This is a companion vignette briefly describing the advanced capabilites available for comparing, summarizing, and integrating screening contrasts with gCrisprTools, introduced with version 2.0.
Historically, gCrisprTools was focused on making results data.frames, which exhaustively summarize the results of a screen contrast. They look like this:
library(Biobase)
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(gCrisprTools)
knitr::opts_chunk$set(message = FALSE, fig.width = 8, fig.height = 8)
data(resultsDF)
head(resultsDF)
## ID target geneID geneSymbol
## Target1106_a Target1106_a TCAGCCGCTCCTACGGATT 1236 Target1106
## Target1106_c Target1106_c GTGCGCTAAGTCGGCAGAT 1236 Target1106
## Target1106_g Target1106_g TAAACTCTGTCGGGTAGAC 1236 Target1106
## Target1106_e Target1106_e TGGTGATTTCTAGGAAGTT 1236 Target1106
## Target1106_b Target1106_b AGACAAGAGCTATCCTATG 1236 Target1106
## Target1106_d Target1106_d CAACTTCGCCTGTTATCCG 1236 Target1106
## gRNA Log2 Fold Change gRNA Depletion P gRNA Depletion Q
## Target1106_a 7.572398 0.99999 1
## Target1106_c 7.589259 1.00000 1
## Target1106_g 7.642459 1.00000 1
## Target1106_e 7.768537 1.00000 1
## Target1106_b 7.569902 1.00000 1
## Target1106_d 7.654991 1.00000 1
## gRNA Enrichment P gRNA Enrichment Q Target-level Enrichment P
## Target1106_a 6.4548e-06 0.0058481 0
## Target1106_c 2.4731e-06 0.0032638 0
## Target1106_g 3.9303e-06 0.0042285 0
## Target1106_e 2.1136e-06 0.0032638 0
## Target1106_b 2.1912e-06 0.0032638 0
## Target1106_d 2.9287e-06 0.0033610 0
## Target-level Enrichment Q Target-level Depletion P
## Target1106_a 0 1
## Target1106_c 0 1
## Target1106_g 0 1
## Target1106_e 0 1
## Target1106_b 0 1
## Target1106_d 0 1
## Target-level Depletion Q Median log2 Fold Change Rho_enrich
## Target1106_a 1 7.580828 9.047675e-25
## Target1106_c 1 7.580828 9.047675e-25
## Target1106_g 1 7.580828 9.047675e-25
## Target1106_e 1 7.580828 9.047675e-25
## Target1106_b 1 7.580828 9.047675e-25
## Target1106_d 1 7.580828 9.047675e-25
## Rho_deplete
## Target1106_a 1
## Target1106_c 1
## Target1106_g 1
## Target1106_e 1
## Target1106_b 1
## Target1106_d 1
In many applications, there are limitations to these objects. For example:
colnames are human-readable instead of machine readablegeneID and geneSymbol is not clearTo help with downstream analyses, we introduce the simpleResult object
res <- ct.simpleResult(resultsDF, collapse = 'geneSymbol')
head(res)
## geneID geneSymbol Rho_enrich Rho_deplete best.p best.q direction
## Target1106 1236 Target1106 9.047675e-25 1 0 0 enrich
## Target1633 1369 Target1633 7.117493e-24 1 0 0 enrich
## Target1631 51316 Target1631 9.377686e-21 1 0 0 enrich
## Target1352 5526 Target1352 7.906212e-19 1 0 0 enrich
## Target1469 639 Target1469 4.826451e-16 1 0 0 enrich
## Target1256 1050 Target1256 3.101526e-11 1 0 0 enrich
These objects are simpler representations of target signals that are applicable for “most” situations (e.g., when the gRNAs associated with a target have similar effects). They are:
This format enables better comparisons between screens, and we have some helper functions to support this:
# Make "another" result
res2 <- res
res2$best.p <- res2$best.p * runif(nrow(res2))
res2$direction[sample(1:nrow(res), 500)] <- res2$direction[sample(1:nrow(res), 500)]
regularized <- ct.regularizeContrasts(dflist = list('Experiment1' = res[1:1500,],
'Experiment2' = res2[1:1900,]),
collapse = 'geneSymbol')
str(regularized)
