diff --git a/analyses/cell-type-wilms-tumor-14/README.md b/analyses/cell-type-wilms-tumor-14/README.md index ba9ff9346..e962b3c2e 100644 --- a/analyses/cell-type-wilms-tumor-14/README.md +++ b/analyses/cell-type-wilms-tumor-14/README.md @@ -31,7 +31,7 @@ This would include: ## Usage -* Run Rscripts with command line +* Run main pipeline with command line ```bash cd /path/to/OpenScPCA-analysis cd analyses/cell-type-wilms-tumor-14 @@ -74,4 +74,7 @@ sudo apt install -y libglpk40 \ ## Computational resources -Analysis could be executed on a virtual computer ([Standard-4XL](https://openscpca.readthedocs.io/en/latest/aws/lsfr/creating-vcs/)) via AWS Lightsail for Research. \ No newline at end of file +Analysis could be executed on a virtual computer ([Standard-4XL](https://openscpca.readthedocs.io/en/latest/aws/lsfr/creating-vcs/)) via AWS Lightsail for Research. + +## Exploratory analysis +In addition to the main pipeline, some exploratory analysis in R notebooks are added into the `./exploratory_analysis` folder, including CNV analysis. Check `./exploratory_analysis/README.md` for more details. \ No newline at end of file diff --git a/analyses/cell-type-wilms-tumor-14/components/dependencies.R b/analyses/cell-type-wilms-tumor-14/components/dependencies.R index ae80dc0ac..774e95b61 100644 --- a/analyses/cell-type-wilms-tumor-14/components/dependencies.R +++ b/analyses/cell-type-wilms-tumor-14/components/dependencies.R @@ -1,2 +1,3 @@ # R dependencies not captured by `renv` # library("missing_package") +library(rtracklayer) diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.Rmd b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.Rmd new file mode 100644 index 000000000..e342a560d --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.Rmd @@ -0,0 +1,124 @@ +--- +title: "Exploratory analysis using Copykat" +author: "Jingxuan Chen" +date: "`r Sys.Date()`" +output: html_document +--- + +## Introduction + +Here we test `CopyKat` to analyze the CNV profile for Wilms tumor sample (SCPCL000850), aiming to identify potential tumor cells from normal cells. + +Before running this notebook, one should first generate `CopyKat` outputs as in `03_runCopyKat.R`. + +## Setup + +### Packages + +```{r packages} +suppressPackageStartupMessages({ + library(dplyr) + library(Seurat) + library(copykat) + library(ggplot2) +}) +``` + +### Paths + + +#### Base directories + +```{r base paths} +# The base path for the OpenScPCA repository, found by its (hidden) .git directory +path_repo <- rprojroot::find_root(rprojroot::is_git_root) + +# The path to this module +path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14") +``` + +#### Input and output files + +Only run for one sample + +```{r paths} +library_id <- "SCPCL000850" +scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","copykat") +dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE) +library_out_dir <- file.path(scratch_out_dir, library_id) +dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE) + +## Input files +# load pre-processed sample objs & anchor transfer results +obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library_id,".rdsSeurat")) ) +level <- "compartment" +predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library_id, "_", level,".csv")) ) +obj <- AddMetaData(object = obj, metadata = predictions) +copykat_result <- readr::read_rds( file = file.path(library_out_dir, paste0(library_id, "_copykat_resultobj.rds")) ) +copykat_result_noref <- readr::read_rds( file = file.path(library_out_dir, paste0(library_id, "_noref_copykat_resultobj.rds")) ) + +``` + +```{r images} +heatmap_wref <- file.path(library_out_dir, paste0(library_id, "_copykat_heatmap.jpeg")) +heatmap_noref <- file.path(library_out_dir, paste0(library_id, "_noref_copykat_heatmap.jpeg")) +``` + +## Analysis content + +#### Run CopyKat with normal cells + +Here, "immune" cells annotated with anchor transfer were used as reference normal cells. Here is the heatmap generated by the software: + +```{r, echo=FALSE, out.width = '100%'} +knitr::include_graphics(heatmap_wref) +``` + +Frequency of predicted `aneuploid`, `diploid`, and `not.defined` categories. + +```{r} +copykat_df <- copykat_result$prediction %>% + select(copykat.pred) %>% + rename(c("ref.copykat" = "copykat.pred")) +table(copykat_df) +``` + +Plot CopyKat identifications onto UMAP. + +```{r} +obj <- AddMetaData(object = obj, metadata = copykat_df) +DimPlot(obj, group.by = "ref.copykat", alpha = 0.3) +``` + +#### Run CopyKat without normal cells + +Here, no normal cells were provided to CopyKat. + +```{r, echo=FALSE, out.width = '100%'} +knitr::include_graphics(heatmap_noref) +``` + +Frequency of predicted `aneuploid`, `diploid`, and `not.defined` categories. + +```{r} +copykat_noref_df <- copykat_result_noref$prediction %>% + select(copykat.pred) %>% + rename(c("noref.copykat" = "copykat.pred")) +table(copykat_noref_df) +``` + +Plot CopyKat identifications onto UMAP. + +```{r} +obj <- AddMetaData(object = obj, metadata = copykat_noref_df) +DimPlot(obj, group.by = "noref.copykat", alpha = 0.3) +``` + +In this Wilms tumor sample (SCPCL000850), it's likely that CopyKat is not working well: (i) The result with or without specifying normal cells are not consistent; (ii) From the heatmap, no much differences in CNV profile could be observed. + +## Session Info + +```{r session info} +# record the versions of the packages used in this analysis and other environment information +sessionInfo() +``` diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.html b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.html new file mode 100644 index 000000000..0fe0c828c --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_copykat_SCPCL000850.html @@ -0,0 +1,574 @@ + + + + + + + + + + + + + + + +Exploratory analysis using Copykat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

Here we test CopyKat to analyze the CNV profile for +Wilms tumor sample (SCPCL000850), aiming to identify potential tumor +cells from normal cells.

