Last updated: 2022-02-25
Checks: 6 1
Knit directory: MelanomaIMC/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). 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.
Great job! The global environment was empty. Objects defined in the global environment 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.
The command set.seed(20200728) 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.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
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 results in this page were generated with repository version 0deb8ee. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
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: .Rproj.user/
Ignored: code/.DS_Store
Ignored: code/._.DS_Store
Ignored: data/.DS_Store
Ignored: data/._.DS_Store
Ignored: data/data_for_analysis/
Ignored: data/data_for_analysis_zenodo/
Ignored: data/full_data.zip
Ignored: data/full_data/
Unstaged changes:
Modified: .gitignore
Modified: analysis/09_Tcell_Score.Rmd
Modified: analysis/12_Bcell_Score.rmd
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 repository in which changes were made to the R Markdown (analysis/12_Bcell_Score.rmd) and HTML (docs/12_Bcell_Score.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | fe331cb | toobiwankenobi | 2022-02-22 | re-run whole analysis |
| html | 73aa800 | toobiwankenobi | 2022-02-22 | add .html for static website |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
sapply(list.files("code/helper_functions", full.names = TRUE), source)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union code/helper_functions/calculateSummary.R
value ?
visible FALSE
code/helper_functions/censor_dat.R
value ?
visible FALSE
code/helper_functions/detect_mRNA_expression.R
value ?
visible FALSE
code/helper_functions/DistanceToClusterCenter.R
value ?
visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
value ?
visible FALSE
code/helper_functions/getSpotnumber.R
value ?
visible FALSE
code/helper_functions/plotCellCounts.R
value ?
visible FALSE
code/helper_functions/plotCellFractions.R
value ?
visible FALSE
code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value ? ?
visible FALSE FALSE
code/helper_functions/scatter_function.R
value ?
visible FALSE
code/helper_functions/sceChecks.R
value ?
visible FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(SingleCellExperiment)Loading required package: SummarizedExperimentLoading required package: MatrixGenericsLoading required package: matrixStats
Attaching package: 'matrixStats'The following object is masked from 'package:dplyr':
count
Attaching package: 'MatrixGenerics'The following objects are masked from 'package:matrixStats':
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,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVarsLoading required package: GenomicRangesLoading required package: stats4Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:dplyr':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
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.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:dplyr':
first, renameThe following objects are masked from 'package:base':
expand.grid, I, unnameLoading required package: IRanges
Attaching package: 'IRanges'The following objects are masked from 'package:dplyr':
collapse, desc, sliceLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'Biobase'The following object is masked from 'package:MatrixGenerics':
