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Rmd 10fdcb0 jeremymsimon 2022-09-14 Initial commit

Load packages and saved workspaces from chromap and cellranger processing

library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.8     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(GenomicRanges)
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'
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    combine, intersect, setdiff, union
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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
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Attaching package: 'IRanges'
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Loading required package: GenomeInfoDb
library(Seurat)
Attaching SeuratObject
library(Signac)
library(EnsDb.Hsapiens.v86)
Loading required package: ensembldb
Loading required package: GenomicFeatures
Loading required package: AnnotationDbi
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")'.

Attaching package: 'AnnotationDbi'
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    select
Loading required package: AnnotationFilter

Attaching package: 'ensembldb'
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    filter
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    filter
library(stringi)
library(ggsankey)
library(plyranges)

Attaching package: 'plyranges'
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    filter, select
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library(reticulate)
use_python("/nas/longleaf/apps/python/3.7.9/bin/python")
load("Cellranger_HGMM_PBMC_seurat_090222_QC_integrated.RData")
load("cCRE_hg38_10x_HGMM_PBMC_chromap_fragments_MACS_q01_unionPeaks_merge100_seurat_090222_QC_integrated.RData")

Get cluster assignments for each cell barcode from each approach

clust.cr <- cellranger.integrated$seurat_clusters
clust.cm <- chromap.integrated$seurat_clusters

Tidy cluster assignments, including reverse complement of chromap-derived barcodes

This step ensures they match the include list used by cellranger, as per this discussion This may be changed in future versions and/or dependent on whether chromap was run with an include list of barcode sequences or barcode translation table (ie for multi-omics)

clust.cr.tbl <- enframe(clust.cr) %>% 
    dplyr::rename("Barcode" = name, "Cellranger.cluster" = value) %>%
    mutate(Cellranger.cluster = as.numeric(as.character(Cellranger.cluster))) %>%
    mutate(Barcode = str_replace_all(Barcode,"-1$",""))

clust.cm.tbl <- enframe(clust.cm) %>% 
    dplyr::rename("Barcode" = name, "Chromap.cluster" = value) %>%
    mutate(Chromap.cluster = as.numeric(as.character(Chromap.cluster))) %>%
    separate(Barcode,c("Sample","Seq"),sep="_") %>%
    mutate(RC = stringi::stri_reverse(chartr(old="ATGC", new="TACG", Seq))) %>%
    unite("Barcode",c(Sample,RC),sep="_") %>%
    dplyr::select(-Seq)

Make Sankey plot showing cluster identity concordance

This plot includes all cells recovered by each approach, not just the common ones (ie a full_join) It is possible there is a discrepancy in how the two algorithms are performing barcode correction; here I’m matching by exact sequence matches

make_long (
    full_join(clust.cr.tbl,clust.cm.tbl,by="Barcode"),
    Cellranger.cluster,Chromap.cluster) %>%
    ggplot(aes(x = x, 
        next_x = next_x, 
        node = node, 
        next_node = next_node,
        fill = factor(node),
        label = node)) +
        geom_sankey() +
        theme_sankey(base_size = 18) +
        theme(legend.position = "none") +
        xlab("") +
        geom_sankey_text(size = 3, color = "black") +
        scale_x_discrete(breaks = c("Cellranger.cluster","Chromap.cluster"),labels = c("Cellranger clusters","Chromap clusters"))
Warning: Removed 2 rows containing missing values (geom_text).

Now use gene activities (“RNA” assay) as a means of comparing alignment signal across the genome

Repeat same barcode renaming as we did above

# Cellranger gene counts
cr.genes <- cellranger.integrated@assays$RNA@data
cr.names <- colnames(cr.genes)
cr.newnames <- str_replace_all(cr.names,"-1","")
colnames(cr.genes) <- cr.newnames


# Chromap gene counts
cm.genes <- chromap.integrated@assays$RNA@data
cm.names <- colnames(cm.genes)
snames <- str_replace_all(cm.names,"_.+","")
seq <- str_replace_all(cm.names,".+_(.+)","\\1")
rc <- stringi::stri_reverse(chartr(old="ATGC", new="TACG", seq))
cm.newnames <- paste0(snames,"_",rc)
colnames(cm.genes) <- cm.newnames

# Reduce each matrix down to set of common barcodes and features
intercells <- intersect(colnames(cr.genes),colnames(cm.genes))
intergenes <- intersect(rownames(cr.genes),rownames(cm.genes))

cr.subset <- cr.genes[intergenes,intercells]
cm.subset <- cm.genes[intergenes,intercells]

Compute cell-by-cell correlations between cellranger and chromap gene activities

Performs slowly with a for-loop, however we do not want to densify our sparse matrices here

cors <- rep(NA,length(intercells))

for(i in 1:length(intercells)) {
    cors[i] <- cor(as.numeric(cr.subset[,i]), as.numeric(cm.subset[,i]),method="spearman")
}

