Last updated: 2021-02-04
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Knit directory: melanoma_publication_old_data/
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knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())First, we will load the libraries needed for this part of the analysis.
library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(fpc)sce = readRDS(file = "data/sce_RNA.rds")# Subset sce object to only contain chemokine producing cells
cur_sce <- sce[,sce$chemokine_cluster != 0]
# define fractions of chemokines present in community
cur_dt <- as.data.table(colData(cur_sce))
cur_dt <- cbind(cur_dt[,chemokine_cluster], cur_dt[,grepl(glob2rx("C*L*"),names(cur_dt)), with=FALSE])
colnames(cur_dt)[1] <- "chemokine_cluster"
cur_dt <- cur_dt %>%
group_by(chemokine_cluster) %>%
summarise_each(funs(sum))Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
Please use `across()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# wide format of frequencies
freqs_wide <- t(scale(t(as.matrix(cur_dt[,-1])), center = FALSE,
scale = rowSums(cur_dt[,-1])))
freqs_wide <- cbind(cur_dt[,1],freqs_wide)
# long format of frequencies
freqs_long <- melt(as.data.table(freqs_wide), id = "chemokine_cluster")
colnames(freqs_long) <- c("chemokine_cluster", "celltype", "fraction")# clustering (based on chemokine abundance within a cluster)
estimate <- clusterboot(data=freqs_wide[,-1], B=100,
bootmethod = c("boot"),
distances = FALSE,
krange = 2:20,
clustermethod = kmeansCBI,
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boot 100 # add cluster ID to frequencies
freq_kmeans <- as.data.table(cbind(estimate$result$partition, freqs_wide[,1]))
colnames(freq_kmeans) <- c("kmean_cluster", "chemokine_cluster")
freq_kmeans$kmean_cluster <- factor(freq_kmeans$kmean_cluster, levels = 0:length(unique(freq_kmeans$kmean_cluster)))
cur_dt <- as.data.table(colData(sce))
cur_dt <- left_join(cur_dt, freq_kmeans)
# add community module to sce object
cur_dt[is.na(kmean_cluster), kmean_cluster := "0"]
colData(sce)$community_module <- cur_dt$kmean_clustersaveRDS(sce, file = "data/sce_rna.rds")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] fpc_2.2-9 data.table_1.13.6
[3] forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.2 purrr_0.3.4
[7] readr_1.4.0 tidyr_1.1.2
[9] tibble_3.0.4 ggplot2_3.3.3
[11] tidyverse_1.3.0 reshape2_1.4.4
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0 GenomicRanges_1.42.0
[17] GenomeInfoDb_1.26.2 IRanges_2.24.1
[19] S4Vectors_0.28.1 BiocGenerics_0.36.0
[21] MatrixGenerics_1.2.0 matrixStats_0.57.0
[23] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0 lubridate_1.7.9.2
[4] httr_1.4.2 prabclus_2.3-2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.2.1 R6_2.5.0
[10] DBI_1.1.0 colorspace_2.0-0 nnet_7.3-14
[13] withr_2.3.0 tidyselect_1.1.0 compiler_4.0.3
[16] git2r_0.28.0 cli_2.2.0 rvest_0.3.6
[19] xml2_1.3.2 DelayedArray_0.16.0 diptest_0.75-7
[22] scales_1.1.1 DEoptimR_1.0-8 robustbase_0.93-7
[25] digest_0.6.27 rmarkdown_2.6 XVector_0.30.0
[28] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_2.0.0
[31] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.13
[34] generics_0.1.0 jsonlite_1.7.2 mclust_5.4.7
[37] RCurl_1.98-1.2 magrittr_2.0.1 modeltools_0.2-23
[40] GenomeInfoDbData_1.2.4 Matrix_1.3-2 Rcpp_1.0.5
[43] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[46] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[49] MASS_7.3-53 zlibbioc_1.36.0 flexmix_2.3-17
[52] plyr_1.8.6 grid_4.0.3 promises_1.1.1
[55] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
[58] hms_0.5.3 knitr_1.30 pillar_1.4.7
[61] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[64] modelr_0.1.8 vctrs_0.3.6 httpuv_1.5.4
[67] cellranger_1.1.0 gtable_0.3.0 kernlab_0.9-29
[70] assertthat_0.2.1 xfun_0.20 broom_0.7.3
[73] later_1.1.0.1 class_7.3-17 cluster_2.1.0
[76] ellipsis_0.3.1