+

Before running this notebook, one should first generate +CopyKat outputs as in 03_runCopyKat.R.

+
+
+

Setup

+
+

Packages

+
suppressPackageStartupMessages({
+  library(dplyr)
+  library(Seurat)
+  library(copykat)
+  library(ggplot2)
+})
+
+
+

Paths

+
+

Base directories

+
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
+path_repo <- rprojroot::find_root(rprojroot::is_git_root)
+
+# The path to this module
+path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14")
+
+
+

Input and output files

+

Only run for one sample

+
library_id <- "SCPCL000850"
+scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","copykat")
+dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE)
+library_out_dir <- file.path(scratch_out_dir, library_id)
+dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE)
+
+## Input files
+# load pre-processed sample objs & anchor transfer results
+obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library_id,".rdsSeurat")) )
+level <- "compartment"
+predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library_id, "_", level,".csv")) ) 
+obj <- AddMetaData(object = obj, metadata = predictions)
+copykat_result <- readr::read_rds( file = file.path(library_out_dir, paste0(library_id, "_copykat_resultobj.rds")) )
+copykat_result_noref <- readr::read_rds( file = file.path(library_out_dir, paste0(library_id, "_noref_copykat_resultobj.rds")) )
+
heatmap_wref <- file.path(library_out_dir, paste0(library_id, "_copykat_heatmap.jpeg"))
+heatmap_noref <- file.path(library_out_dir, paste0(library_id, "_noref_copykat_heatmap.jpeg"))
+
+
+
+
+

Analysis content

+
+

Run CopyKat with normal cells

+

Here, “immune” cells annotated with anchor transfer were used as +reference normal cells. Here is the heatmap generated by the +software:

+

+

Frequency of predicted aneuploid, diploid, +and not.defined categories.

+
copykat_df <- copykat_result$prediction %>%
+  select(copykat.pred) %>% 
+  rename(c("ref.copykat" = "copykat.pred"))
+table(copykat_df)
+
## ref.copykat
+##   aneuploid     diploid not.defined 
+##        1089        1276         578
+

Plot CopyKat identifications onto UMAP.

+
obj <- AddMetaData(object = obj, metadata = copykat_df)
+DimPlot(obj, group.by = "ref.copykat", alpha = 0.3)
+

+
+
+

Run CopyKat without normal cells

+

Here, no normal cells were provided to CopyKat.

+

+

Frequency of predicted aneuploid, diploid, +and not.defined categories.

+
copykat_noref_df <- copykat_result_noref$prediction %>%
+  select(copykat.pred) %>% 
+  rename(c("noref.copykat" = "copykat.pred"))
+table(copykat_noref_df)
+
## noref.copykat
+##   aneuploid     diploid not.defined 
+##        1458         907         578
+

Plot CopyKat identifications onto UMAP.

+
obj <- AddMetaData(object = obj, metadata = copykat_noref_df)
+DimPlot(obj, group.by = "noref.copykat", alpha = 0.3)
+

+

In this Wilms tumor sample (SCPCL000850), it’s likely that CopyKat is +not working well: (i) The result with or without specifying normal cells +are not consistent; (ii) From the heatmap, no much differences in CNV +profile could be observed.