rowMediansThe following objects are masked from 'package:matrixStats':
anyMissing, rowMedianslibrary(ggplot2)
library(scater)Loading required package: scuttlelibrary(viridis)Loading required package: viridisLitelibrary(igraph)
Attaching package: 'igraph'The following object is masked from 'package:scater':
normalizeThe following object is masked from 'package:GenomicRanges':
unionThe following object is masked from 'package:IRanges':
unionThe following object is masked from 'package:S4Vectors':
unionThe following objects are masked from 'package:BiocGenerics':
normalize, path, unionThe following objects are masked from 'package:dplyr':
as_data_frame, groups, unionThe following objects are masked from 'package:stats':
decompose, spectrumThe following object is masked from 'package:base':
unionlibrary(CATALYST)
library(reshape2)
library(cowplot)
library(ggridges)
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ tibble 3.1.6 ✓ purrr 0.3.4
✓ tidyr 1.2.0 ✓ stringr 1.4.0
✓ readr 2.1.2 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x tibble::as_data_frame() masks igraph::as_data_frame(), dplyr::as_data_frame()
x IRanges::collapse() masks dplyr::collapse()
x Biobase::combine() masks BiocGenerics::combine(), dplyr::combine()
x purrr::compose() masks igraph::compose()
x matrixStats::count() masks dplyr::count()
x tidyr::crossing() masks igraph::crossing()
x IRanges::desc() masks dplyr::desc()
x tidyr::expand() masks S4Vectors::expand()
x dplyr::filter() masks stats::filter()
x S4Vectors::first() masks dplyr::first()
x igraph::groups() masks dplyr::groups()
x dplyr::lag() masks stats::lag()
x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
x purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
x S4Vectors::rename() masks dplyr::rename()
x purrr::simplify() masks igraph::simplify()
x IRanges::slice() masks dplyr::slice()library(viridis)
library(dplyr)
library(cytomapper)Loading required package: EBImage
Attaching package: 'EBImage'The following object is masked from 'package:purrr':
transposeThe following object is masked from 'package:SummarizedExperiment':
resizeThe following object is masked from 'package:Biobase':
channelThe following objects are masked from 'package:GenomicRanges':
resize, tileThe following objects are masked from 'package:IRanges':
resize, tile
Attaching package: 'cytomapper'The following objects are masked from 'package:Biobase':
channelNames, channelNames<-library(concaveman)
library(data.table)
Attaching package: 'data.table'The following object is masked from 'package:EBImage':
transposeThe following object is masked from 'package:purrr':
transposeThe following objects are masked from 'package:reshape2':
dcast, meltThe following object is masked from 'package:SummarizedExperiment':
shiftThe following object is masked from 'package:GenomicRanges':
shiftThe following object is masked from 'package:IRanges':
shiftThe following objects are masked from 'package:S4Vectors':
first, secondThe following objects are masked from 'package:dplyr':
between, first, lastlibrary(sf)Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUElibrary(ggbeeswarm)
library(RANN)sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
image_prot <- read.csv("data/data_for_analysis/protein/Image.csv")
sce_prot$bcell_patch_score <- NULL
sce_rna$bcell_patch_score <- NULL# Protein
im_size_prot <- (image_prot$Height_cellmask * image_prot$Width_cellmask)/1000000
im_size_prot <- data.frame(im_size_prot)
