Last updated: 2019-07-26
Checks: 4 3
Knit directory: MAGL/
This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish
to commit the R Markdown file and build the HTML.
The global environment had objects present when the code in the R Markdown file was run. These objects can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment. Use wflow_publish
or wflow_build
to ensure that the code is always run in an empty environment.
The following objects were defined in the global environment when these results were created:
Name | Class | Size |
---|---|---|
data | environment | 56 bytes |
env | environment | 56 bytes |
The command set.seed(20190311)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
To ensure reproducibility of the results, delete the cache directory 4-visualization_cache
and re-run the analysis. To have workflowr automatically delete the cache directory prior to building the file, set delete_cache = TRUE
when running wflow_build()
or wflow_publish()
.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: ._.Renviron
Ignored: ._.gitignore
Ignored: ._README.md
Ignored: ._Snakefile
Ignored: ._config.yaml
Ignored: ._session_info.txt
Ignored: .snakemake/
Ignored: MAGL/.DS_Store
Ignored: MAGL/.RData
Ignored: MAGL/.Rhistory
Ignored: MAGL/._.DS_Store
Ignored: MAGL/._.Rprofile
Ignored: MAGL/._.gitignore
Ignored: MAGL/._data
Ignored: MAGL/analysis/._refs.bib
Ignored: MAGL/analysis/0-preprocessing_cache/
Ignored: MAGL/analysis/1-clustering_cache/
Ignored: MAGL/analysis/3-differential_cache/
Ignored: MAGL/analysis/4-visualization_cache/
Ignored: MAGL/analysis/5-geneset_cache/
Ignored: MAGL/analysis/6-more_cache/
Ignored: MAGL/code/._utils.R
Ignored: MAGL/data/
Ignored: MAGL/output/
Ignored: data/
Ignored: figures/
Ignored: meta/
Ignored: results/
Ignored: scripts/.DS_Store
Ignored: scripts/._.DS_Store
Ignored: scripts/._apply_scdd.R
Ignored: scripts/._plot_perf_by_expr.R
Ignored: scripts/._plot_perf_by_ss.R
Ignored: scripts/._plot_runtimes.R
Ignored: scripts/._prep_magl.R
Ignored: scripts/._prep_sim.R
Ignored: scripts/._session_info.R
Ignored: scripts/._sim_qc.R
Ignored: scripts/._utils.R
Untracked files:
Untracked: .RData
Untracked: .RDataTmp
Untracked: .Rhistory
Untracked: MAGL/code/utils.R
Untracked: MAGL/current
Untracked: figs/
Unstaged changes:
Modified: MAGL/.Rprofile
Modified: MAGL/analysis/4-visualization.Rmd
Modified: MAGL/analysis/5-geneset.Rmd
Deleted: scripts/plot_perf_by_lfc.R
Deleted: scripts/plot_sim_ex.R
Deleted: scripts/prep_data.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
File | Version | Author | Date | Message |
---|---|---|---|---|
html | 033e536 | HelenaLC | 2019-07-11 | update landing page |
Rmd | 08bf260 | HelenaLC | 2019-06-17 | update |
html | 08bf260 | HelenaLC | 2019-06-17 | update |
library(cowplot)
library(ComplexHeatmap)
library(dplyr)
library(ggplot2)
library(muscat)
library(RColorBrewer)
library(purrr)
library(reshape2)
library(scater)
library(SingleCellExperiment)
library(UpSetR)
sce <- readRDS(file.path("output", "MAGL-SCE.rds"))
res <- readRDS(file.path("output", "MAGL-DS_res.rds"))
For easy accession, we store the character vectors of cluster and sample IDs, as well as the number of clusters and samples:
nk <- length(kids <- set_names(levels(sce$cluster_id)))
ns <- length(sids <- set_names(levels(sce$sample_id)))
ng <- length(gids <- set_names(levels(sce$group_id)))
To get a general overview of the differential testing results, we first filter them to retain hits with \(\text{FDR}<5\%\) and \(~|~logFC~|~>1\), and view the number & percentage of differential findings by cluster. Finally, we extract the top hits (lowest adj. p-value) in each cluster.