+
+
+
+

Session Info

+
# record the versions of the packages used in this analysis and other environment information
+sessionInfo()
+
## R version 4.4.0 (2024-04-24)
+## Platform: x86_64-pc-linux-gnu
+## Running under: Ubuntu 22.04.4 LTS
+## 
+## Matrix products: default
+## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
+## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
+## 
+## locale:
+##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
+##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
+##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
+## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
+## 
+## time zone: Etc/UTC
+## tzcode source: system (glibc)
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices datasets  utils     methods   base     
+## 
+## other attached packages:
+## [1] ggplot2_3.5.1      copykat_1.1.0      Seurat_5.1.0       SeuratObject_5.0.2
+## [5] sp_2.1-4           dplyr_1.1.4       
+## 
+## loaded via a namespace (and not attached):
+##   [1] RColorBrewer_1.1-3     jsonlite_1.8.9         magrittr_2.0.3        
+##   [4] spatstat.utils_3.1-0   farver_2.1.2           rmarkdown_2.28        
+##   [7] vctrs_0.6.5            ROCR_1.0-11            spatstat.explore_3.3-2
+##  [10] htmltools_0.5.8.1      sass_0.4.9             sctransform_0.4.1     
+##  [13] parallelly_1.38.0      KernSmooth_2.23-22     bslib_0.8.0           
+##  [16] htmlwidgets_1.6.4      ica_1.0-3              plyr_1.8.9            
+##  [19] plotly_4.10.4          zoo_1.8-12             cachem_1.1.0          
+##  [22] igraph_2.0.3           mime_0.12              lifecycle_1.0.4       
+##  [25] pkgconfig_2.0.3        Matrix_1.7-0           R6_2.5.1              
+##  [28] fastmap_1.2.0          fitdistrplus_1.2-1     future_1.34.0         
+##  [31] shiny_1.9.1            digest_0.6.37          colorspace_2.1-1      
+##  [34] patchwork_1.3.0        rprojroot_2.0.4        tensor_1.5            
+##  [37] RSpectra_0.16-2        irlba_2.3.5.1          labeling_0.4.3        
+##  [40] progressr_0.14.0       fansi_1.0.6            spatstat.sparse_3.1-0 
+##  [43] httr_1.4.7             polyclip_1.10-7        abind_1.4-8           
+##  [46] compiler_4.4.0         withr_3.0.1            fastDummies_1.7.4     
+##  [49] highr_0.11             MASS_7.3-60.2          tools_4.4.0           
+##  [52] lmtest_0.9-40          httpuv_1.6.15          future.apply_1.11.2   
+##  [55] goftest_1.2-3          glue_1.8.0             nlme_3.1-164          
+##  [58] promises_1.3.0         grid_4.4.0             Rtsne_0.17            
+##  [61] cluster_2.1.6          reshape2_1.4.4         generics_0.1.3        
+##  [64] gtable_0.3.5           spatstat.data_3.1-2    tzdb_0.4.0            
+##  [67] tidyr_1.3.1            data.table_1.16.0      hms_1.1.3             
+##  [70] utf8_1.2.4             spatstat.geom_3.3-3    RcppAnnoy_0.0.22      
+##  [73] ggrepel_0.9.6          RANN_2.6.2             pillar_1.9.0          
+##  [76] stringr_1.5.1          spam_2.10-0            RcppHNSW_0.6.0        
+##  [79] later_1.3.2            splines_4.4.0          lattice_0.22-6        
+##  [82] renv_1.0.7             survival_3.5-8         deldir_2.0-4          
+##  [85] tidyselect_1.2.1       miniUI_0.1.1.1         pbapply_1.7-2         
+##  [88] knitr_1.48             gridExtra_2.3          scattermore_1.2       
+##  [91] xfun_0.47              matrixStats_1.4.1      stringi_1.8.4         
+##  [94] lazyeval_0.2.2         yaml_2.3.10            evaluate_1.0.0        
+##  [97] codetools_0.2-20       tibble_3.2.1           BiocManager_1.30.25   
+## [100] cli_3.6.3              uwot_0.2.2             xtable_1.8-4          
+## [103] reticulate_1.39.0      munsell_0.5.1          jquerylib_0.1.4       
+## [106] Rcpp_1.0.13            globals_0.16.3         spatstat.random_3.3-2 
+## [109] png_0.1-8              spatstat.univar_3.0-1  parallel_4.4.0        
+## [112] readr_2.1.5            dotCall64_1.1-1        listenv_0.9.1         
+## [115] viridisLite_0.4.2      scales_1.3.0           ggridges_0.5.6        
+## [118] leiden_0.4.3.1         purrr_1.0.2            rlang_1.1.4           
+## [121] cowplot_1.1.3
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.Rmd b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.Rmd new file mode 100644 index 000000000..1f654f330 --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.Rmd @@ -0,0 +1,171 @@ +--- +title: "Exploratory CNV analysis using inferCNV" +author: "Jingxuan Chen" +date: "`r Sys.Date()`" +output: html_document +--- + +## Introduction + +Here we test `inferCNV` to analyze the CNV profile for Wilms tumor sample (SCPCL000850), aiming to identify potential tumor cells from normal cells. + +Before running this notebook, one should first generate `inferCNV` outputs as in `03_runInferCNV.R`. + +## Setup + +### Packages + +```{r packages} +suppressPackageStartupMessages({ + library(dplyr) + library(Seurat) + library(infercnv) + library(ggplot2) +}) +``` + +### Paths + +#### Base directories + +```{r base paths} +# The base path for the OpenScPCA repository, found by its (hidden) .git directory +path_repo <- rprojroot::find_root(rprojroot::is_git_root) + +# The path to this module +path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14") +``` + +#### Input and output files + +Only run for one sample + +```{r paths} +library_id <- "SCPCL000850" +scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","infercnv") +dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE) +library_out_dir <- file.path(scratch_out_dir, library_id) +dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE) + +## Input files +# load pre-processed sample objs & anchor transfer results +obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library_id,".rdsSeurat")) ) +level <- "compartment" +predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library_id, "_", level,".csv")) ) +obj <- AddMetaData(object = obj, metadata = predictions) +infercnv_result <- readr::read_rds( file = file.path(library_out_dir, "run.final.infercnv_obj") ) +# add infercnv output to seurat object +obj_cnv <- infercnv::add_to_seurat( + seurat_obj = obj, + infercnv_output_path = library_out_dir +) +``` + +```{r images} +heatmap_wref <- file.path(library_out_dir, "infercnv.png") +normal_level <- "immune" +``` + +## Analysis content + +Here, "immune" cells annotated with anchor transfer were used as reference normal cells. Here is the heatmap generated by the software: + +```{r, echo=FALSE, out.width = '100%'} +knitr::include_graphics(heatmap_wref) +``` + +We could observe some different CNV profiles between "fetal_nephron" and "stroma", especially chr1 and chr11. However, it's hard to observe any different CNV profiles within the "fetal_nephron" subgroups. + +`inferCNV` outputs results for each Chromosome. Thus, I applied several ways to summarize the overall CNV profile. + +#### CNV score - proportion of genes with CNV + +Based on [this](https://www.biostars.org/p/9573777/) Biostars discussion, I calculated a `cnv_score`, which basically indicates the proportion of CNVs across all genes. + +```{r} +## summary strategy 1 https://www.biostars.org/p/9573777/ +scores <- apply(infercnv_result@expr.data, 2 ,function(x){ sum(x < 0.95 | x > 1.05)/length(x) }) %>% + as.data.frame() %>% + mutate(pred = obj$predicted.id) %>% + mutate(cnv_score = ifelse( + pred == normal_level, + NA, + . + )) +``` + +Ideally, we should observe bimodal distribution for this score, indicating the CNV difference between normal and tumor cells. However, here we could get a distribution close to normal. +```{r} +hist(scores$cnv_score, breaks = 100) +``` + +By plotting the CNV score onto UMAP, no clear pattern is shown. +```{r} +obj <- AddMetaData(object = obj, metadata = scores) +FeaturePlot(obj, features = "cnv_score", alpha = 0.3) + + scale_color_viridis_c() +``` + +#### Summary based upon number of chromosomes that have CNV +This strategy to summary CNV results is based on [`cell-type-ewings`](https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/analyses/cell-type-ewings/template_notebooks/cnv-workflow) module. + +```{r} +## summary strategy 2 ewings analysis, based on number of chr that have cnv +cnv_df <- obj_cnv@meta.data %>% + select(matches("predicted.id") | starts_with("has_cnv_chr") & !matches("has_cnv_chrMT")) %>% + mutate(count_cnv_chr = ifelse(predicted.id == normal_level, + NA, + rowSums(across(starts_with("has_cnv_chr"))) + ) ) +``` + +```{r} +hist(cnv_df$count_cnv_chr) +``` + +```{r} +obj <- AddMetaData(object = obj, metadata = cnv_df) +FeaturePlot(obj, features = "count_cnv_chr", alpha = 0.3) + + scale_color_viridis_c() +``` + +#### Summary based upon weighted means of CNV proportion +This strategy to summary CNV results is based on [`cell-type-ewings`](https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/analyses/cell-type-ewings/template_notebooks/cnv-workflow) module. + +```{r} +## summary strategy 3 ewings analysis, based on number of chr that have cn +chr_weights <- infercnv_result@gene_order |> + as.data.frame() |> + dplyr::count(chr) |> + # only keep chr 1-22, drops MT and GL chr + dplyr::filter(chr %in% glue::glue("chr{seq(1,22)}")) |> + dplyr::pull(n) + +cnv_df <- obj_cnv@meta.data %>% + select(matches("predicted.id") | starts_with("proportion_scaled_cnv_chr") & !ends_with("chrMT")) %>% + rowwise() %>% + mutate(weight_mean = ifelse( + predicted.id == normal_level, + NA, + weighted.mean(across(starts_with("proportion_scaled_cnv_chr")), chr_weights/sum(chr_weights)) + ) ) +``` + +```{r} +hist(cnv_df$weight_mean) +``` + +```{r} +obj <- AddMetaData(object = obj, metadata = cnv_df) +FeaturePlot(obj, features = "weight_mean", alpha = 0.3) + + scale_color_viridis_c() +``` + +In conclusion, the heatmap generated by `inferCNV` indicates different CNV profiles between `fetal_nephron` and `stroma`, especially in Chr1. However, none of the three summary strategies seem working. + +## Session Info + +```{r session info} +# record the versions of the packages used in this analysis and other environment information +sessionInfo() +``` diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.html b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.html new file mode 100644 index 000000000..98665b647 --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.html @@ -0,0 +1,671 @@ + + + + + + + + + + + + + + + +Exploratory CNV analysis using inferCNV + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