im_size_prot$Description <- image_prot$Metadata_Description# max patch size per image
max_patch <- data.frame(colData(sce_prot)) %>%
filter(bcell_patch != 0) %>%
group_by(Description, bcell_patch) %>%
summarise(n=n()) %>%
summarise(max_patch_size = max(n)) %>%
arrange(-max_patch_size)`summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.# assing patch score
max_patch$bcell_patch_score <- ifelse(max_patch$max_patch_size >= median(max_patch$max_patch_size), "B cell Follicles", "Small B cell Patches")median(max_patch$max_patch_size)[1] 3611 No B cells (lower half of median split in images with no B cell patches) 2 No B cell Patches (upper half of median split in images with no B cell patches) 3 Small B cell Patches (lower half of median split for maximum patch size per image) 4 B cell Follicles (upper half of median split for maximum patch size per image)
# images with no patches
noPatch_img <- data.frame(colData(sce_prot)) %>%
group_by(Description) %>%
summarise(n=sum(bcell_patch)) %>%
distinct(Description, .keep_all = T) %>%
ungroup() %>%
filter(n==0)
# remove all images with patches
Bcell <- data.frame(colData(sce_prot)) %>%
filter(Description %in% noPatch_img$Description) %>%
group_by(Description,celltype) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ celltype, value.var = "n", fill = 0) %>%
select(Description, `B cell`)`summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.Bcell$density <- Bcell$`B cell` / im_size_prot[match(Bcell$Description, im_size_prot$Description),]$im_size_prot
# assing patch score
Bcell$bcell_patch_score <- ifelse(Bcell$density >= median(Bcell$density), "No B cell Patches", "No B cells")
# merge both data sets
data <- rbind(Bcell[,c("Description", "bcell_patch_score")], max_patch[,c("Description", "bcell_patch_score")])
# factorize with levels
data$bcell_patch_score <- factor(data$bcell_patch_score, levels = c("No B cells", "No B cell Patches", "Small B cell Patches", "B cell Follicles"))
# group sizes
data %>%
group_by(bcell_patch_score) %>%
summarise(n=n())# A tibble: 4 × 2
bcell_patch_score n
<fct> <int>
1 No B cells 58
2 No B cell Patches 59
3 Small B cell Patches 24
4 B cell Follicles 25median(Bcell$density)[1] 1.982121cur_rna <- data.frame(colData(sce_rna))[,c("Description", "ImageNumber")]
cur_prot <- data.frame(colData(sce_prot))[,c("Description", "ImageNumber")]
cur_rna <- left_join(cur_rna, data)Joining, by = "Description"cur_prot <- left_join(cur_prot, data)Joining, by = "Description"sce_rna$bcell_patch_score <- cur_rna$bcell_patch_score
sce_prot$bcell_patch_score <- cur_prot$bcell_patch_scoredata.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
distinct(PatientID, .keep_all = T) %>%
group_by(bcell_patch_score) %>%
summarise(patients = n())# A tibble: 4 × 2
bcell_patch_score patients
<fct> <int>
1 No B cells 21
2 No B cell Patches 21
3 Small B cell Patches 17
4 B cell Follicles 10saveRDS(sce_prot, file = "data/data_for_analysis/sce_protein.rds")
saveRDS(sce_rna, file = "data/data_for_analysis/sce_RNA.rds")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 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=en_US.UTF-8
[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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RANN_2.6.1 ggbeeswarm_0.6.0
[3] sf_1.0-5 data.table_1.14.2
[5] concaveman_1.1.0 cytomapper_1.6.0
[7] EBImage_4.36.0 forcats_0.5.1
[9] stringr_1.4.0 purrr_0.3.4
[11] readr_2.1.2 tidyr_1.2.0