## List of 2
## $ Experiment1:'data.frame': 1500 obs. of 7 variables:
## ..$ geneID : chr [1:1500] "10059" "2982" "1294" "5641" ...
## ..$ geneSymbol : chr [1:1500] "NoTarget" "Target10" "Target100" "Target1000" ...
## ..$ Rho_enrich : num [1:1500] 0.81 1 1 1 0.238 ...
## ..$ Rho_deplete: num [1:1500] 0.000367 0.097341 0.014417 0.000897 0.227184 ...
## ..$ best.p : num [1:1500] 0.008 0.312 0.078 0.004 0.162 0.403 0.162 0.022 0.133 0.005 ...
## ..$ best.q : num [1:1500] 0.227 0.965 0.694 0.147 1 ...
## ..$ direction : chr [1:1500] "deplete" "deplete" "deplete" "deplete" ...
## $ Experiment2:'data.frame': 1500 obs. of 7 variables:
## ..$ geneID : chr [1:1500] "10059" "2982" "1294" "5641" ...
## ..$ geneSymbol : chr [1:1500] "NoTarget" "Target10" "Target100" "Target1000" ...
## ..$ Rho_enrich : num [1:1500] 0.81 1 1 1 0.238 ...
## ..$ Rho_deplete: num [1:1500] 0.000367 0.097341 0.014417 0.000897 0.227184 ...
## ..$ best.p : num [1:1500] 0.004677 0.043282 0.003161 0.000862 0.016369 ...
## ..$ best.q : num [1:1500] 0.227 0.965 0.694 0.147 1 ...
## ..$ direction : chr [1:1500] "deplete" "deplete" "deplete" "deplete" ...
The above function takes in a (named) list of results objects and creates a list of simpleResults with the same names that are “in register” with one another, using the shared elements specified by collapse.
The main utility of this function is twofold. First, it enables comparison of lightweight results objects that are derived from screens that could have been executed with different libraries, systems, or technologies by focusing on the targets and their directional significance measures. Second, this “named list of standardized results” can become a standard structure for encoding grouped contrasts for comparison purposes, allowing us to do more sophisticated comparisons between and across contrasts.
The standardized format allows straightforward identification of signal overlaps observed within multiple screens via ct.compareContrasts(). By default, these comparisons are done conditionally, but users may specify more rigid criteria.
comparison <- ct.compareContrasts(dflist = regularized,
statistics = c('best.q', 'best.p'),
cutoffs = c(0.5,0.05),
same.dir = rep(TRUE, length(regularized)))
head(comparison, 30)
## geneID geneSymbol Rho_enrich Rho_deplete best.p best.q
## NoTarget 10059 NoTarget 0.810198260 0.0003666943 0.008 0.2269867
## Target10 2982 Target10 1.000000000 0.0973406417 0.312 0.9651262
## Target100 1294 Target100 1.000000000 0.0144169517 0.078 0.6944937
## Target1000 5641 Target1000 1.000000000 0.0008970760 0.004 0.1467586
## Target1001 323 Target1001 0.238351536 0.2271836123 0.162 1.0000000
## Target1003 11274 Target1003 1.000000000 0.1122383453 0.403 1.0000000
## Target1004 3697 Target1004 0.367507856 0.9733795039 0.162 1.0000000
## Target1005 445 Target1005 1.000000000 0.0067735320 0.022 0.4106667
## Target1006 10874 Target1006 1.000000000 0.0290253030 0.133 0.7877429
## Target1007 2488 Target1007 0.001687259 0.9734058256 0.005 0.3432258
## Target1008 3269 Target1008 0.341020764 0.9926446575 0.223 1.0000000
## Target1009 23114 Target1009 1.000000000 0.0524040002 0.206 0.9113680
## Target101 7398 Target101 0.026185087 0.8184220674 0.043 1.0000000
## Target1014 4057 Target1014 1.000000000 0.2241604775 0.582 1.0000000
## Target1016 2326 Target1016 1.000000000 0.1931948401 0.526 1.0000000
## Target1017 83461 Target1017 0.169090848 0.0428962671 0.115 1.0000000
## Target1019 650 Target1019 1.000000000 0.0353408339 0.150 0.7993234
## Target102 347734 Target102 1.000000000 0.1445274167 0.442 1.0000000
## Target1020 2324 Target1020 1.000000000 0.2345659583 0.574 1.0000000
## Target1021 821 Target1021 0.048472096 0.0031305804 0.022 0.4106667
## Target1022 6533 Target1022 1.000000000 0.2222939519 0.536 1.0000000
## Target1023 6950 Target1023 1.000000000 0.2252680878 0.578 1.0000000
## Target1024 633 Target1024 1.000000000 0.1437704685 0.432 1.0000000
## Target1025 5073 Target1025 0.167112788 0.0901525902 0.134 1.0000000
## Target1026 54468 Target1026 1.000000000 0.2690389752 0.632 1.0000000
## Target1027 9344 Target1027 1.000000000 0.0666379350 0.254 0.9521783
## Target1028 1439 Target1028 0.078493433 0.6994925536 0.073 1.0000000
## Target1029 4481 Target1029 0.007853608 0.9798213353 0.013 0.7476757
## Target103 5167 Target103 1.000000000 0.0379885178 0.167 0.8401324
## Target1030 29106 Target1030 0.089682636 0.2064889045 0.089 1.0000000
## direction replicated
## NoTarget deplete TRUE