Here we test inferCNV to analyze the CNV profile for +Wilms tumor sample (SCPCL000850), aiming to identify potential tumor +cells from normal cells.

+

Before running this notebook, one should first generate +inferCNV outputs as in 03_runInferCNV.R.

+
+
+

Setup

+
+

Packages

+
suppressPackageStartupMessages({
+  library(dplyr)
+  library(Seurat)
+  library(infercnv)
+  library(ggplot2)
+})
+
+
+

Paths

+
+

Base directories

+
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
+path_repo <- rprojroot::find_root(rprojroot::is_git_root)
+
+# The path to this module
+path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14")
+
+
+

Input and output files

+

Only run for one sample

+
library_id <- "SCPCL000850"
+scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","infercnv")
+dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE)
+library_out_dir <- file.path(scratch_out_dir, library_id)
+dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE)
+
+## Input files
+# load pre-processed sample objs & anchor transfer results
+obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library_id,".rdsSeurat")) )
+level <- "compartment"
+predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library_id, "_", level,".csv")) ) 
+obj <- AddMetaData(object = obj, metadata = predictions)
+infercnv_result <- readr::read_rds( file = file.path(library_out_dir, "run.final.infercnv_obj") )
+# add infercnv output to seurat object
+obj_cnv <- infercnv::add_to_seurat(
+  seurat_obj = obj,
+  infercnv_output_path = library_out_dir
+)
+
heatmap_wref <- file.path(library_out_dir, "infercnv.png")
+normal_level <- "immune"
+
+
+
+
+

Analysis content

+

Here, “immune” cells annotated with anchor transfer were used as +reference normal cells. Here is the heatmap generated by the +software:

+

+

We could observe some different CNV profiles between “fetal_nephron” +and “stroma”, especially chr1 and chr11. However, it’s hard to observe +any different CNV profiles within the “fetal_nephron” subgroups.

