Last updated: 2021-12-08
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Knit directory: cacoaAnalysis/
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| Rmd | 91efca9 | viktor_petukhov | 2021-12-08 | MS report |
library(cacoa)
library(dataorganizer)
library(ggplot2)
library(dplyr)
library(cowplot)
library(Matrix)
library(dplyr)
library(magrittr)
library(readr)
library(org.Hs.eg.db)
theme_set(theme_bw())cao <- read_rds(DataPath("MS/cao.rds")) %>% Cacoa$new()
cao$plot.params <- list(size=0.1, alpha=0.1, font.size=c(2, 3))Dependency on metadata
lapply(c("coda", "expression.shifts"), function(sp) {
cao$plotSampleDistances(space=sp, legend.position=c(1, 1))
}) %>%
plot_grid(plotlist=., ncol=2, labels=c("CoDA", "Expression"), hjust=0, label_x=0.02, label_y=0.99)
sample_meta <- cao$data.object$misc$sample_metadata
lapply(c("coda", "expression.shifts"), function(sp) {
smd <- as.data.frame(sample_meta) %>% dplyr::select(-sample, -diagnosis)
sep.res <- cao$estimateMetadataSeparation(smd, space=sp, dist="l1", name=paste0("md.", sp),
show.warning=FALSE)
(-log10(sep.res$padjust)) %>% {tibble(Type=names(.), value=.)} %>%
cacoa:::plotMeanMedValuesPerCellType(type="bar", yline=-log10(0.05),
ylab="-log10(separation p-value)") +
scale_y_continuous(expand=c(0, 0, 0.05, 0)) +
scale_fill_manual(values=rep("#2b8cbe", length(sample_meta))) +
theme(axis.title.y=element_text(size=13))
}) %>%
plot_grid(plotlist=., ncol=2, labels=c("CoDA", "Expression"), hjust=0, label_x=0.2, label_y=0.98)
There is significant separation by Capbatch, Seqbatch and stage. Capbatch and Seqbatch are very similar, so we show only the former.
lapply(c('coda', 'expression'), function(sp) {
lapply(c("Seqbatch", "stage"), function(n) {
cao$plotSampleDistances(space=sp, legend.position=c(0, 1), sample.colors=sample_meta[[n]])
}) %>% plot_grid(plotlist=., nrow=1)
}) %>% plot_grid(plotlist=., ncol=1)
CoDA space (top row) has even stronger separation by batch than expression space.
Compositional differences
Cluster-based
cao$plotCellLoadings()
cao$plotContrastTree()
We can see that most neuronal subtypes go to the left branch of the tree, while all glial and immune types go to the right.
Cluster-free
g0 <- cao$plotEmbedding(color.by='cell.groups')
g0
KDE-based:
cao$plotCellDensity(name='cell.density.kde') %>% plot_grid(plotlist=., ncol=2)
Differential cell density:
plot_grid(g0, cao$plotDiffCellDensity(name='cell.density.kde', legend.position=c(0, 1)), ncol=2)
Graph-based:
cao$plotCellDensity(name='cell.density.graph') %>% plot_grid(plotlist=., ncol=2)
Differential cell density:
plot_grid(g0, cao$plotDiffCellDensity(name='cell.density.graph', legend.position=c(0, 1)), ncol=2)
Here, graph-based method shows higher sensitivity, and its results mostly match to the cluster-based version. It also detects that in Astrocytes only one subtype is affected.
Expression differences
Cluster-based
All genes:
cao$plotExpressionShiftMagnitudes()
No significant changes detected.