## Target10 deplete FALSE
## Target100 deplete FALSE
## Target1000 deplete TRUE
## Target1001 enrich FALSE
## Target1003 deplete FALSE
## Target1004 enrich FALSE
## Target1005 deplete FALSE
## Target1006 deplete FALSE
## Target1007 enrich TRUE
## Target1008 enrich FALSE
## Target1009 deplete FALSE
## Target101 enrich FALSE
## Target1014 deplete FALSE
## Target1016 deplete FALSE
## Target1017 enrich FALSE
## Target1019 deplete FALSE
## Target102 deplete FALSE
## Target1020 deplete FALSE
## Target1021 deplete TRUE
## Target1022 deplete FALSE
## Target1023 deplete FALSE
## Target1024 deplete FALSE
## Target1025 enrich FALSE
## Target1026 deplete FALSE
## Target1027 deplete FALSE
## Target1028 enrich FALSE
## Target1029 enrich FALSE
## Target103 deplete FALSE
## Target1030 enrich FALSE
This function returns a simplifiedResult version of the mainresult argument, appended with a logical column indicating whether signals in the mainresult contrast passing the first significance cutoff are replicated in the validationresult contrast at the second significance cutoff. This sort of conditional scoring was shown previously to be useful in interpreting the results of validation and counterscreens.
For convenience, we can also return summary statistics characterizing the overlap between the screens:
ct.compareContrasts(dflist = regularized,
statistics = c('best.q', 'best.p'),
cutoffs = c(0.5,0.05),
same.dir = rep(TRUE, length(regularized)),
return.stats = TRUE)
## expected observed p
## enrich 3.7487 25 0
## deplete 41.7489 129 0
## all 45.4976 154 0
This can be useful when exploring appropriate cutoff thresholds, or when asking broader questions about overall congruence.
We provide a number of methods for visualizing and contextualizing the overall signals present within sets of screens. One of the simplest is the cointrast barcharts, which represent the number of observed enrichment and depletion signals in each screen according to specified significance criteria:
ct.contrastBarchart(regularized, background = FALSE, statistic = 'best.p')
In the above figure, each contrast is represented as a horizontal bar, and targets enriched and depleted are represented as bars extending to the right and the left of the vertical dotted line, respectively.
We can also compare the signals observed in two screens directly in a scatterplot:
scat <- ct.scatter(regularized,
targets = 'geneSymbol',
statistic = 'best.p',
cutoff = 0.05)
This provides a scatter plot of the indicated statistic, with quadrants defined according to the user-specified cutoff. The number of targets in each of the quadrants is indicated in grey, and quadrants are keyed like this:
1 2 3 4 5 6 7 8 9
A more complete genewise picture can be achieved by examining the returned invisible object, which appends the relevant quadrants to assist in in focusing on particular targets of interest:
head(scat)
## geneID geneSymbol Rho.enrich.Experiment1 Rho.deplete.Experiment1
## NoTarget 10059 NoTarget 0.09140869 3.4356958
## Target10 2982 Target10 0.00000000 1.0117058
## Target100 1294 Target100 0.00000000 1.8411266
## Target1000 5641 Target1000 0.00000000 3.0471708
## Target1001 323 Target1001 0.62278205 0.6436230
## Target1003 11274 Target1003 0.00000000 0.9498587
## p.Experiment1 q.Experiment1 Rho.enrich.Experiment2
## NoTarget 2.0705811 0.594707067 0.09140869
## Target10 0.5051500 0.003309628 0.00000000
## Target100 1.1051303 0.141597698 0.00000000
## Target1000 2.3467875 0.759349249 0.00000000
## Target1001 0.7891466 0.000000000 0.62278205
## Target1003 0.3941565 0.000000000 0.00000000
## Rho.deplete.Experiment2 p.Experiment2 q.Experiment2 quadrant
## NoTarget 3.4356958 2.3248173 0.594707067 7
## Target10 1.0117058 1.3631291 0.003309628 8
## Target100 1.8411266 2.4925088 0.141597698 8
## Target1000 3.0471708 3.0370070 0.759349249 7
## Target1001 0.6436230 1.7844783 0.000000000 8
## Target1003 0.9498587 0.7343638 0.000000000 5
## x y
## NoTarget -2.0705811 -2.3248173
## Target10 -0.5051500 -1.3631291
## Target100 -1.1051303 -2.4925088
## Target1000 -2.3467875 -3.0370070
## Target1001 0.7891466 -1.7844783
## Target1003 -0.3941565 -0.7343638
As an aside, this simplified infrastructure allows straightforward dissection of interaction effects as well. Often, it is helpful to identify targets that are enriched or depleted with respect to one contrast but inactive with respect to another (e.g., to identify genes that impact a screening phenotype but do not deplete over time).