+

inferCNV outputs results for each Chromosome. Thus, I +applied several ways to summarize the overall CNV profile.

+
+

CNV score - proportion of genes with CNV

+

Based on this +Biostars discussion, I calculated a cnv_score, which +basically indicates the proportion of CNVs across all genes.

+
## summary strategy 1 https://www.biostars.org/p/9573777/
+scores <- apply(infercnv_result@expr.data, 2 ,function(x){ sum(x < 0.95 | x > 1.05)/length(x) }) %>% 
+  as.data.frame() %>%
+  mutate(pred = obj$predicted.id) %>%
+  mutate(cnv_score = ifelse(
+    pred == normal_level,
+    NA,
+    .
+  ))
+

Ideally, we should observe bimodal distribution for this score, +indicating the CNV difference between normal and tumor cells. However, +here we could get a distribution close to normal.

+
hist(scores$cnv_score, breaks = 100)
+

+

By plotting the CNV score onto UMAP, no clear pattern is shown.

+
obj <- AddMetaData(object = obj, metadata = scores)
+FeaturePlot(obj, features = "cnv_score", alpha = 0.3) +
+  scale_color_viridis_c()
+
## Scale for colour is already present.
+## Adding another scale for colour, which will replace the existing scale.
+

+
+
+

Summary based upon number of chromosomes that have CNV

+

This strategy to summary CNV results is based on cell-type-ewings +module.

+
## summary strategy 2 ewings analysis, based on number of chr that have cnv
+cnv_df <- obj_cnv@meta.data %>%
+  select(matches("predicted.id") | starts_with("has_cnv_chr") & !matches("has_cnv_chrMT")) %>%
+  mutate(count_cnv_chr = ifelse(predicted.id == normal_level,
+                            NA,
+                            rowSums(across(starts_with("has_cnv_chr")))
+                            ) )
+
hist(cnv_df$count_cnv_chr)
+

+
obj <- AddMetaData(object = obj, metadata = cnv_df)
+FeaturePlot(obj, features = "count_cnv_chr", alpha = 0.3) +
+  scale_color_viridis_c()
+
## Scale for colour is already present.
+## Adding another scale for colour, which will replace the existing scale.
+

+
+
+

Summary based upon weighted means of CNV proportion

+

This strategy to summary CNV results is based on cell-type-ewings +module.

+
## summary strategy 3 ewings analysis, based on number of chr that have cn
+chr_weights <- infercnv_result@gene_order |> 
+  as.data.frame() |> 
+  dplyr::count(chr) |> 
+  # only keep chr 1-22, drops MT and GL chr
+  dplyr::filter(chr %in% glue::glue("chr{seq(1,22)}")) |> 
+  dplyr::pull(n)
+
+cnv_df <- obj_cnv@meta.data %>%
+  select(matches("predicted.id") | starts_with("proportion_scaled_cnv_chr") & !ends_with("chrMT")) %>%
+  rowwise() %>%
+  mutate(weight_mean = ifelse(
+    predicted.id == normal_level,
+    NA,
+    weighted.mean(across(starts_with("proportion_scaled_cnv_chr")), chr_weights/sum(chr_weights))
+  ) )
+
hist(cnv_df$weight_mean)
+

+
obj <- AddMetaData(object = obj, metadata = cnv_df)
+
## Warning: Setting row names on a tibble is deprecated.
+
FeaturePlot(obj, features = "weight_mean", alpha = 0.3) +
+  scale_color_viridis_c()
+
## Scale for colour is already present.
+## Adding another scale for colour, which will replace the existing scale.
+

+

In conclusion, the heatmap generated by inferCNV +indicates different CNV profiles between fetal_nephron and +stroma, especially in Chr1. However, none of the three +summary strategies seem working.