Draw histogram of cell-by-cell correlations based on gene activity

hist(cors,xlab="Spearman correlation",main="Gene Activity correlations, Cellranger vs Chromap, common cells")

Export cluster assignments for python-based metrics

Filter for just common cell barcodes between two datasets. Make sure barcodes are printed in the same order!

clust.cr.tbl %>%
    dplyr::filter(Barcode %in% intercells) %>%
    arrange(Barcode) %>%
    write_csv("cellranger_clusters_intersectingCells.csv")

clust.cm.tbl %>%
    dplyr::filter(Barcode %in% intercells) %>%
    arrange(Barcode) %>%
    write_csv("chromap_clusters_intersectingCells.csv")

Now in python, compute NMI and ARI to summarize cluster membership similarities for common cells

from sklearn.metrics.cluster import normalized_mutual_info_score
from sklearn.metrics.cluster import adjusted_rand_score
import pandas as pd
cr_df = pd.read_csv('cellranger_clusters_intersectingCells.csv')
cr_clusters = cr_df['Cellranger.cluster'].values.tolist()

cm_df = pd.read_csv('chromap_clusters_intersectingCells.csv')
cm_clusters = cm_df['Chromap.cluster'].values.tolist()
normalized_mutual_info_score(cr_clusters, cm_clusters)
0.7580365956216562
adjusted_rand_score(cr_clusters, cm_clusters)
0.5655073895903598

Compute percent overlap of peak calls between cellranger and chromap-MACS2 (excluding Zhang et. al cCRE features)

Load in all original peaks

HGMM.cr.peaks.all <- read.table("10x_HGMM_cellranger/outs/peaks.bed")
colnames(HGMM.cr.peaks.all) <- c("chr","start","end")
HGMM.cr.peaks.all.gr <- makeGRangesFromDataFrame(HGMM.cr.peaks.all)
HGMM.cr.peaks.all.gr <- keepStandardChromosomes(HGMM.cr.peaks.all.gr,pruning.mode="coarse")

PBMC.cr.peaks.all <- read.table("10x_PBMC_cellranger/outs/peaks.bed")
colnames(PBMC.cr.peaks.all) <- c("chr","start","end")
PBMC.cr.peaks.all.gr <- makeGRangesFromDataFrame(PBMC.cr.peaks.all)
PBMC.cr.peaks.all.gr <- keepStandardChromosomes(PBMC.cr.peaks.all.gr,pruning.mode="coarse")

Make union set of all cellranger peaks with plyranges

cr.union.all.gr <- union_ranges(HGMM.cr.peaks.all.gr,PBMC.cr.peaks.all.gr)

Read in chromap-MACS2 peaks (already a union of both samples)

cm.peaks.all <- read.table("10x_HGMM_PBMC_chromap_fragments_MACS_q01_unionPeaks.bed")
colnames(cm.peaks.all) <- c("chr","start","end")
cm.peaks.all.gr <- makeGRangesFromDataFrame(cm.peaks.all)
cm.peaks.all.gr <- keepStandardChromosomes(cm.peaks.all.gr,pruning.mode="coarse")

Compute overlap as a fraction of the chromap-derived peaks

length(join_overlap_inner(cm.peaks.all.gr,cr.union.all.gr)) / length(cm.peaks.all.gr)
[1] 0.8548084

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.5 (Ootpa)

Matrix products: default
BLAS/LAPACK: /nas/longleaf/rhel8/apps/r/4.1.0/lib/libopenblas_haswellp-r0.3.5.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] reticulate_1.25           plyranges_1.14.0         
 [3] ggsankey_0.0.99999        stringi_1.7.6            
 [5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.3         
 [7] AnnotationFilter_1.18.0   GenomicFeatures_1.46.5   
 [9] AnnotationDbi_1.56.2      Biobase_2.54.0           
[11] Signac_1.7.0.9003         SeuratObject_4.0.4       
[13] Seurat_4.1.0              GenomicRanges_1.46.1     
[15] GenomeInfoDb_1.30.1       IRanges_2.28.0           
[17] S4Vectors_0.32.4          BiocGenerics_0.40.0      
[19] forcats_0.5.1             stringr_1.4.0            
[21] dplyr_1.0.9               purrr_0.3.4              
[23] readr_2.1.2               tidyr_1.2.0              
[25] tibble_3.1.8              ggplot2_3.3.6            
[27] tidyverse_1.3.1           workflowr_1.7.0          

