co-expression analysis gene pbmc
2022-06-27
## ## Registered S3 method overwritten by 'ggtree':
## method from
## identify.gg ggfun##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
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## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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## expand.grid## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
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## anyMissing, rowMedians## Attaching SeuratObject##
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## intersect, setdiff, setequal, union## Loading required package: shiny## Loading required package: usethispbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/pbmc_human/pbmc_tutorial_final.rds")
pbmc## An object of class Seurat
## 13714 features across 2638 samples within 1 assay
## Active assay: RNA (13714 features, 2000 variable features)
## 2 dimensional reductions calculated: pca, umapWhat if we change the seurat object to sce object.
pbmc.sce <- as.SingleCellExperiment(pbmc)
target <- colData(pbmc.sce)
target <- target$seurat_clustersexprs <- as.data.frame(assay(pbmc.sce, 'logcounts'))Create fcoex object
fc <- new_fcoex(data.frame(exprs),target)## Created new fcoex object.Once you have set up your fcoex object, the first step is to convert a count matrix into a binarized dataframe.
fc <- discretize(fc, number_of_bins = 8)Getting the modules
fc <- find_cbf_modules(fc, verbose = FALSE, is_parallel = FALSE)## Getting SU scores## Running FCBF to find module headers## [1] "Number of prospective features = 113"## Calculating adjacency matrix## Trimming and getting modules from adjacency matrixfc <- get_nets(fc)network_plots <- show_net(fc)
network_plots[["CD79A"]]
network_plots[["HLA-DRB1"]]
gmt_filename <- system.file("extdata", "pathways.gmt", package = "CEMiTool")
if (gmt_filename == "")
{
print("You likely need to install CEMiTool")
} else {
gmt_in <- pathwayPCA::read_gmt(gmt_filename, description = TRUE)
}
fc <- mod_ora(fc, gmt_in)## --> No gene can be mapped....## --> Expected input gene ID: SLC22A2,ABCC9,ABCG1,SLC22A1,RPL27A,vpr## --> return NULL...## Warning in FUN(X[[i]], ...): Enrichment for module PTPRCAP is NULL## --> No gene can be mapped....## --> Expected input gene ID: RPL10A,RPL35A,RPL17,RPS17,ABCC1,RPL19## --> return NULL...## Warning in FUN(X[[i]], ...): Enrichment for module S100A4 is NULLfc <- plot_ora(fc)save_plots(name = "fcoex_vignette", fc, force = TRUE, directory = "./Plots")## $TYROBP## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps##
## $CD3D##
## $S100A8##
## $`HLA-DRB1`##
## $LST1## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps##
## $CST3##
## $CD79A##
## $CST7##
## $FCGR3A
## NULL
##
## $PTPRCAP
## NULL
##
## $S100A4
## NULL
##
## $CD3D
## $CD3D$pl##
## $CD3D$numsig
## [1] 10
##
##
## $CD79A
## $CD79A$pl##
## $CD79A$numsig
## [1] 10
##
##
## $CST3
## $CST3$pl##
## $CST3$numsig
## [1] 3
##
##
## $CST7
## $CST7$pl##
## $CST7$numsig
## [1] 10
##
##
## $FCGR3A
## $FCGR3A$pl##
## $FCGR3A$numsig
## [1] 0
##
##
## $`HLA-DRB1`
## $`HLA-DRB1`$pl##
## $`HLA-DRB1`$numsig
## [1] 10
##
##
## $LST1
## $LST1$pl##
## $LST1$numsig
## [1] 2
##
##
## $S100A8
## $S100A8$pl##
## $S100A8$numsig
## [1] 0
##
##
## $TYROBP
## $TYROBP$pl##
## $TYROBP$numsig
## [1] 0## [[1]]
## png
## 2
##
## [[2]]
## png
## 2Reclustering the cells to find module-based populations
fc <- recluster(fc)## Detecting clusters for the following modules:## [1] "TYROBP"## TYROBP## [1] "CD3D"## CD3D## [1] "S100A8"## S100A8## [1] "HLA-DRB1"## HLA-DRB1## [1] "LST1"## LST1## [1] "CST3"## CST3## [1] "CD79A"## CD79A## [1] "CST7"## CST7## [1] "FCGR3A"## FCGR3A## [1] "PTPRCAP"## PTPRCAP## [1] "S100A4"## S100A4identities_based_on_the_HLA_DRB1_module <- idents(fc)$`HLA-DRB1`
colData(pbmc.sce) <- cbind(colData(pbmc.sce), `mod_HLA_DRB1` = identities_based_on_the_HLA_DRB1_module)
identities_based_on_the__CD79A_module <- idents(fc)$`HLA-DRB1`
colData(pbmc.sce) <- cbind(colData(pbmc.sce), mod_CD79A = idents(fc)$CD79A)pbmc.sce <- make_hexbin(pbmc.sce, nbins = 40, dimension_reduction = "UMAP", use_dims=c(1,2))plot_hexbin_meta(pbmc.sce, col="seurat_clusters", action="majority")
p1 <- plot_hexbin_feature_plus(pbmc.sce,
col="seurat_clusters", type="logcounts",
feature="CD79A", action="mean") +
ggtitle("original clusters (CD79A expression)") +
theme_void()
p2 <- plot_hexbin_feature_plus(pbmc.sce,
col="seurat_clusters", type="logcounts",
feature="HLA-DRB1", action="mean") +
ggtitle("original clusters (HLA-DRB1 expression)") +
theme_void()
plot_grid(p1, p2)
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 2022-06-27
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
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## tweenr 1.0.2 2021-03-23 [1] CRAN (R 4.0.2)
## usethis * 2.1.5 2021-12-09 [1] CRAN (R 4.0.2)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.0.2)
## uwot 0.1.11 2021-12-02 [1] CRAN (R 4.0.2)
## V8 4.2.0 2022-05-14 [1] CRAN (R 4.0.2)
## vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.0.2)
## viridis 0.6.2 2021-10-13 [1] CRAN (R 4.0.2)
## 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)
## xfun 0.30 2022-03-02 [1] CRAN (R 4.0.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.2)
## XVector 0.30.0 2020-10-27 [1] Bioconductor
## yaml 2.2.2 2022-01-25 [1] CRAN (R 4.0.2)
## yulab.utils 0.0.4 2021-10-09 [1] CRAN (R 4.0.2)
## zlibbioc 1.36.0 2020-10-27 [1] Bioconductor
## 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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