+
+
+
+

Session Info

+
# record the versions of the packages used in this analysis and other environment information
+sessionInfo()
+
## R version 4.4.0 (2024-04-24)
+## Platform: x86_64-pc-linux-gnu
+## Running under: Ubuntu 22.04.4 LTS
+## 
+## Matrix products: default
+## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
+## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
+## 
+## locale:
+##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
+##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
+##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
+## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
+## 
+## time zone: Etc/UTC
+## tzcode source: system (glibc)
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices datasets  utils     methods   base     
+## 
+## other attached packages:
+## [1] ggplot2_3.5.1      infercnv_1.20.0    Seurat_5.1.0       SeuratObject_5.0.2
+## [5] sp_2.1-4           dplyr_1.1.4       
+## 
+## loaded via a namespace (and not attached):
+##   [1] RcppAnnoy_0.0.22            splines_4.4.0              
+##   [3] later_1.3.2                 bitops_1.0-8               
+##   [5] tibble_3.2.1                polyclip_1.10-7            
+##   [7] fastDummies_1.7.4           lifecycle_1.0.4            
+##   [9] rprojroot_2.0.4             fastcluster_1.2.6          
+##  [11] edgeR_4.2.1                 doParallel_1.0.17          
+##  [13] globals_0.16.3              lattice_0.22-6             
+##  [15] MASS_7.3-60.2               magrittr_2.0.3             
+##  [17] limma_3.60.4                plotly_4.10.4              
+##  [19] sass_0.4.9                  rmarkdown_2.28             
+##  [21] jquerylib_0.1.4             yaml_2.3.10                
+##  [23] httpuv_1.6.15               sctransform_0.4.1          
+##  [25] spam_2.10-0                 spatstat.sparse_3.1-0      
+##  [27] reticulate_1.39.0           cowplot_1.1.3              
+##  [29] pbapply_1.7-2               RColorBrewer_1.1-3         
+##  [31] multcomp_1.4-26             abind_1.4-8                
+##  [33] zlibbioc_1.50.0             Rtsne_0.17                 
+##  [35] GenomicRanges_1.56.1        purrr_1.0.2                
+##  [37] BiocGenerics_0.50.0         TH.data_1.1-2              
+##  [39] sandwich_3.1-1              GenomeInfoDbData_1.2.12    
+##  [41] IRanges_2.38.1              S4Vectors_0.42.1           
+##  [43] ggrepel_0.9.6               irlba_2.3.5.1              
+##  [45] listenv_0.9.1               spatstat.utils_3.1-0       
+##  [47] goftest_1.2-3               RSpectra_0.16-2            
+##  [49] spatstat.random_3.3-2       fitdistrplus_1.2-1         
+##  [51] parallelly_1.38.0           leiden_0.4.3.1             
+##  [53] codetools_0.2-20            coin_1.4-3                 
+##  [55] DelayedArray_0.30.1         tidyselect_1.2.1           
+##  [57] futile.logger_1.4.3         UCSC.utils_1.0.0           
+##  [59] farver_2.1.2                rjags_4-16                 
+##  [61] matrixStats_1.4.1           stats4_4.4.0               
+##  [63] spatstat.explore_3.3-2      jsonlite_1.8.9             
+##  [65] progressr_0.14.0            ggridges_0.5.6             
+##  [67] survival_3.5-8              iterators_1.0.14           
+##  [69] foreach_1.5.2               tools_4.4.0                
+##  [71] ica_1.0-3                   Rcpp_1.0.13                
+##  [73] glue_1.8.0                  gridExtra_2.3              
+##  [75] SparseArray_1.4.8           xfun_0.47                  
+##  [77] MatrixGenerics_1.16.0       GenomeInfoDb_1.40.1        
+##  [79] withr_3.0.1                 formatR_1.14               
+##  [81] BiocManager_1.30.25         fastmap_1.2.0              
+##  [83] fansi_1.0.6                 caTools_1.18.3             
+##  [85] digest_0.6.37               parallelDist_0.2.6         
+##  [87] R6_2.5.1                    mime_0.12                  
+##  [89] colorspace_2.1-1            scattermore_1.2            
+##  [91] gtools_3.9.5                tensor_1.5                 
+##  [93] spatstat.data_3.1-2         utf8_1.2.4                 
+##  [95] tidyr_1.3.1                 generics_0.1.3             
+##  [97] renv_1.0.7                  data.table_1.16.0          
+##  [99] httr_1.4.7                  htmlwidgets_1.6.4          
+## [101] S4Arrays_1.4.1              uwot_0.2.2                 
+## [103] pkgconfig_2.0.3             gtable_0.3.5               
+## [105] modeltools_0.2-23           lmtest_0.9-40              
+## [107] SingleCellExperiment_1.26.0 XVector_0.44.0             
+## [109] htmltools_0.5.8.1           dotCall64_1.1-1            
+## [111] scales_1.3.0                Biobase_2.64.0             
+## [113] png_0.1-8                   phyclust_0.1-34            
+## [115] spatstat.univar_3.0-1       knitr_1.48                 
+## [117] lambda.r_1.2.4              tzdb_0.4.0                 
+## [119] reshape2_1.4.4              coda_0.19-4.1              
+## [121] nlme_3.1-164                cachem_1.1.0               
+## [123] zoo_1.8-12                  stringr_1.5.1              
+## [125] KernSmooth_2.23-22          parallel_4.4.0             
+## [127] miniUI_0.1.1.1              libcoin_1.0-10             
+## [129] pillar_1.9.0                grid_4.4.0                 
+## [131] vctrs_0.6.5                 gplots_3.1.3.1             
