InterCellDB Seurat object
Ehsan.Rz
2022-06-16
library(InterCellDB)## library(Seurat)## Attaching SeuratObjectlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(devtools)## Loading required package: usethislibrary(GO.db)## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:dplyr':
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## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:dplyr':
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## first, rename## The following object is masked from 'package:base':
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## expand.grid##
## Attaching package: 'IRanges'## The following objects are masked from 'package:dplyr':
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## collapse, desc, slice##
## Attaching package: 'AnnotationDbi'## The following object is masked from 'package:dplyr':
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## selectlibrary(future) ##
## Attaching package: 'future'## The following object is masked from 'package:IRanges':
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## values## The following object is masked from 'package:S4Vectors':
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## valuesseurat.obj <- readRDS("/mnt/nectar_volume/home/eraz0001/ggplot/seurat.object.rds")all.markers <- FindAllMarkers(seurat.obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)## Calculating cluster cDC2## Calculating cluster NK## Calculating cluster Myeloid## Calculating cluster migDC## Calculating cluster LN T cell## Calculating cluster Tumor T cell## Calculating cluster GD/MAIT## Calculating cluster cDC1## Calculating cluster B cell## Calculating cluster pDC## Calculating cluster Endo tumor## Calculating cluster Endo lymphatic## Calculating cluster Endo LN## Calculating cluster CAF 3## Calculating cluster CAF 1## Calculating cluster CAF 2## Calculating cluster Fibroblast LNall.markers %>% group_by(cluster) %>% top_n(n=15, wt=avg_log2FC) -> top15colnames(top15)[2] <- "LogFC"
colnames(top15)[5] <- "PVal"
head(top15)## # A tibble: 6 × 7
## # Groups: cluster [1]
## p_val LogFC pct.1 pct.2 PVal cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 2.44e-234 2.53 0.99 0.293 4.70e-230 cDC2 Plbd1
## 2 1.13e-219 4.23 0.642 0.099 2.18e-215 cDC2 Cd209a
## 3 7.17e-196 2.61 0.956 0.359 1.38e-191 cDC2 H2-DMb1
## 4 3.89e-188 2.57 0.838 0.239 7.52e-184 cDC2 Cfp
## 5 2.09e-181 2.81 0.984 0.499 4.03e-177 cDC2 H2-Eb1
## 6 4.17e-181 2.23 0.94 0.362 8.05e-177 cDC2 Napsatmp.counts <- seurat.obj[["RNA"]]@datacolnames(tmp.counts) <- as.character(Idents(seurat.obj))tmp.counts[1:5, 1:5]## 5 x 5 sparse Matrix of class "dgCMatrix"
## cDC2 NK NK cDC2 Myeloid
## Gnai3 . 1.4526836 0.7893417 0.02189368 .
## Cdc45 0.1342404 0.1345867 . 1.01913912 0.1698247
## Scml2 . . . . .
## Apoh . . . . .
## Narf . . . 0.02189368 .inter.obj <- CreateInterCellObject(top15, species = "mouse", add.exprs = TRUE, exprs.data = tmp.counts)
#saveRDS(inter.obj, file = "/mnt/nectar_volume/home/eraz0001/ggplot/inter.obj.rds")Step 2: Customize interactions and proteins from databases
inter.obj <- SelectDBSubset(inter.obj,
use.exp = TRUE,
exp.score.range = c(1, 1000),
use.know = TRUE,
know.score.range = c(1, 1000),
use.pred = TRUE,
pred.score.range = c(1, 1000),
sel.action.mode = "ALL",
sel.action.effect = "ALL")## [1] "Change reference database."object
A InterCellDBPack or InterCell object. SelectDBSubset.default uses
InterCellDBPack as input.
… Parameters passed to function SelectDBSubset.default.
combined.score.range
The combined score from all evidence sources, which works on the whole
database.
use.exp It adds the data whose evidence is experimentally validatd.
exp.score.range It controls the score range when selecting subset by experimentally validatd evidence, i.e. when use.exp = TRUE. The score should be 2 numbers within 1~1000.
use.know
It adds the data whose evidence is pathway curated.
know.score.range
It controls the score range when selecting subset by pathway curated
evidence, i.e. when use.know = TRUE. The score should be 2 numbers
within 1~1000.
use.pred
It adds the data whose evidence is predicted.
pred.score.range
It controls the score range when selecting subset by predicted evidence,
i.e. when use.pred = TRUE. The score should be 2 numbers within
1~1000.
sel.physical
It selects the subset of gene pairs whose corresponding protein pairs
are physical associated. This parameter is identical to set
sel.action.mode = “binding” for now due to the database limitation. It
may change in future.
sel.action.mode Selection by action mode. “ALL” means not use this to select subset. Other options will be directly select gene pair in that action mode. Supported options are listed in kpred.action.mode.
sel.action.effect
Selection by action effect. “ALL” means not use this to select subset.