Top DE genes:
cao$estimateExpressionShiftMagnitudes(n.permutations=5000, top.n.genes=500, n.pcs=8,
min.samp.per.type=4, name='es.top.de', verbose=FALSE)cao$plotExpressionShiftMagnitudes(name='es.top.de')
Number of DE genes:
cao$estimateDEPerCellType(
independent.filtering=TRUE, test='DESeq2.Wald', verbose=FALSE, resampling.method='fix.samples',
fix.n.samples=6, n.cells.subsample=30, name='de.fix.samples', n.resamplings=50
)cao$plotNumberOfDEGenes(
name="de.fix.samples", type="box", show.resampling.results=TRUE, jitter.alpha=0.5,
show.jitter=TRUE, y.offset=1
) + scale_y_log10(labels=c(0, 10, 100), breaks=c(1, 11, 101), expand=c(0, 0), limits=c(1, 300))
Cluster-free
cao$plotClusterFreeExpressionShifts(legend.position=c(0, 1), font.size=c(1, 2))
Functional interpretation
cao$plotVolcano(xlim=c(-3, 3), ylim=c(0, 3.5), lf.cutoff=1)
cao$plotOntologyHeatmapCollapsed(name="GSEA", genes="all", n=30, clust.method="ward.D")Loading required package: DOSEDOSE v3.16.0 For help: https://guangchuangyu.github.io/software/DOSE
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
cao$estimateGenePrograms(method="leiden", z.adj=TRUE, smooth=FALSE)cao$plotGeneProgramScores(
legend.position=c(0, 1), plot.na=FALSE,
adj.list=theme(legend.key.width=unit(8, "pt"), legend.key.height=unit(12, "pt"))
)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.3/lib/R/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.3/lib/R/lib/libRlapack.so
locale:
[1] C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DOSE_3.16.0 org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0
[4] IRanges_2.24.1 S4Vectors_0.28.1 Biobase_2.50.0
[7] BiocGenerics_0.36.1 readr_1.4.0 magrittr_2.0.1
[10] cowplot_1.1.1 dplyr_1.0.7 ggplot2_3.3.5
[13] dataorganizer_0.1.0 cacoa_0.2.0 Matrix_1.2-18
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] N2R_0.1.1 circlize_0.4.13 fastmatch_1.1-0
[4] plyr_1.8.6 igraph_1.2.6 lazyeval_0.2.2
[7] splines_4.0.3 BiocParallel_1.24.1 urltools_1.7.3
[10] digest_0.6.28 foreach_1.5.1 htmltools_0.5.2
[13] GOSemSim_2.16.1 GO.db_3.12.1 fansi_0.5.0
[16] RMTstat_0.3 memoise_2.0.0 cluster_2.1.0
[19] doParallel_1.0.16 ComplexHeatmap_2.9.4 extrafont_0.17
[22] matrixStats_0.61.0 R.utils_2.10.1 extrafontdb_1.0
[25] sccore_1.0.0 colorspace_2.0-2 blob_1.2.2
[28] ggrepel_0.9.1 pagoda2_1.0.7 xfun_0.26
[31] crayon_1.4.1 brew_1.0-6 iterators_1.0.13
[34] ape_5.5 glue_1.4.2 gtable_0.3.0
[37] GetoptLong_1.0.5 proj4_1.0-10.1 leidenAlg_0.1.0
[40] Rook_1.1-1 Rttf2pt1_1.3.8 shape_1.4.6
[43] maps_3.3.0 abind_1.4-5 scales_1.1.1
[46] DBI_1.1.1 Rcpp_1.0.7 tmvnsim_1.0-2
[49] clue_0.3-59 tidytree_0.3.4 bit_4.0.4
[52] fgsea_1.16.0 RColorBrewer_1.1-2 ellipsis_0.3.2
[55] pkgconfig_2.0.3 R.methodsS3_1.8.1 farver_2.1.0
[58] utf8_1.2.2 tidyselect_1.1.1 labeling_0.4.2
[61] rlang_0.4.11 reshape2_1.4.4 later_1.1.0.1
[64] munsell_0.5.0 tools_4.0.3 cachem_1.0.6
[67] generics_0.1.0 RSQLite_2.2.8 evaluate_0.14
[70] stringr_1.4.0 fastmap_1.1.0 ggdendro_0.1.22
[73] yaml_2.2.1 knitr_1.36 bit64_4.0.5
[76] fs_1.5.0 purrr_0.3.4 nlme_3.1-149
[79] whisker_0.4 ash_1.0-15 ggrastr_1.0.0
[82] R.oo_1.24.0 grr_0.9.5 DO.db_2.9
[85] compiler_4.0.3 beeswarm_0.4.0 png_0.1-7
[88] tibble_3.1.5 stringi_1.7.5 highr_0.9
[91] drat_0.1.8 ggalt_0.4.0 lattice_0.20-41
[94] psych_2.1.6 vctrs_0.3.8 pillar_1.6.3
[97] lifecycle_1.0.1 triebeard_0.3.0 jquerylib_0.1.4
[100] GlobalOptions_0.1.2 data.table_1.14.2 irlba_2.3.3
[103] Matrix.utils_0.9.8 qvalue_2.22.0 httpuv_1.5.4
[106] conos_1.4.4 R6_2.5.1 promises_1.1.1
[109] KernSmooth_2.23-17 gridExtra_2.3 vipor_0.4.5
[112] codetools_0.2-16 MASS_7.3-53 assertthat_0.2.1
[115] rprojroot_2.0.2 rjson_0.2.20 withr_2.4.2
[118] mnormt_2.0.2 EnhancedVolcano_1.8.0 mgcv_1.8-33
[121] hms_1.1.1 grid_4.0.3 tidyr_1.1.4
[124] rmarkdown_2.11 dendsort_0.3.3 Rtsne_0.15
[127] git2r_0.27.1 ggbeeswarm_0.6.0