+## [133] RANN_2.6.2                  promises_1.3.0             
+## [135] xtable_1.8-4                cluster_2.1.6              
+## [137] evaluate_1.0.0              readr_2.1.5                
+## [139] locfit_1.5-9.10             mvtnorm_1.3-1              
+## [141] cli_3.6.3                   compiler_4.4.0             
+## [143] futile.options_1.0.1        rlang_1.1.4                
+## [145] crayon_1.5.3                future.apply_1.11.2        
+## [147] labeling_0.4.3              argparse_2.2.3             
+## [149] plyr_1.8.9                  stringi_1.8.4              
+## [151] viridisLite_0.4.2           deldir_2.0-4               
+## [153] munsell_0.5.1               lazyeval_0.2.2             
+## [155] spatstat.geom_3.3-3         Matrix_1.7-0               
+## [157] RcppHNSW_0.6.0              hms_1.1.3                  
+## [159] patchwork_1.3.0             future_1.34.0              
+## [161] statmod_1.5.0               shiny_1.9.1                
+## [163] highr_0.11                  SummarizedExperiment_1.34.0
+## [165] ROCR_1.0-11                 igraph_2.0.3               
+## [167] RcppParallel_5.1.9          bslib_0.8.0                
+## [169] ape_5.8
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runCopyKat.R b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runCopyKat.R new file mode 100644 index 000000000..ed76beb48 --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runCopyKat.R @@ -0,0 +1,63 @@ +library(dplyr) +library(Seurat) +library(copykat) +library(ggplot2) + +path_repo <- rprojroot::find_root(rprojroot::is_git_root) +path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14") + +library <- "SCPCL000850" + +# create output dirs +scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","copykat") +dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE) +library_out_dir <- file.path(scratch_out_dir, library) +dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE) + +# results_out_dir <- file.path(path_anal, "results", "03_cnv") +# dir.create(results_out_dir, showWarnings = FALSE, recursive = TRUE) +# plots_out_dir <- file.path(path_anal, "plots", "03_cnv") +# dir.create(results_out_dir, showWarnings = FALSE, recursive = TRUE) + + +# load pre-processed sample objs & anchor transfer results +obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library,".rdsSeurat")) ) +level <- "compartment" +predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library, "_", level,".csv")) ) +obj <- AddMetaData(object = obj, metadata = predictions) + +# prepare copykat input matrix & normal cell list +count_mat <- SeuratObject::GetAssayData(obj, assay = "RNA", layer = "count") +normal_cells <- predictions %>% + tibble::column_to_rownames(var = "X") %>% + filter(predicted.id == "immune") + +# save copykat intermediate results by setting directory +setwd(library_out_dir) + +# run copykat with reference normal cells +copykat_result <- copykat( + rawmat = count_mat, + id.type = "E", + sam.name = library, + norm.cell.names = rownames(normal_cells), + ngene.chr = 2, + plot.genes = FALSE, + output.seg = FALSE, + n.cores = 8 +) +readr::write_rds(copykat_result, file = file.path(library_out_dir, paste0(library, "_copykat_resultobj.rds"))) + +# run copykat without normal cells +copykat_result_noref <- copykat( + rawmat = count_mat, + id.type = "E", + sam.name = paste0(library,"_noref"), + norm.cell.names = NULL, + ngene.chr = 2, + plot.genes = FALSE, + output.seg = FALSE, + n.cores = 8 +) +readr::write_rds(copykat_result_noref, file = file.path(library_out_dir, paste0(library, "_noref_copykat_resultobj.rds"))) + diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runInferCNV.R b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runInferCNV.R new file mode 100644 index 000000000..826ed578b --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/03_runInferCNV.R @@ -0,0 +1,57 @@ +library(dplyr) +library(Seurat) +library(infercnv) +library(ggplot2) + +path_repo <- rprojroot::find_root(rprojroot::is_git_root) +path_anal <- file.path(path_repo,"analyses","cell-type-wilms-tumor-14") + +library <- "SCPCL000850" + +# create output dirs +scratch_out_dir <- file.path(path_anal, "scratch", "03_cnv","infercnv") +dir.create(scratch_out_dir, showWarnings = FALSE, recursive = TRUE) +library_out_dir <- file.path(scratch_out_dir, library) +dir.create(library_out_dir, showWarnings = FALSE, recursive = TRUE) + +# results_out_dir <- file.path(path_anal, "results", "03_cnv") +# dir.create(results_out_dir, showWarnings = FALSE, recursive = TRUE) +# plots_out_dir <- file.path(path_anal, "plots", "03_cnv") +# dir.create(results_out_dir, showWarnings = FALSE, recursive = TRUE) + + +# load pre-processed sample objs & anchor transfer results +obj <- SeuratObject::LoadSeuratRds( file.path(path_anal,"scratch","00_preprocessing_rds",paste0(library,".rdsSeurat")) ) +level <- "compartment" +predictions <- read.csv( file.path(path_anal, "results", "01_anchor_transfer_seurat", paste0(library, "_", level,".csv")) ) +obj <- AddMetaData(object = obj, metadata = predictions) + +# create annotation file for infercnv +annotation_file <- file.path(scratch_out_dir, paste0(library,"_annotations_file_infercnv.txt")) +# annotation_df <- data.frame(cell = obj$barcodes, cluster = obj$cluster) +annotation_df <- data.frame(cell = obj$barcodes, cluster = obj$predicted.id) +write.table(annotation_df, file = annotation_file, sep = "\t", quote = F, row.names = F, col.names = F) +# check gene order file, from infercnv ftp +gene_order_file <- file.path(scratch_out_dir,"Homo_sapiens.GRCh38.104.gene_order.txt") +gene_order <- read.table(file = gene_order_file) %>% + arrange(V2, V3) + +obj <- obj[rownames(obj) %in% gene_order$V1] +count_mat <- SeuratObject::GetAssayData(obj, assay = "RNA", layer = "count") + +infercnv_obj = CreateInfercnvObject(raw_counts_matrix=count_mat, + annotations_file=annotation_file, + delim="\t", + gene_order_file=gene_order_file, + ref_group_names="immune") + + +infercnv_out = infercnv::run(infercnv_obj, + cutoff = 0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics + out_dir = library_out_dir, + denoise = TRUE, + HMM = TRUE, + save_rds = FALSE, + num_threads = 8) + +readr::write_rds(infercnv_out, file = file.path(library_out_dir,"infercnv_out.rds")) \ No newline at end of file diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/README.md b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/README.md new file mode 100644 index 000000000..5b7f96377 --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/03_cnv/README.md @@ -0,0 +1,34 @@ +## Exploratory analysis + +#### CNV analysis +The goal of this exploratory CNV analysis is to see if `CopyKat` and `inferCNV` can be useful methods for predicting tumor cells based on ploidy. This analysis uses the library `SCPCL000850` (sample `SCPCS000518`) to explore these methods. + +See below for details on how to run this exploratory CNV analysis. +- Note: Outputs from main pipeline step `00_preprocessing_rds` for library `SCPCL000850` are required to run the following analysis, see [scripts](https://github.com/AlexsLemonade/OpenScPCA-analysis/blob/main/analyses/cell-type-wilms-tumor-14/run_cell-type-wilms-14.sh) to run main pipeline. +```bash +# run this analysis from analysis module folder to make use of renv.lock +cd /path/to/OpenScPCA-analysis +cd analyses/cell-type-wilms-tumor-14/ + +# required for rendering Rmd files. +# This path exists on OpenScPCA Lightsail for Research instances, but you may need to specify a different path if running on a different platform, or run `rmarkdown::render` inside your rstudio session. +export RSTUDIO_PANDOC="/usr/lib/rstudio/resources/app/bin/quarto/bin/tools" + +## CopyKat +# generate CNV results using copykat +Rscript ./exploratory_analysis/03_cnv/03_runCopyKat.R +# render Rmd +Rscript -e "rmarkdown::render('./exploratory_analysis/03_cnv/03_copykat_SCPCL000850.Rmd')" + +## inferCNV +# generate inferCNV inputs +step_name="03_cnv" +scratch_dir_step="./scratch/${step_name}/infercnv" && mkdir -p ${scratch_dir_step} +Rscript ../cell-type-ewings/scripts/cnv-workflow/00-make-gene-order-file.R \ + --local_ref_dir ${scratch_dir_step} \ + --scratch_dir ${scratch_dir_step} +# run inferCNV (takes a while) +Rscript ./exploratory_analysis/03_cnv/03_runInferCNV.R +# render Rmd +Rscript -e "rmarkdown::render('./exploratory_analysis/03_cnv/03_infercnv_SCPCL000850.Rmd')" +``` \ No newline at end of file diff --git a/analyses/cell-type-wilms-tumor-14/exploratory_analysis/README.md b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/README.md new file mode 100644 index 000000000..c1adcb2f2 --- /dev/null +++ b/analyses/cell-type-wilms-tumor-14/exploratory_analysis/README.md @@ -0,0 +1,4 @@ +## Exploratory analysis + +This folder includes scripts and notebooks for exploratory analysis. List of conducted analysis: +- `./03_cnv/`: CNV inference using `CopyKat` and `inferCNV`. \ No newline at end of file diff --git a/analyses/cell-type-wilms-tumor-14/renv.lock b/analyses/cell-type-wilms-tumor-14/renv.lock index a0e14bda8..1705449df 100644 --- a/analyses/cell-type-wilms-tumor-14/renv.lock +++ b/analyses/cell-type-wilms-tumor-14/renv.lock @@ -66,6 +66,20 @@ ], "Hash": "ef32d07aafdd12f24c5827374ae3590d" }, + "BiocIO": { + "Package": "BiocIO", + "Version": "1.14.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "R", + "S4Vectors", + "methods", + "tools" + ], + "Hash": "f97a7ef01d364cf20d1946d43a3d526f" + }, "BiocManager": { "Package": "BiocManager", "Version": "1.30.25", @@ -76,6 +90,25 @@ ], "Hash": "3aec5928ca10897d7a0a1205aae64627" }, + "BiocParallel": { + "Package": "BiocParallel", + "Version": "1.38.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BH", + "R", + "codetools", + "cpp11", + "futile.logger", + "methods", + "parallel", + "snow", + "stats", + "utils" + ], + "Hash": "7b6e79f86e3d1c23f62c5e2052e848d4" + }, "BiocVersion": { "Package": "BiocVersion", "Version": "3.19.1", @@ -86,6 +119,26 @@ ], "Hash": "b892e27fc9659a4c8f8787d34c37b8b2" }, + "Biostrings": { + "Package": "Biostrings", + "Version": "2.72.1", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "GenomeInfoDb", + "IRanges", + "R", + "S4Vectors", + "XVector", + "crayon", + "grDevices", + "methods", + "stats", + "utils" + ], + "Hash": "886ff0ed958d6f839ed2e0d01f6853b3" + }, "DelayedArray": { "Package": "DelayedArray", "Version": "0.30.1", @@ -154,6 +207,28 @@ ], "Hash": "c3c792a7b7f2677be56e8632c5b7543d" }, + "GenomicAlignments": { + "Package": "GenomicAlignments", + "Version": "1.40.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "BiocParallel", + "Biostrings", + 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