Comparison of chromap and cellranger quantifications over master union peak set

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Last updated: 2022-09-14

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Knit directory: chromap_vs_cellranger_scATAC_exploration_10x/

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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()
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library(GenomicRanges)

Loading required package: stats4

Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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    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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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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Loading required package: AnnotationFilter

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library(stringi)
library(plyranges)

Attaching package: 'plyranges'

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Create one unified union set of all peak coordinates (both approaches)

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")

Read in cCRE peaks to join

cre <- read.table("cCRE_hg38.bed")
colnames(cre) <- c("chr","start","end")
cre.gr <- makeGRangesFromDataFrame(cre)
cre.gr <- keepStandardChromosomes(cre.gr,pruning.mode="coarse")

Create master union set

all.union <- union_ranges(union_ranges(cr.union.all.gr,cm.peaks.all.gr), cre.gr)

# Filter out bad peaks based on length
peakwidths <- width(all.union)
all.union <- all.union[peakwidths  < 10000 & peakwidths > 20]

Compute cellranger-derived coverage over this master union peak set

Extract gene annotations from EnsDb

annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)

Warning in .Seqinfo.mergexy(x, y): The 2 combined objects have no sequence levels in common. (Use
  suppressWarnings() to suppress this warning.)

Warning in .Seqinfo.mergexy(x, y): The 2 combined objects have no sequence levels in common. (Use
  suppressWarnings() to suppress this warning.)

Warning in .Seqinfo.mergexy(x, y): The 2 combined objects have no sequence levels in common. (Use
  suppressWarnings() to suppress this warning.)

Warning in .Seqinfo.mergexy(x, y): The 2 combined objects have no sequence levels in common. (Use
  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

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  suppressWarnings() to suppress this warning.)

seqlevelsStyle(annotations) <- 'UCSC'

Import PBMC cellranger data

Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for you

Computing hash

Warning in CreateSeuratObject.Assay(counts = PBMC_cr_chrom_assay, assay =
"peaks", : Some cells in meta.data not present in provided counts matrix.

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from peaks to peaks_

PBMC_cr_counts <- Read10X_h5(filename = "10x_PBMC_cellranger/outs/filtered_peak_bc_matrix.h5")

PBMC_cr_metadata <- read.csv(
  file = "10x_PBMC_cellranger/outs/singlecell.csv",
  header = TRUE,
  row.names = 1
)

PBMC_cr_chrom_assay <- CreateChromatinAssay(
  counts = PBMC_cr_counts,
  sep = c(":", "-"),
  genome = 'hg38',
  fragments = '10x_PBMC_cellranger/outs/fragments.tsv.gz',
  min.cells = 10,
  min.features = 200
)

PBMC_cr_seurat <- CreateSeuratObject(
  counts = PBMC_cr_chrom_assay,
  assay = "peaks",
  meta.data = PBMC_cr_metadata
)

Add gene annotation information to the object and prepend cell type on cell barcodes

Annotation(PBMC_cr_seurat) <- annotations

PBMC_cr_seurat$Sample <- "PBMC"
PBMC_cr_seurat <- RenameCells(PBMC_cr_seurat, add.cell.id = "PBMC")

Import HGMM cellranger data

Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for you

Computing hash

Warning in CreateSeuratObject.Assay(counts = HGMM_cr_chrom_assay, assay =
"peaks", : Some cells in meta.data not present in provided counts matrix.

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from peaks to peaks_

HGMM_cr_counts <- Read10X_h5(filename = "10x_HGMM_cellranger/outs/filtered_peak_bc_matrix.h5")

HGMM_cr_metadata <- read.csv(
  file = "10x_HGMM_cellranger/outs/singlecell.csv",
  header = TRUE,
  row.names = 1
)

HGMM_cr_chrom_assay <- CreateChromatinAssay(
  counts = HGMM_cr_counts,
  sep = c(":", "-"),
  genome = 'hg38',
  fragments = '10x_HGMM_cellranger/outs/fragments.tsv.gz',
  min.cells = 10,
  min.features = 200
)