tbl <- res$table[[1]] %>%
# filter abs(logFC) > 1 & FDR < 0.05
lapply(dplyr::filter, p_adj.loc < 0.05, abs(logFC) > 1) %>%
# sort by FDR
lapply(dplyr::arrange, p_adj.loc)
# nb. & % of hits by cluster
n_de <- vapply(tbl, nrow, numeric(1))
cbind(n_de, perc = round(n_de / nrow(sce) * 100, 2))
n_de perc
Astrocytes 560 5.06
Endothelial 641 5.79
Microglia 319 2.88
Oligodendrocytes 123 1.11
OPC 204 1.84
CPE cells 84 0.76
Excit. Neuron 28 0.25
Inhib. Neuron 9 0.08
deg_by_k <- lapply(tbl, pull, "gene")
upset(fromList(deg_by_k), sets = rev(kids)[-c(1, 2)],
mb.ratio = c(0.6, 0.4), nintersects = 30,
text.scale = 0.8, point.size = 2, line.size = 0.4)
Version | Author | Date |
---|---|---|
08bf260 | HelenaLC | 2019-06-17 |
The scater[@McCarthy2017-scater] packages provides a variety of visualizations for single-cell data. Here, we use plotExpression
to render violin plots of the top differential genes identified for each cluster. We specify x = "sample_id"
to obtain one violin per sample, and colour_by = "group_id"
to signify the experimental condition each sample belongs to.
# split cells by cluster
cs_by_k <- split(colnames(sce), sce$cluster_id)
# get top-hits for ea. cluster
n <- 8
top_gs <- lapply(tbl, function(u) {
n <- ifelse(nrow(u) < n, nrow(u), n)
u$gene[seq_len(n)]
})
for (k in kids) {
cat("## ", k, "\n")
if (length(top_gs[[k]]) != 0)
print(plotExpression(
sce[, cs_by_k[[k]]], features = top_gs[[k]],
x = "sample_id", colour_by = "group_id") +
guides(fill = guide_legend(override.aes = list(size = 2, alpha = 1))) +
theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)))
cat("\n\n")
}
for (dr in c("TSNE", "UMAP")) {
cat("## ", dr, "\n")
df <- data.frame(reducedDim(sce, dr), colData(sce))
labs <- paste(switch(dr, TSNE = "t-SNE", dr), "dim.", 1:2)
p <- ggplot(df, aes_string(x = colnames(df)[1], y = colnames(df)[2])) +
labs(x = labs[1], y = labs[2]) +
theme_void() + theme(aspect.ratio = 1,
legend.key.height = unit(2, "mm"),
legend.position = "bottom")
p1 <- p + geom_point(size = 0.1, alpha = 0.2, aes(col = cluster_id)) +
scale_color_manual(values = CATALYST:::.cluster_cols) +
guides(color = guide_legend(ncol = 3,
override.aes = list(size = 2, alpha = 1)))
p2 <- p + geom_point(size = 0.1, alpha = 0.2, aes(col = group_id)) +
guides(color = guide_legend(ncol = 1,
override.aes = list(size = 2, alpha = 1)))
ps <- plot_grid(
p1 + theme(legend.position = "none"),
p2 + theme(legend.position = "none"))
ls <- plot_grid(get_legend(p1), get_legend(p2), rel_widths = c(4, 1))
print(plot_grid(ps, ls, ncol = 1, rel_heights = c(3, 1)))
cat("\n\n")
}
Registered S3 methods overwritten by 'car':
method from
influence.merMod lme4
cooks.distance.influence.merMod lme4
dfbeta.influence.merMod lme4
dfbetas.influence.merMod lme4
Version | Author | Date |
---|---|---|
08bf260 | HelenaLC | 2019-06-17 |
Version | Author | Date |
---|---|---|
08bf260 | HelenaLC | 2019-06-17 |
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-3.6.0/lib/libRblas.so
LAPACK: /usr/local/R/R-3.6.0/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] UpSetR_1.4.0 scater_1.12.2
[3] SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.0
[5] DelayedArray_0.10.0 BiocParallel_1.18.0
[7] matrixStats_0.54.0 Biobase_2.44.0
[9] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[11] IRanges_2.18.1 S4Vectors_0.22.0