library(Biobase)
library(limma)
library(gCrisprTools)
#Create a complex model design; removing the replicate term for clarity
data("es", package = "gCrisprTools")
data("ann", package = "gCrisprTools")
design <- model.matrix(~ 0 + TREATMENT_NAME, pData(es))
colnames(design) <- gsub('TREATMENT_NAME', '', colnames(design))
contrasts <-makeContrasts(ControlTime = ControlExpansion - ControlReference,
DeathOverTime = DeathExpansion - ControlReference,
Interaction = DeathExpansion - ControlExpansion,
levels = design)
es <- ct.normalizeGuides(es, method = "scale") #See man page for other options
vm <- voom(exprs(es), design)
fit <- lmFit(vm, design)
fit <- contrasts.fit(fit, contrasts)
fit <- eBayes(fit)
allResults <- sapply(colnames(fit$coefficients),
function(x){
ct.generateResults(fit,
annotation = ann,
RRAalphaCutoff = 0.1,
permutations = 1000,
scoring = "combined",
permutation.seed = 2,
contrast.term = x)
}, simplify = FALSE)
allSimple <- ct.regularizeContrasts(allResults)
Using the logical columns we can Identify relevant sets of targets. For example, the number of targets changing over time in both timecourses:
time.effect <- ct.compareContrasts(list("con" = allSimple$ControlTime,
"tx" = allSimple$DeathOverTime))
summary(time.effect$replicated)
## Mode FALSE TRUE
## logical 2012 116
or targets with a toxicity modifying effect that compromise intrinsic viability:
mod.control <- ct.compareContrasts(list("con" = allSimple$ControlTime,
"Interaction" = allSimple$Interaction),
same.dir = c(TRUE, FALSE))
summary(mod.control$replicated)
## Mode FALSE TRUE
## logical 2075 53
Sometimes you might be curious about the relationship between many contrasts. You can accomplish this by making an UpSet plot:
upset <- ct.upSet(allSimple)
In addition to constructing the UpSet plot above, by default we include fold enrichment and p-value estimates to help interpret the various bars in the context of the nominal or conditional expected values.
Finally, the above function returns a combination matrix containing the overlap values and associated targets, which can be useful for interrogating intersection sets of interest.
show(upset)
## A combination matrix with 3 sets and 7 combinations.
## ranges of combination set size: c(21, 165).
## mode for the combination size: conditional.
## sets are on rows.
##
## Combination sets are:
## ControlTime DeathOverTime Interaction code size
## x x x 111 28
## x x 110 116
## x x 101 21
## x x 011 70
## x 100 165
## x 010 110
## x 001 55
##
## Sets are:
## set size
## ControlTime 2128
## DeathOverTime 2128
## Interaction 2128
See the documentation about combination matrices provided in the ComplexHeatmap package for accessor functions and additional information about the structure and use of this object.