HGMM_cr_seurat <- CreateSeuratObject(
  counts = HGMM_cr_chrom_assay,
  assay = "peaks",
  meta.data = HGMM_cr_metadata
)

Add gene annotation information to the object and prepend cell type on cell barcodes

Annotation(HGMM_cr_seurat) <- annotations

HGMM_cr_seurat$Sample <- "HGMM"
HGMM_cr_seurat <- RenameCells(HGMM_cr_seurat, add.cell.id = "HGMM")

Compute feature matrix for master union set of peaks

HGMM.counts <- FeatureMatrix(
  fragments = Fragments(HGMM_cr_seurat),
  features = all.union,
  cells = colnames(HGMM_cr_seurat)
)

Extracting reads overlapping genomic regions

PBMC.counts <- FeatureMatrix(
  fragments = Fragments(PBMC_cr_seurat),
  features = all.union,
  cells = colnames(PBMC_cr_seurat)
)

Extracting reads overlapping genomic regions

Create new chromatin assays and seurat objects

HGMM_assay <- CreateChromatinAssay(HGMM.counts, fragments = Fragments(HGMM_cr_seurat))
HGMM_seurat <- CreateSeuratObject(HGMM_assay, assay = "ATAC", meta.data=as.data.frame(HGMM_cr_seurat@meta.data))

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from atac to atac_

HGMM_seurat$Sample <- "HGMM"

PBMC_assay <- CreateChromatinAssay(PBMC.counts, fragments = Fragments(PBMC_cr_seurat))
PBMC_seurat <- CreateSeuratObject(PBMC_assay, assay = "ATAC", meta.data=as.data.frame(PBMC_cr_seurat@meta.data))

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from atac to atac_

PBMC_seurat$Sample <- "PBMC"

cellranger.combined <- merge(HGMM_seurat, y = PBMC_seurat)

Compute chromap-derived coverage over this master union peak set

Compute for PBMC chromap data

Computing hash

Extracting reads overlapping genomic regions

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from peaks to peaks_

PBMC_10x_path <- "10x_PBMC_chromap_fragments.tsv.gz"
PBMC_10x_cells <- read_tsv(PBMC_10x_path,col_names=c("chr","start","stop","cell","support"),col_types=c("-","-","-","-","c"),col_select="cell") %>% pull(cell) %>% unique()
names(x = PBMC_10x_cells) <- paste0("PBMC_", PBMC_10x_cells)
PBMC_10x_frags <- CreateFragmentObject(path = PBMC_10x_path, cells = PBMC_10x_cells, max.lines=NULL)
PBMC_10x_mat <- FeatureMatrix(
  fragments = PBMC_10x_frags,
  features = all.union,
  process_n = 20000,
  sep = c("-", "-"),
  verbose = TRUE
)
PBMC_10x_assay <- CreateChromatinAssay(PBMC_10x_mat, fragments = PBMC_10x_frags, genome = 'hg38', min.features = 500)
PBMC_10x_seurat <- CreateSeuratObject(PBMC_10x_assay, assay = "peaks")
PBMC_10x_seurat$Sample <- "PBMC"

Compute for HGMM chromap data

Computing hash

Extracting reads overlapping genomic regions

Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from peaks to peaks_

HGMM_10x_path <- "10x_HGMM_chromap_fragments.tsv.gz"
HGMM_10x_cells <- read_tsv(HGMM_10x_path,col_names=c("chr","start","stop","cell","support"),col_types=c("-","-","-","-","c"),col_select="cell") %>% pull(cell) %>% unique()
names(x = HGMM_10x_cells) <- paste0("HGMM_", HGMM_10x_cells)
HGMM_10x_frags <- CreateFragmentObject(path = HGMM_10x_path, cells = HGMM_10x_cells, max.lines=NULL)
HGMM_10x_mat <- FeatureMatrix(
  fragments = HGMM_10x_frags,
  features = all.union,
  process_n = 20000,
  sep = c("-", "-"),
  verbose = TRUE
)
HGMM_10x_assay <- CreateChromatinAssay(HGMM_10x_mat, fragments = HGMM_10x_frags, genome = 'hg38', min.features = 500)
HGMM_10x_seurat <- CreateSeuratObject(HGMM_10x_assay, assay = "peaks")
HGMM_10x_seurat$Sample <- "HGMM"