[13] BiocGenerics_0.30.0 reshape2_1.4.3
[15] purrr_0.3.2 RColorBrewer_1.1-2
[17] muscat_0.99.10 ggplot2_3.2.0
[19] dplyr_0.8.3 ComplexHeatmap_2.0.0
[21] cowplot_1.0.0 BiocStyle_2.12.0
loaded via a namespace (and not attached):
[1] tidyr_0.8.3 acepack_1.4.1
[3] bit64_0.9-7 knitr_1.23
[5] irlba_2.3.3 multcomp_1.4-10
[7] data.table_1.12.2 rpart_4.1-15
[9] RCurl_1.95-4.12 doParallel_1.0.14
[11] flowCore_1.50.0 TH.data_1.0-10
[13] RSQLite_2.1.1 future_1.14.0
[15] bit_1.1-14 httpuv_1.5.1
[17] assertthat_0.2.1 viridis_0.5.1
[19] xfun_0.8 hms_0.5.0
[21] evaluate_0.14 promises_1.0.1
[23] DEoptimR_1.0-8 progress_1.2.2
[25] caTools_1.17.1.2 readxl_1.3.1
[27] igraph_1.2.4.1 DBI_1.0.0
[29] geneplotter_1.62.0 CATALYST_1.8.5
[31] htmlwidgets_1.3 backports_1.1.4
[33] annotate_1.62.0 vctrs_0.2.0
[35] abind_1.4-5 withr_2.1.2
[37] grr_0.9.5 robustbase_0.93-5
[39] checkmate_1.9.4 sctransform_0.2.0
[41] prettyunits_1.0.2 scran_1.12.1
[43] cluster_2.1.0 lazyeval_0.2.2
[45] crayon_1.3.4 drc_3.0-1
[47] genefilter_1.66.0 labeling_0.3
[49] edgeR_3.26.5 pkgconfig_2.0.2
[51] nlme_3.1-140 vipor_0.4.5
[53] blme_1.0-4 nnet_7.3-12
[55] rlang_0.4.0 globals_0.12.4
[57] sandwich_2.5-1 rsvd_1.0.1
[59] cellranger_1.1.0 rprojroot_1.3-2
[61] graph_1.62.0 Matrix_1.2-17
[63] carData_3.0-2 boot_1.3-23
[65] zoo_1.8-6 Matrix.utils_0.9.7
[67] base64enc_0.1-3 beeswarm_0.2.3
[69] whisker_0.3-2 ggridges_0.5.1
[71] GlobalOptions_0.1.0 png_0.1-7
[73] viridisLite_0.3.0 rjson_0.2.20
[75] bitops_1.0-6 shinydashboard_0.7.1
[77] ConsensusClusterPlus_1.48.0 KernSmooth_2.23-15
[79] blob_1.2.0 DelayedMatrixStats_1.6.0
[81] workflowr_1.4.0 shape_1.4.4
[83] stringr_1.4.0 scales_1.0.0
[85] memoise_1.1.0 magrittr_1.5
[87] plyr_1.8.4 gplots_3.0.1.1
[89] gdata_2.18.0 zlibbioc_1.30.0
[91] compiler_3.6.0 dqrng_0.2.1
[93] plotrix_3.7-6 clue_0.3-57
[95] lme4_1.1-21 DESeq2_1.24.0
[97] rrcov_1.4-7 XVector_0.24.0
[99] lmerTest_3.1-0 listenv_0.7.0
[101] TMB_1.7.15 htmlTable_1.13.1
[103] Formula_1.2-3 FlowSOM_1.16.0
[105] MASS_7.3-51.4 tidyselect_0.2.5
[107] stringi_1.4.3 forcats_0.4.0
[109] shinyBS_0.61 highr_0.8
[111] yaml_2.2.0 BiocSingular_1.0.0
[113] locfit_1.5-9.1 latticeExtra_0.6-28
[115] ggrepel_0.8.1 tools_3.6.0
[117] future.apply_1.3.0 rio_0.5.16
[119] circlize_0.4.6 rstudioapi_0.10
[121] foreach_1.4.4 foreign_0.8-71
[123] git2r_0.26.1 gridExtra_2.3
[125] Rtsne_0.15 digest_0.6.20
[127] BiocManager_1.30.4 shiny_1.3.2
[129] Rcpp_1.0.1 car_3.0-3
[131] later_0.8.0 httr_1.4.0
[133] AnnotationDbi_1.46.0 colorspace_1.4-1
[135] XML_3.98-1.20 fs_1.3.1
[137] splines_3.6.0 statmod_1.4.32
[139] plotly_4.9.0 xtable_1.8-4
[141] jsonlite_1.6 nloptr_1.2.1
[143] dynamicTreeCut_1.63-1 corpcor_1.6.9
[145] zeallot_0.1.0 R6_2.4.0
[147] Hmisc_4.2-0 mime_0.7
[149] pillar_1.4.2 htmltools_0.3.6
[151] nnls_1.4 glue_1.3.1
[153] minqa_1.2.4 DT_0.7
[155] BiocNeighbors_1.2.0 codetools_0.2-16
[157] tsne_0.1-3 pcaPP_1.9-73
[159] mvtnorm_1.0-11 lattice_0.20-38
[161] tibble_2.1.3 numDeriv_2016.8-1.1
[163] pbkrtest_0.4-7 curl_3.3
[165] ggbeeswarm_0.6.0 colorRamps_2.3
[167] gtools_3.8.1 shinyjs_1.0
[169] zip_2.0.3 openxlsx_4.1.0.1
[171] survival_2.44-1.1 limma_3.40.2
[173] glmmTMB_0.2.3 rmarkdown_1.14
[175] munsell_0.5.0 GetoptLong_0.1.7
[177] GenomeInfoDbData_1.2.1 iterators_1.0.10
[179] variancePartition_1.14.0 haven_2.1.1
[181] gtable_0.3.0