Though not subject to specific set-level analyses yet, the ct.seas() function can be likewise extended to use lists of results. The standardized objects can then be consolidated to ask broader questions about enrichment and depletion using standard methods:
genesetdb <- sparrow::getMSigGeneSetDb(collection = 'h', species = 'human', id.type = 'entrez')
sparrowList <- ct.seas(allSimple, gdb = genesetdb)
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (35.12% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (65.07% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (63.99% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
show(sparrowList)
## $ControlTime
## SparrowResult (max FDR by collection set to 0.20%)
## ---------------------------------------------------
## collection method geneset_count sig_count sig_up sig_down
## 1 H ora 50 0 0 0
## 2 H ora.deplete 50 0 0 0
## 3 H ora.enrich 50 0 0 0
## 4 H fgsea 50 0 0 0
##
##
## $DeathOverTime
## SparrowResult (max FDR by collection set to 0.20%)
## ---------------------------------------------------
## collection method geneset_count sig_count sig_up sig_down
## 1 H ora 50 0 0 0
## 2 H ora.deplete 50 0 0 0
## 3 H ora.enrich 50 0 0 0
## 4 H fgsea 50 1 0 1
##
##
## $Interaction
## SparrowResult (max FDR by collection set to 0.20%)
## ---------------------------------------------------
## collection method geneset_count sig_count sig_up sig_down
## 1 H ora 50 0 0 0
## 2 H ora.deplete 50 0 0 0
## 3 H ora.enrich 50 0 0 0
## 4 H fgsea 50 1 0 1
#Can use returned matrices to facilitate downstream comparisons:
plot(-log10(sparrow::results(sparrowList$DeathOverTime, 'fgsea')$padj),
-log10(sparrow::results(sparrowList$Interaction, 'fgsea')$padj),
pch = 19, col = rgb(0,0,0.8,0.5),
ylab = "Pathway -log10(P), Treatment Over Time",
xlab = "Pathway -log10(P), Marginal Time Effect, Treatment Arm",
main = 'Evidence for Pathway Enrichment')
abline(0,1,lty = 2)
sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] limma_3.49.4 gCrisprTools_1.99.3 Biobase_2.53.0
## [4] BiocGenerics_0.39.2
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.19.4 colorspace_2.0-2
## [3] rjson_0.2.20 ellipsis_0.3.2
## [5] circlize_0.4.13 XVector_0.33.0
## [7] GenomicRanges_1.45.0 GlobalOptions_0.1.2
## [9] clue_0.3-60 bit64_4.0.5
## [11] AnnotationDbi_1.55.2 fansi_0.5.0
## [13] RobustRankAggreg_1.1 codetools_0.2-18
## [15] doParallel_1.0.16 cachem_1.0.6
## [17] knitr_1.36 jsonlite_1.7.2
## [19] annotate_1.71.0 cluster_2.1.2
## [21] png_0.1-7 graph_1.71.2
## [23] msigdbr_7.4.1 compiler_4.1.1
## [25] httr_1.4.2 backports_1.2.1
## [27] assertthat_0.2.1 Matrix_1.3-4
## [29] fastmap_1.1.0 ontologyIndex_2.7
## [31] lazyeval_0.2.2 htmltools_0.5.2
## [33] tools_4.1.1 gtable_0.3.0
## [35] glue_1.4.2 GenomeInfoDbData_1.2.7
## [37] dplyr_1.0.7 fastmatch_1.1-3
## [39] Rcpp_1.0.7 vctrs_0.3.8
## [41] Biostrings_2.61.2 babelgene_21.4
## [43] iterators_1.0.13 xfun_0.27
## [45] stringr_1.4.0 lifecycle_1.0.1
## [47] irlba_2.3.3 XML_3.99-0.8
## [49] edgeR_3.35.3 zlibbioc_1.39.0
## [51] scales_1.1.1 MatrixGenerics_1.5.4
## [53] parallel_4.1.1 SummarizedExperiment_1.23.5
## [55] RColorBrewer_1.1-2 ComplexHeatmap_2.9.4
## [57] memoise_2.0.0 gridExtra_2.3
## [59] ggplot2_3.3.5 stringi_1.7.5
## [61] RSQLite_2.2.8 highr_0.9
## [63] S4Vectors_0.31.5 BiocIO_1.3.0
## [65] foreach_1.5.1 checkmate_2.0.0
## [67] BiocParallel_1.27.17 shape_1.4.6
## [69] GenomeInfoDb_1.29.10 rlang_0.4.12
## [71] pkgconfig_2.0.3 matrixStats_0.61.0
## [73] bitops_1.0-7 evaluate_0.14
## [75] lattice_0.20-45 purrr_0.3.4
## [77] htmlwidgets_1.5.4 bit_4.0.4
## [79] tidyselect_1.1.1 GSEABase_1.55.2
## [81] plyr_1.8.6 magrittr_2.0.1
## [83] R6_2.5.1 snow_0.4-3
## [85] IRanges_2.27.2 magick_2.7.3
## [87] generics_0.1.0 DelayedArray_0.19.4
## [89] DBI_1.1.1 pillar_1.6.4
## [91] KEGGREST_1.33.0 RCurl_1.98-1.5
## [93] tibble_3.1.5 crayon_1.4.1
## [95] BiocSet_1.7.2 utf8_1.2.2
## [97] plotly_4.10.0 rmarkdown_2.11
## [99] viridis_0.6.2 GetoptLong_1.0.5
## [101] locfit_1.5-9.4 grid_4.1.1
## [103] data.table_1.14.2 blob_1.2.2
## [105] digest_0.6.28 xtable_1.8-4
## [107] tidyr_1.1.4 stats4_4.1.1
## [109] munsell_0.5.0 viridisLite_0.4.0
## [111] BiasedUrn_1.07 sparrow_0.99.6