation_col = colAnno)
Focus on F1
Factor values
plot_factor(MOFAobject,
factors = 1,
color_by = "IGHV"
)
Weight of genomic features
plot_top_weights(MOFAobject,
view = "Mutations",
factor = 1,
nfeatures = 10, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
F1 is clearly related to IGHV
Focus on F2
Factor 2 values
plot_factor(MOFAobject,
factors = 2,
color_by = "trisomy12"
)
Weight of genomic features
plot_top_weights(MOFAobject,
view = "Mutations",
factor =2,
nfeatures = 10, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
F2 is largely trisomy12
Focus on F3
Factor 3 values
plot_factor(MOFAobject,
factors = 3,
color_by = "Factor3"
)
Weight of genomic features on LF3
plot_top_weights(MOFAobject,
view = "Mutations",
factor =3,
nfeatures = 10, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
Weight of drug features on LF3
plot_weights(MOFAobject,
view = "Drugs",
factor = 3,
nfeatures = 10, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
Associations between F3 and CLL-PD
load("~/CLLproject_jlu/analysis/CLLsubgroup/facTab_methSeqOnly.RData")
comTab <- facTab %>%
mutate(F3 = facMat["Factor3", match(patID, colnames(facMat))])
ggplot(comTab, aes(x=factor, y=F3)) +
geom_point() +
geom_smooth(method = "lm") +
xlab("CLL-PD")`geom_smooth()` using formula 'y ~ x'Warning: Removed 148 rows containing non-finite values (stat_smooth).Warning: Removed 148 rows containing missing values (geom_point).
F3 is basically CLL-PD
Focus on F4
F4 only explains variance from drug response and gene expression view
Weight of drug features on LF3
plot_weights(MOFAobject,
view = "Drugs",
factor = 4,
nfeatures = 20, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
plot_top_weights(MOFAobject,
view = "Drugs",
factor =4,
nfeatures = 20, # Top number of features to highlight
scale = T # Scale weights from -1 to 1
)
Any interesting drugs?
Association tests between drug responses and F4
library(limma)
Attaching package: 'limma'The following object is masked from 'package:DESeq2':
plotMAThe following object is masked from 'package:BiocGenerics':
plotMAf4 <- facMat["Factor4",]
testMat <- viabMat[,names(f4)]
designMat <- model.matrix(~f4)
fit <- lmFit(testMat, designMat)
fit2 <- eBayes(fit)
resTab <- topTable(fit2, number =Inf)Removing intercept from test coefficientsP value histogram
hist(resTab$P.Value)
Results passed 10% FDR
resTab.sig <- filter(resTab, adj.P.Val < 0.1) %>%
as_tibble(rownames = "drugName") %>%
select(drugName, logFC, P.Value, adj.P.Val) %>%
left_join(distinct(drugAnno, drugName, target, pathway))Joining, by = "drugName"resTab.sig %>% mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Scatter plots to visualize top 9 associations
pList <- lapply(seq(9), function(i) {
rec <- resTab.sig[i,]
plotTab <- tibble(patID = colnames(viabMat),
viab = viabMat[rec$drugName,],
F4 = f4,
IGHV = gene["IGHV", match(patID, colnames(gene))])
ggplot(plotTab, aes(x=F4, y=viab)) +
geom_point(aes(col =factor(IGHV))) + geom_smooth(method = "lm") +
ggtitle(rec$drugName)
})
cowplot::plot_grid(plotlist = pList, ncol=3)`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Pathways associated with F4
library(MOFAdata)
data(reactomeGS)
res.positive <- run_enrichment(MOFAobject,
feature.sets = reactomeGS,
view = "mRNA",
sign = "positive"
)Intersecting features names in the model and the gene set annotation results in a total of 4989 features.
Running feature set Enrichment Analysis with the following options...
View: mRNA
Number of feature sets: 556
Set statistic: mean.diff
Statistical test: parametric Subsetting weights with positive sign# GSEA on negative weights, with default options
res.negative <- run_enrichment(MOFAobject,
feature.sets = reactomeGS,
view = "mRNA",
sign = "negative"
)Intersecting features names in the model and the gene set annotation results in a total of 4989 features.
Running feature set Enrichment Analysis with the following options...
View: mRNA
Number of feature sets: 556
Set statistic: mean.diff
Statistical test: parametric Subsetting weights with negative signPositive associations
plot_enrichment(res.positive, factor = 4, max.pathways = 15)
** F4 is perhaps associated with stress response**
Negative associations
plot_enrichment(res.negative, factor = 4, max.pathways = 15)
Does F4 associated with baseline ATP levels?
Baseline ATP levels after 48 hours
load("~/CLLproject_jlu/var/newEMBL_20210129.RData")
basalATP <- emblNew %>% filter(type == "neg") %>%
group_by(patID) %>% summarise(ATPcount = median(val, na.rm=TRUE))plotTab <- tibble(patID = colnames(facMat),
F4 = facMat["Factor4",]) %>%
left_join(basalATP)Joining, by = "patID"ggplot(plotTab, aes(x=F4, y=ATPcount)) + geom_point() +
geom_smooth(method = "lm")`geom_smooth()` using formula 'y ~ x'
F4 is not associated with baseline ATP.