Other options will be directly select gene pair in that action effect.
Supported options are listed in kpred.action.effect.
sel.action.merge.option Either ‘intersect’ or ‘union’. The option for merging the result from selection on action mode and action effect.
action.score.range
It controls the score range when selecting subset from action databases.
The score should be 2 numbers within 1~1000.
slim.along.with.pairs
This decides whether to select the corresponding subset of action pair
database after selecting subset of gene pair database.
genes.receiver <- FetchGeneOI(inter.obj, sel.location = c("Plasma Membrane", "Extracellular Region", "Cytoskeleton", "Cytosol"), sel.location.score = c(4:5), sel.type = "Receptor", sel.go.terms = "GO:0006955")## [1] "Fetch 161 genes of interest."object
A InterCell object or InterCellDBPack object. FetchGeneOI.default gets
InterCellDBPack as input.
… Parameters passed to function FetchGeneOI.default.
sel.location
Use subcellular location of gene product to select gene, and options are
listed in ListAllGeneLocation.
sel.location.score
The score of corresponding subcellular location, range from 1 to 5.
Consider score = c(4, 5) as high and common usage.
sel.type
Use molecular function of gene product to select gene, and options are
listed in ListAllGeneType.
sel.merge.type
The merged types. Comparing to sel.type, it has less options, which are
given in ListAllGeneMergeType. See details for help.
ret.with.property
It decides whether the return values are attached with gene
properties(location, type, etc).
sel.go.terms
Use GO terms to select gene, and supported options are listed in
ListAllGeneGOTerm.
go.use.relative Decide if the go terms in the related GO term tree are used. See details for help.
go.relative.option
Decide which relation to the selected GO term is used. Options are
‘ancestor’, ‘parents’, ‘offspring’, ‘children’. See details for
help.
genes.sender <- FetchGeneOI(inter.obj, sel.location = "Extracellular Region", sel.location.score = c(4:5), sel.go.terms = "GO:0006955")## [1] "Fetch 278 genes of interest."Step 3: Perform full network analysis
plan("multiprocess", workers = 2)## Warning: Strategy 'multiprocess' is deprecated in future (>= 1.20.0). Instead,
## explicitly specify either 'multisession' or 'multicore'. In the current R
## session, 'multiprocess' equals 'multisession'.## Warning in supportsMulticoreAndRStudio(...): [ONE-TIME WARNING] Forked
## processing ('multicore') is not supported when running R from RStudio
## because it is considered unstable. For more details, how to control forked
## processing or not, and how to silence this warning in future R sessions, see ?
## parallelly::supportsMulticoretmp.permlist <- Tool.GenPermutation(inter.obj, tmp.counts, perm.times = 100)
plan("sequential")plan("multiprocess", workers = 4)
inter.obj <- AnalyzeInterInFullView(inter.obj,
sel.some.genes.X = genes.receiver,
sel.some.genes.Y = genes.sender,
sel.exprs.change = "Xup.Yup",
run.permutation = TRUE,
perm.expression = tmp.permlist,
perm.pval.sides = "one-side-large",
perm.pval.cutoff = 0.05)
plan("sequential")object
A InterCell object, which is created by CreateInterCellObject.
sel.clusters.X
Defining one part of interacting clusters. The options can be found from
ListAllClusters. See details for help.
sel.clusters.Y
Defining the other part of interacting clusters. The options can be
found from ListAllClusters. See details for help.
sel.exprs.change
Options are ‘Xup.Yup’, ‘Xup.Ydn’, ‘Xdn.Yup’, ‘Xdn.Ydn’. It gives the
corresponding expression change of every interacting gene pair.
sel.some.genes.X
It gives the genes to be expected to be expressed in clusters given by
parameter sel.clusters.X. It can either be return by FetchGeneOI, or
just character though losing some gene properties.
sel.some.genes.Y
It gives the genes to be expected to be expressed in clusters given by
parameter sel.clusters.Y. It can be generated as paramter
sel.some.genes.X.