Merge chromap objects

chromap.combined <- merge(HGMM_10x_seurat, y = PBMC_10x_seurat)

Compare coverage from both approaches

Rename cellranger cell names

cr.names <- colnames(cellranger.combined)
cr.newnames <- str_replace_all(cr.names,"-1","")
cellranger.combined <- RenameCells(cellranger.combined,new.names = cr.newnames)

Take reverse complement of chromap barcode sequences

cm.names <- colnames(chromap.combined)

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)
chromap.combined <- RenameCells(chromap.combined, new.names = cm.newnames)

Reduce each matrix down to set of common barcodes and features

intercells <- intersect(colnames(cellranger.combined),colnames(chromap.combined))
intergenes <- intersect(rownames(cellranger.combined),rownames(chromap.combined))

cr.subset <- cellranger.combined@assays$ATAC@counts[intergenes,intercells]
cm.subset <- chromap.combined@assays$peaks@counts[intergenes,intercells]

Compute cell-by-cell correlations between cellranger and chromap feature coverage

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 peak signal

hist(cors,xlab="Spearman correlation",main="Union peak set correlations, Cellranger vs Chromap, common cells")

Session information

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] plyranges_1.14.0          stringi_1.7.6            
 [3] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.3         
 [5] AnnotationFilter_1.18.0   GenomicFeatures_1.46.5   
 [7] AnnotationDbi_1.56.2      Biobase_2.54.0           
 [9] Signac_1.7.0.9003         SeuratObject_4.0.4       
[11] Seurat_4.1.0              GenomicRanges_1.46.1     
[13] GenomeInfoDb_1.30.1       IRanges_2.28.0           
[15] S4Vectors_0.32.4          BiocGenerics_0.40.0      
[17] forcats_0.5.1             stringr_1.4.0            
[19] dplyr_1.0.9               purrr_0.3.4              
[21] readr_2.1.2               tidyr_1.2.0              
[23] tibble_3.1.8              ggplot2_3.3.6            
[25] tidyverse_1.3.1           workflowr_1.7.0          