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] MOFAdata_1.12.0 limma_3.52.2
[3] pheatmap_1.0.12 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.9
[7] purrr_0.3.4 readr_2.1.2
[9] tidyr_1.2.0 tibble_3.1.7
[11] ggplot2_3.3.6 tidyverse_1.3.1
[13] MOFA2_1.6.0 MultiAssayExperiment_1.22.0
[15] sva_3.44.0 BiocParallel_1.30.3
[17] genefilter_1.78.0 mgcv_1.8-40
[19] nlme_3.1-158 DESeq2_1.36.0
[21] SummarizedExperiment_1.26.1 Biobase_2.56.0
[23] MatrixGenerics_1.8.0 matrixStats_0.62.0
[25] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[27] IRanges_2.30.0 S4Vectors_0.34.0
[29] BiocGenerics_0.42.0 reticulate_1.25
[31] jyluMisc_0.1.5
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2 tidyselect_1.1.2
[4] RSQLite_2.2.14 AnnotationDbi_1.58.0 htmlwidgets_1.5.4
[7] grid_4.2.0 Rtsne_0.16 maxstat_0.7-25
[10] munsell_0.5.0 codetools_0.2-18 DT_0.23
[13] withr_2.5.0 colorspace_2.0-3 filelock_1.0.2
[16] highr_0.9 knitr_1.39 rstudioapi_0.13
[19] ggsignif_0.6.3 labeling_0.4.2 git2r_0.30.1
[22] slam_0.1-50 GenomeInfoDbData_1.2.8 KMsurv_0.1-5
[25] farver_2.1.0 bit64_4.0.5 rhdf5_2.40.0
[28] rprojroot_2.0.3 basilisk_1.8.0 vctrs_0.4.1
[31] generics_0.1.2 TH.data_1.1-1 xfun_0.31
[34] sets_1.0-21 R6_2.5.1 locfit_1.5-9.5
[37] bitops_1.0-7 rhdf5filters_1.8.0 cachem_1.0.6
[40] fgsea_1.22.0 DelayedArray_0.22.0 assertthat_0.2.1
[43] promises_1.2.0.1 scales_1.2.0 multcomp_1.4-19
[46] gtable_0.3.0 sandwich_3.0-2 workflowr_1.7.0
[49] rlang_1.0.2 splines_4.2.0 rstatix_0.7.0
[52] broom_0.8.0 modelr_0.1.8 yaml_2.3.5
[55] reshape2_1.4.4 abind_1.4-5 crosstalk_1.2.0
[58] backports_1.4.1 httpuv_1.6.5 tools_4.2.0
[61] relations_0.6-12 ellipsis_0.3.2 gplots_3.1.3
[64] jquerylib_0.1.4 RColorBrewer_1.1-3 Rcpp_1.0.8.3
[67] plyr_1.8.7 visNetwork_2.1.0 zlibbioc_1.42.0
[70] RCurl_1.98-1.7 basilisk.utils_1.8.0 ggpubr_0.4.0
[73] cowplot_1.1.1 zoo_1.8-10 haven_2.5.0
[76] ggrepel_0.9.1 cluster_2.1.3 exactRankTests_0.8-35
[79] fs_1.5.2 magrittr_2.0.3 data.table_1.14.2
[82] reprex_2.0.1 survminer_0.4.9 mvtnorm_1.1-3
[85] hms_1.1.1 shinyjs_2.1.0 mime_0.12
[88] evaluate_0.15 xtable_1.8-4 XML_3.99-0.10
[91] readxl_1.4.0 gridExtra_2.3 compiler_4.2.0
[94] KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.2
[97] tzdb_0.3.0 later_1.3.0 geneplotter_1.74.0
[100] lubridate_1.8.0 DBI_1.1.3 corrplot_0.92
[103] dbplyr_2.2.0 MASS_7.3-57 Matrix_1.4-1
[106] car_3.1-0 cli_3.3.0 marray_1.74.0
[109] parallel_4.2.0 igraph_1.3.2 pkgconfig_2.0.3
[112] km.ci_0.5-6 dir.expiry_1.4.0 piano_2.12.0
[115] xml2_1.3.3 annotate_1.74.0 bslib_0.3.1
[118] XVector_0.36.0 drc_3.0-1 rvest_1.0.2
[121] digest_0.6.29 Biostrings_2.64.0 cellranger_1.1.0
[124] rmarkdown_2.14 fastmatch_1.1-3 survMisc_0.5.6
[127] uwot_0.1.11 edgeR_3.38.1 shiny_1.7.1
[130] gtools_3.9.2.2 lifecycle_1.0.1 jsonlite_1.8.0
[133] Rhdf5lib_1.18.2 carData_3.0-5 fansi_1.0.3
[136] pillar_1.7.0 lattice_0.20-45 KEGGREST_1.36.2
[139] fastmap_1.1.0 httr_1.4.3 plotrix_3.8-2
[142] survival_3.3-1 glue_1.6.2 png_0.1-7
[145] bit_4.0.4 stringi_1.7.6 sass_0.4.1
[148] HDF5Array_1.24.1 blob_1.2.3 caTools_1.18.2
[151] memoise_2.0.1