sel.genes.option
Options are ‘intersect’ or ‘union’. ‘intersect’ strictly restricts gene
pair to have one gene partner in sel.some.genes.X and the other in
sel.some.genes.Y. ‘union’ restricts gene pair to have at least one gene
either in sel.some.genes.X or sel.some.genes.Y.
sel.gene.pairs
Directly specify the desired gene pairs. It should be given in standard
table that is generated by FormatCustomGenePairs. To note, it’s strictly
aligned to clusters, see details.
run.permutation It decides whether to permutation test to distinguish significant changed gene pairs with the rest. If set TRUE, parameter perm.expression, perm.pval.sides and perm.pval.cutoff should be provided. If set FALSE, no statistical test is used, which means all gene pairs will be passed to result.
perm.expression A list of permutations of gene expression. It should be generated by function Tool.GenPermutation().
perm.pval.sides Default is ‘two-sides’. All allowed options are ‘one-side-large’ or ‘one-side-small’ or ‘two-sides’. Explicit explanation are given in details.
perm.pval.cutoff
The P value cutoff for
force.process
It stops the program when no subset of genes are selected either by
sel.some.genes.X and sel.some.genes.Y or by sel.gene.pairs, which may
take long time to process. To force process, set this to TRUE.
verbose If set TRUE, the progress bar will be shown.
torm.LN.clusters <- c("LN T cell", "Endo lymphatic", "Endo LN", "Fibroblast LN")
all.clusters <- ListAllClusters(inter.obj)
used.clusters <- setdiff(all.clusters, torm.LN.clusters)
used.clusters <- used.clusters[order(used.clusters)]
fullview.result <- GetResultFullView(inter.obj,
show.clusters.in.x = used.clusters,
show.clusters.in.y = used.clusters,
plot.axis.x.name = "signal receiving cells",
plot.axis.y.name = "signal sending cells",
nodes.size.range = c(1,8))## [1] "Reading clusters shown in X-axis: B cell, CAF 1, CAF 2, CAF 3, cDC1, cDC2, Endo tumor, GD/MAIT, migDC, Myeloid, NK, pDC, Tumor T cell."
## [1] "Reading clusters shown in Y-axis: B cell, CAF 1, CAF 2, CAF 3, cDC1, cDC2, Endo tumor, GD/MAIT, migDC, Myeloid, NK, pDC, Tumor T cell."Tool.ShowGraph(fullview.result)
Tool.WriteTables(fullview.result, dir.path = "./")Step 4: Perform intercellular analysis
inter.obj <- FetchInterOI(inter.obj, cluster.x = "GD/MAIT", cluster.y = "Myeloid")## [1] "Using 11 gene pairs."
## [1] "Generating from GD/MAIT and Myeloid."Summarize action mode and action effect: Then, we can show the composition of action mode for all gene pairs. Here, we use 6 action modes.
inter.obj <- AnalyzeInterInAction(inter.obj)used.action.mode <- c("activation", "inhibition", "catalysis", "reaction", "expression", "ptmod")
used.color.mode <- c("#FB8072", "#80B1D3", "#8DD3C7", "#FFFFB3", "#BEBADA", "#FDB462")
action.mode.result <- GetResultPieActionMode(inter.obj, limits.exprs.change = c("Xup.Yup"), limits.action.mode = used.action.mode, color.action.mode = used.color.mode)
Tool.ShowGraph(action.mode.result)
action.effect.result <- GetResultPieActionEffect(inter.obj, limits.exprs.change = c("Xup.Yup"))x<- Tool.ShowGraph(action.effect.result)
x## NULL4.2 Subset and rank gene pairs
inter.obj <- SelectInterSubset(inter.obj,
sel.action.mode = "activation",
sel.action.effect = "positive",
sel.action.merge.option = "union"
)result.inter.pairs <- GetResultTgCrosstalk(inter.obj,
func.to.use = list(
Power = function(x) { log1p(x) }, # further log-transform the power
Confidence = function(x) { 1/(1+x) } # invert the confidence to show
),
axis.order.xy = c("Power", "Power"), # order genes by power
axis.order.xy.decreasing = c(TRUE, TRUE), # select if ordering is decreasing
nodes.size.range = c(1, 8))
Tool.ShowGraph(result.inter.pairs)
We rank gene pairs by their power, and select those top ranked
genes.