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3              rtracklayer_1.54.0         
  [3] scattermore_0.8             bit64_4.0.5                
  [5] knitr_1.37                  irlba_2.3.5                
  [7] DelayedArray_0.20.0         data.table_1.14.2          
  [9] rpart_4.1.16                KEGGREST_1.34.0            
 [11] RCurl_1.98-1.6              generics_0.1.2             
 [13] callr_3.7.0                 cowplot_1.1.1              
 [15] RSQLite_2.2.10              RANN_2.6.1                 
 [17] future_1.24.0               bit_4.0.4                  
 [19] tzdb_0.2.0                  spatstat.data_2.1-2        
 [21] xml2_1.3.3                  lubridate_1.8.0            
 [23] httpuv_1.6.5                SummarizedExperiment_1.24.0
 [25] assertthat_0.2.1            xfun_0.30                  
 [27] hms_1.1.1                   jquerylib_0.1.4            
 [29] evaluate_0.15               promises_1.2.0.1           
 [31] fansi_1.0.3                 restfulr_0.0.13            
 [33] progress_1.2.2              dbplyr_2.1.1               
 [35] readxl_1.3.1                igraph_1.3.3               
 [37] DBI_1.1.2                   htmlwidgets_1.5.4          
 [39] spatstat.geom_2.3-2         ellipsis_0.3.2             
 [41] backports_1.4.1             biomaRt_2.50.3             
 [43] deldir_1.0-6                MatrixGenerics_1.6.0       
 [45] vctrs_0.4.1                 ROCR_1.0-11                
 [47] abind_1.4-5                 cachem_1.0.6               
 [49] withr_2.5.0                 BSgenome_1.62.0            
 [51] checkmate_2.0.0             vroom_1.5.7                
 [53] sctransform_0.3.3           GenomicAlignments_1.30.0   
 [55] prettyunits_1.1.1           goftest_1.2-3              
 [57] cluster_2.1.2               lazyeval_0.2.2             
 [59] crayon_1.5.1                hdf5r_1.3.5                
 [61] pkgconfig_2.0.3             nlme_3.1-155               
 [63] ProtGenerics_1.26.0         nnet_7.3-17                
 [65] rlang_1.0.4                 globals_0.14.0             
 [67] lifecycle_1.0.1             miniUI_0.1.1.1             
 [69] filelock_1.0.2              BiocFileCache_2.2.1        
 [71] modelr_0.1.8                dichromat_2.0-0            
 [73] cellranger_1.1.0            rprojroot_2.0.2            
 [75] polyclip_1.10-0             matrixStats_0.62.0         
 [77] lmtest_0.9-40               Matrix_1.4-0               
 [79] zoo_1.8-9                   reprex_2.0.1               
 [81] base64enc_0.1-3             whisker_0.4                
 [83] ggridges_0.5.3              processx_3.5.2             
 [85] png_0.1-7                   viridisLite_0.4.0          
 [87] rjson_0.2.21                bitops_1.0-7               
 [89] getPass_0.2-2               KernSmooth_2.23-20         
 [91] Biostrings_2.62.0           blob_1.2.2                 
 [93] parallelly_1.30.0           spatstat.random_2.1-0      
 [95] jpeg_0.1-9                  scales_1.2.0               
 [97] memoise_2.0.1               magrittr_2.0.2             
 [99] plyr_1.8.7                  ica_1.0-2                  
[101] zlibbioc_1.40.0             compiler_4.1.0             
[103] BiocIO_1.4.0                RColorBrewer_1.1-3         
[105] fitdistrplus_1.1-6          Rsamtools_2.10.0           
[107] cli_3.3.0                   XVector_0.34.0             
[109] listenv_0.8.0               patchwork_1.1.1            
[111] pbapply_1.5-0               ps_1.6.0                   
[113] htmlTable_2.4.0             Formula_1.2-4              
[115] MASS_7.3-55                 mgcv_1.8-40                
[117] tidyselect_1.1.2            highr_0.9                  
[119] yaml_2.3.5                  latticeExtra_0.6-29        
[121] ggrepel_0.9.1               grid_4.1.0                 
[123] sass_0.4.0                  VariantAnnotation_1.40.0   
[125] fastmatch_1.1-3             tools_4.1.0                
[127] future.apply_1.8.1          parallel_4.1.0             
[129] rstudioapi_0.13             foreign_0.8-82             
[131] git2r_0.30.1                gridExtra_2.3              
[133] Rtsne_0.15                  digest_0.6.29              
[135] shiny_1.7.1                 Rcpp_1.0.8.3               
[137] broom_1.0.0                 later_1.3.0                
[139] RcppAnnoy_0.0.19            httr_1.4.2                 
[141] biovizBase_1.42.0           colorspace_2.0-3           
[143] rvest_1.0.2                 XML_3.99-0.9               
[145] fs_1.5.2                    tensor_1.5                 
[147] reticulate_1.25             splines_4.1.0              
[149] uwot_0.1.11                 RcppRoll_0.3.0             
[151] spatstat.utils_2.3-0        plotly_4.10.0              
[153] xtable_1.8-4                jsonlite_1.8.0             
[155] R6_2.5.1                    Hmisc_4.6-0                
[157] pillar_1.7.0                htmltools_0.5.2            
[159] mime_0.12                   glue_1.6.2                 
[161] fastmap_1.1.0               BiocParallel_1.28.3        
[163] codetools_0.2-18            utf8_1.2.2                 
[165] lattice_0.20-45             bslib_0.3.1                
[167] spatstat.sparse_2.1-0       curl_4.3.2                 
[169] leiden_0.3.9                survival_3.2-13            
[171] rmarkdown_2.12              munsell_0.5.0              
[173] GenomeInfoDbData_1.2.7      haven_2.4.3                
[175] reshape2_1.4.4              gtable_0.3.0               
[177] spatstat.core_2.4-0