[13] tibble_3.1.6 tidyverse_1.3.1
[15] ggridges_0.5.3 cowplot_1.1.1
[17] reshape2_1.4.4 CATALYST_1.18.1
[19] igraph_1.2.11 viridis_0.6.2
[21] viridisLite_0.4.0 scater_1.22.0
[23] scuttle_1.4.0 ggplot2_3.3.5
[25] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[27] Biobase_2.54.0 GenomicRanges_1.46.1
[29] GenomeInfoDb_1.30.1 IRanges_2.28.0
[31] S4Vectors_0.32.3 BiocGenerics_0.40.0
[33] MatrixGenerics_1.6.0 matrixStats_0.61.0
[35] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.7 flowWorkspace_4.6.0
[3] knitr_1.37 irlba_2.3.5
[5] multcomp_1.4-18 DelayedArray_0.20.0
[7] RCurl_1.98-1.5 doParallel_1.0.16
[9] generics_0.1.2 flowCore_2.6.0
[11] ScaledMatrix_1.2.0 terra_1.5-17
[13] callr_3.7.0 TH.data_1.1-0
[15] proxy_0.4-26 ggpointdensity_0.1.0
[17] tzdb_0.2.0 xml2_1.3.3
[19] lubridate_1.8.0 httpuv_1.6.5
[21] assertthat_0.2.1 xfun_0.29
[23] hms_1.1.1 jquerylib_0.1.4
[25] evaluate_0.14 promises_1.2.0.1
[27] fansi_1.0.2 dbplyr_2.1.1
[29] readxl_1.3.1 Rgraphviz_2.38.0
[31] DBI_1.1.2 htmlwidgets_1.5.4
[33] ellipsis_0.3.2 ggcyto_1.22.0
[35] ggnewscale_0.4.5 ggpubr_0.4.0
[37] backports_1.4.1 cytolib_2.6.1
[39] svgPanZoom_0.3.4 RcppParallel_5.1.5
[41] sparseMatrixStats_1.6.0 vctrs_0.3.8
[43] abind_1.4-5 withr_2.4.3
[45] ggforce_0.3.3 aws.signature_0.6.0
[47] svglite_2.0.0 cluster_2.1.2
[49] crayon_1.4.2 drc_3.0-1
[51] units_0.7-2 pkgconfig_2.0.3
[53] tweenr_1.0.2 vipor_0.4.5
[55] rlang_1.0.0 lifecycle_1.0.1
[57] sandwich_3.0-1 modelr_0.1.8
[59] rsvd_1.0.5 cellranger_1.1.0
[61] rprojroot_2.0.2 polyclip_1.10-0
[63] graph_1.72.0 tiff_0.1-11
[65] Matrix_1.4-0 raster_3.5-15
[67] carData_3.0-5 Rhdf5lib_1.16.0
[69] zoo_1.8-9 reprex_2.0.1
[71] base64enc_0.1-3 beeswarm_0.4.0
[73] whisker_0.4 GlobalOptions_0.1.2
[75] processx_3.5.2 pheatmap_1.0.12
[77] png_0.1-7 rjson_0.2.21
[79] bitops_1.0-7 shinydashboard_0.7.2
[81] getPass_0.2-2 KernSmooth_2.23-20
[83] rhdf5filters_1.6.0 ConsensusClusterPlus_1.58.0
[85] DelayedMatrixStats_1.16.0 classInt_0.4-3
[87] shape_1.4.6 jpeg_0.1-9
[89] rstatix_0.7.0 ggsignif_0.6.3
[91] aws.s3_0.3.21 beachmat_2.10.0
[93] scales_1.1.1 magrittr_2.0.2
[95] plyr_1.8.6 hexbin_1.28.2
[97] zlibbioc_1.40.0 compiler_4.1.2
[99] RColorBrewer_1.1-2 plotrix_3.8-2
[101] clue_0.3-60 cli_3.1.1
[103] XVector_0.34.0 ncdfFlow_2.40.0
[105] ps_1.6.0 FlowSOM_2.2.0
[107] MASS_7.3-55 tidyselect_1.1.1
[109] stringi_1.7.6 RProtoBufLib_2.6.0
[111] yaml_2.2.2 BiocSingular_1.10.0
[113] locfit_1.5-9.4 latticeExtra_0.6-29
[115] ggrepel_0.9.1 grid_4.1.2
[117] sass_0.4.0 tools_4.1.2
[119] parallel_4.1.2 CytoML_2.6.0
[121] circlize_0.4.13 rstudioapi_0.13
[123] foreach_1.5.2 git2r_0.29.0
[125] gridExtra_2.3 farver_2.1.0
[127] Rtsne_0.15 digest_0.6.29
[129] shiny_1.7.1 Rcpp_1.0.8
[131] car_3.0-12 broom_0.7.12
[133] later_1.3.0 httr_1.4.2
[135] ComplexHeatmap_2.10.0 colorspace_2.0-2
[137] rvest_1.0.2 XML_3.99-0.8
[139] fs_1.5.2 splines_4.1.2
[141] RBGL_1.70.0 sp_1.4-6
[143] systemfonts_1.0.3 xtable_1.8-4
[145] jsonlite_1.7.3 R6_2.5.1
[147] pillar_1.7.0 htmltools_0.5.2
[149] mime_0.12 nnls_1.4
[151] glue_1.6.1 fastmap_1.1.0
[153] BiocParallel_1.28.3 BiocNeighbors_1.12.0
[155] fftwtools_0.9-11 class_7.3-20
[157] codetools_0.2-18 mvtnorm_1.1-3
[159] utf8_1.2.2 lattice_0.20-45
[161] bslib_0.3.1 curl_4.3.2
[163] colorRamps_2.3 gtools_3.9.2
[165] survival_3.2-13 rmarkdown_2.11
[167] munsell_0.5.0 e1071_1.7-9
[169] rhdf5_2.38.0 GetoptLong_1.0.5
[171] GenomeInfoDbData_1.2.7 iterators_1.0.13
[173] HDF5Array_1.22.1 haven_2.4.3
[175] gtable_0.3.0