inter.obj <- SelectInterSubset(inter.obj,
sel.some.genes.X = c("Cxcr6"),
sel.some.genes.Y = c("Ccl6", "Ccl9")
)4.3 Evaluate the specificity of gene pairs
tmp.target.cluster.groups <- ListClusterGroups(inter.obj,
use.former = TRUE,
cluster.former = c("Myeloid"))
tmp.target.cluster.groups <- setdiff(tmp.target.cluster.groups, c("Myeloid~Myeloid", "Myeloid~Endo LN", "Myeloid~Endo lymphatic", "Myeloid~Fibroblast LN", "Myeloid~LN T cell"))inter.obj <- AnalyzeInterSpecificity(inter.obj,
to.cmp.cluster.groups = tmp.target.cluster.groups
)
result.specificity <- GetResultTgSpecificity(inter.obj,
sel.uq.cnt.options = seq_along(tmp.target.cluster.groups),
plot.uq.cnt.merged = TRUE,
plot.name.X.to.Y = FALSE,
func.to.use = list(Power = function(x) { log1p(x) },
Confidence = function(x) { 1/(1+x) }),
dot.size.range = c(2,8),
dot.colour.seq = c("#00809D", "#EEEEEE", "#C30000"),
dot.colour.value.seq = c(0, 0.4, 1)
)## Warning in GetResultTgSpecificity(inter.obj, sel.uq.cnt.options =
## seq_along(tmp.target.cluster.groups), : Select not-existed uq.cnt: 2, 3, 4, 5,
## 6, 7, 8, 9, 10, 11, 12, which will be ignored!Tool.ShowGraph(result.specificity)
Summarize gene pairs in spatial pattern
tmp.hide.locations <- setdiff(ListAllGeneLocation(inter.obj), c("Plasma Membrane", "Extracellular Region"))
set.seed(101L)
result.sptialpattern <- GetResultTgCellPlot(inter.obj,
area.extend.times = 20,
hide.other.area = TRUE,
hide.locations.X = tmp.hide.locations,
hide.locations.Y = tmp.hide.locations,
expand.gap.radius.list = list(ECM = 8, CTP = 2, NC = 2, OTHER = 2),
link.size = 0.3,
link.alpha = 0.8,
legend.manual.left.spacing = grid::unit(0.1, "cm")
)## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.## Warning in if (class(legend.manual.internal.spacing) != "unit") {: the condition
## has length > 1 and only the first element will be used## Warning in GetResultTgCellPlot(inter.obj, area.extend.times = 20,
## hide.other.area = TRUE, : Given spacing is not class:unit. Auto change to unit
## in 'cm'.Tool.ShowGraph(result.sptialpattern)
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-06-17
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] CRAN (R 4.0.2)
## AnnotationDbi * 1.52.0 2020-10-27 [1] Bioconductor
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
## Biobase * 2.50.0 2020-10-27 [1] Bioconductor
## BiocGenerics * 0.36.1 2021-04-16 [1] Bioconductor
## bit 4.0.4 2020-08-04 [1] CRAN (R 4.0.2)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.0.2)
## blob 1.2.2 2021-07-23 [1] CRAN (R 4.0.2)
## brio 1.1.3 2021-11-30 [1] CRAN (R 4.0.2)
## bslib 0.3.1 2021-10-06 [1] CRAN (R 4.0.2)
## cachem 1.0.6 2021-08-19 [1] CRAN (R 4.0.2)
## callr 3.7.0 2021-04-20 [1] CRAN (R 4.0.2)
## cli 3.2.0 2022-02-14 [1] CRAN (R 4.0.2)
## cluster 2.1.3 2022-03-28 [1] CRAN (R 4.0.2)
## codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.2)
## colorspace 2.0-2 2021-06-24 [1] CRAN (R 4.0.2)
## cowplot 1.1.1 2020-12-30 [1] CRAN (R 4.0.2)
## crayon 1.4.2 2021-10-29 [1] CRAN (R 4.0.2)
## data.table 1.14.2 2021-09-27 [1] CRAN (R 4.0.2)
## DBI 1.1.2 2021-12-20 [1] CRAN (R 4.0.2)
## deldir 1.0-6 2021-10-23 [1] CRAN (R 4.0.2)
## desc 1.4.0 2021-09-28 [1] CRAN (R 4.0.2)
## devtools * 2.4.3 2021-11-30 [1] CRAN (R 4.0.2)
## digest 0.6.29 2021-12-01 [1] CRAN (R 4.0.2)
## dplyr * 1.0.8 2022-02-08 [1] CRAN (R 4.0.2)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.2)
## evaluate 0.15 2022-02-18 [1] CRAN (R 4.0.2)
## fansi 1.0.2 2022-01-14 [1] CRAN (R 4.0.2)
## farver 2.1.0 2021-02-28 [1] CRAN (R 4.0.2)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.2)
## fitdistrplus 1.1-6 2021-09-28 [1] CRAN (R 4.0.2)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.0.2)
## future * 1.23.0 2021-10-31 [1] CRAN (R 4.0.2)
## future.apply 1.8.1 2021-08-10 [1] CRAN (R 4.0.2)
## generics 0.1.2 2022-01-31 [1] CRAN (R 4.0.2)
## ggplot2 3.3.6 2022-05-03 [1] CRAN (R 4.0.2)
## ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.0.2)
## ggridges 0.5.3 2021-01-08 [1] CRAN (R 4.0.2)
## globals 0.14.0 2020-11-22 [1] CRAN (R 4.0.2)
## glue 1.6.1 2022-01-22 [1] CRAN (R 4.0.2)
## GO.db * 3.12.1 2022-06-16 [1] Bioconductor
## goftest 1.2-3 2021-10-07 [1] CRAN (R 4.0.2)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.2)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
## highr 0.9 2021-04-16 [1] CRAN (R 4.0.2)
## hms 1.1.1 2021-09-26 [1] CRAN (R 4.0.2)
## htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.0.2)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.0.2)
## httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.0.2)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
## ica 1.0-2 2018-05-24 [1] CRAN (R 4.0.2)
## igraph 1.2.11 2022-01-04 [1] CRAN (R 4.0.2)
## InterCellDB * 0.9.2 2022-06-16 [1] Github (ZJUDBlab/InterCellDB@3a0d4c8)
## IRanges * 2.24.1 2020-12-12 [1] Bioconductor
## irlba 2.3.5 2021-12-06 [1] CRAN (R 4.0.2)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2)
## jsonlite 1.7.3 2022-01-17 [1] CRAN (R 4.0.2)
## KernSmooth 2.23-17 2020-04-26 [2] CRAN (R 4.0.2)
## knitr 1.39 2022-04-26 [1] CRAN (R 4.0.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.0.2)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.0.2)
## lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.2)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.0.2)
## leiden 0.3.9 2021-07-27 [1] CRAN (R 4.0.2)
## lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.0.2)
## limma 3.46.0 2020-10-27 [1] Bioconductor
## listenv 0.8.0 2019-12-05 [1] CRAN (R 4.0.2)
## lmtest 0.9-39 2021-11-07 [1] CRAN (R 4.0.2)
## magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.0.2)
## MASS 7.3-51.6 2020-04-26 [2] CRAN (R 4.0.2)
## Matrix 1.4-0 2021-12-08 [1] CRAN (R 4.0.2)
## matrixStats 0.61.0 2021-09-17 [1] CRAN (R 4.0.2)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2)
## mgcv 1.8-31 2019-11-09 [2] CRAN (R 4.0.2)
## mime 0.12 2021-09-28 [1] CRAN (R 4.0.2)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.0.2)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
## nlme 3.1-148 2020-05-24 [2] CRAN (R 4.0.2)
## parallelly 1.30.0 2021-12-17 [1] CRAN (R 4.0.2)
## patchwork 1.1.1 2020-12-17 [1] CRAN (R 4.0.2)
## pbapply 1.5-0 2021-09-16 [1] CRAN (R 4.0.2)
## pillar 1.7.0 2022-02-01 [1] CRAN (R 4.0.2)
## pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.0.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
## pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.0.2)
## plotly 4.10.0 2021-10-09 [1] CRAN (R 4.0.2)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.0.2)
## png 0.1-7 2013-12-03 [1] CRAN (R 4.0.2)
## polyclip 1.10-0 2019-03-14 [1] CRAN (R 4.0.2)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
## processx 3.5.2 2021-04-30 [1] CRAN (R 4.0.2)
## progress 1.2.2 2019-05-16 [1] CRAN (R 4.0.2)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.0.2)
## ps 1.6.0 2021-02-28 [1] CRAN (R 4.0.2)
## purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.0.2)
## RANN 2.6.1 2019-01-08 [1] CRAN (R 4.0.2)
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## Rcpp 1.0.8 2022-01-13 [1] CRAN (R 4.0.2)
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## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.0.2)
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## viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.0.2)
## withr 2.4.3 2021-11-30 [1] CRAN (R 4.0.2)
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## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.2)
## yaml 2.2.2 2022-01-25 [1] CRAN (R 4.0.2)
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.0.2)
##
## [1] /mnt/nectar_volume/home/eraz0001/R/x86_64-pc-linux-gnu-library/4.0
## [2] /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/library
##
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