loaded via a namespace (and not attached):
  [1] utf8_1.2.2                  tidyselect_1.1.2           
  [3] RSQLite_2.2.10              htmlwidgets_1.5.4          
  [5] grid_4.1.0                  BiocParallel_1.28.3        
  [7] Rtsne_0.15                  munsell_0.5.0              
  [9] codetools_0.2-18            ica_1.0-2                  
 [11] future_1.24.0               miniUI_0.1.1.1             
 [13] withr_2.5.0                 spatstat.random_2.1-0      
 [15] colorspace_2.0-3            filelock_1.0.2             
 [17] highr_0.9                   knitr_1.37                 
 [19] rstudioapi_0.13             ROCR_1.0-11                
 [21] tensor_1.5                  listenv_0.8.0              
 [23] labeling_0.4.2              MatrixGenerics_1.6.0       
 [25] git2r_0.30.1                GenomeInfoDbData_1.2.7     
 [27] polyclip_1.10-0             farver_2.1.0               
 [29] bit64_4.0.5                 rprojroot_2.0.2            
 [31] parallelly_1.30.0           vctrs_0.4.1                
 [33] generics_0.1.2              xfun_0.30                  
 [35] BiocFileCache_2.2.1         R6_2.5.1                   
 [37] DelayedArray_0.20.0         bitops_1.0-7               
 [39] spatstat.utils_2.3-0        cachem_1.0.6               
 [41] assertthat_0.2.1            vroom_1.5.7                
 [43] BiocIO_1.4.0                promises_1.2.0.1           
 [45] scales_1.2.0                gtable_0.3.0               
 [47] globals_0.14.0              processx_3.5.2             
 [49] goftest_1.2-3               rlang_1.0.4                
 [51] RcppRoll_0.3.0              splines_4.1.0              
 [53] rtracklayer_1.54.0          lazyeval_0.2.2             
 [55] spatstat.geom_2.3-2         broom_1.0.0                
 [57] yaml_2.3.5                  reshape2_1.4.4             
 [59] abind_1.4-5                 modelr_0.1.8               
 [61] backports_1.4.1             httpuv_1.6.5               
 [63] tools_4.1.0                 ellipsis_0.3.2             
 [65] spatstat.core_2.4-0         jquerylib_0.1.4            
 [67] RColorBrewer_1.1-3          ggridges_0.5.3             
 [69] Rcpp_1.0.8.3                plyr_1.8.7                 
 [71] progress_1.2.2              zlibbioc_1.40.0            
 [73] RCurl_1.98-1.6              prettyunits_1.1.1          
 [75] ps_1.6.0                    rpart_4.1.16               
 [77] deldir_1.0-6                pbapply_1.5-0              
 [79] cowplot_1.1.1               zoo_1.8-9                  
 [81] SummarizedExperiment_1.24.0 haven_2.4.3                
 [83] ggrepel_0.9.1               cluster_2.1.2              
 [85] here_1.0.1                  fs_1.5.2                   
 [87] magrittr_2.0.2              data.table_1.14.2          
 [89] scattermore_0.8             lmtest_0.9-40              
 [91] reprex_2.0.1                RANN_2.6.1                 
 [93] whisker_0.4                 ProtGenerics_1.26.0        
 [95] fitdistrplus_1.1-6          matrixStats_0.62.0         
 [97] hms_1.1.1                   patchwork_1.1.1            
 [99] mime_0.12                   evaluate_0.15              
[101] xtable_1.8-4                XML_3.99-0.9               
[103] readxl_1.3.1                gridExtra_2.3              
[105] biomaRt_2.50.3              compiler_4.1.0             
[107] KernSmooth_2.23-20          crayon_1.5.1               
[109] htmltools_0.5.2             mgcv_1.8-40                
[111] later_1.3.0                 tzdb_0.2.0                 
[113] lubridate_1.8.0             DBI_1.1.2                  
[115] dbplyr_2.1.1                rappdirs_0.3.3             
[117] MASS_7.3-55                 Matrix_1.4-0               
[119] cli_3.3.0                   parallel_4.1.0             
[121] igraph_1.3.3                pkgconfig_2.0.3            
[123] GenomicAlignments_1.30.0    getPass_0.2-2              
[125] plotly_4.10.0               spatstat.sparse_2.1-0      
[127] xml2_1.3.3                  bslib_0.3.1                
[129] XVector_0.34.0              rvest_1.0.2                
[131] callr_3.7.0                 digest_0.6.29              
[133] sctransform_0.3.3           RcppAnnoy_0.0.19           
[135] spatstat.data_2.1-2         Biostrings_2.62.0          
[137] rmarkdown_2.12              cellranger_1.1.0           
[139] leiden_0.3.9                fastmatch_1.1-3            
[141] uwot_0.1.11                 restfulr_0.0.13            
[143] curl_4.3.2                  shiny_1.7.1                
[145] Rsamtools_2.10.0            rjson_0.2.21               
[147] lifecycle_1.0.1             nlme_3.1-155               
[149] jsonlite_1.8.0              viridisLite_0.4.0          
[151] fansi_1.0.3                 pillar_1.7.0               
[153] lattice_0.20-45             KEGGREST_1.34.0            
[155] fastmap_1.1.0               httr_1.4.2                 
[157] survival_3.2-13             glue_1.6.2                 
[159] png_0.1-7                   bit_4.0.4                  
[161] sass_0.4.0                  blob_1.2.2                 
[163] memoise_2.0.1               irlba_2.3.5                
[165] future.apply_1.8.1