Error in (function (classes, fdef, mtable) about sierra

[davhum]

Hi Nelson,

Thanks for trying out our new package. We suspect the issue you are experiencing is due to the passing of a fasta file as a genome object. i.e. this line

genome <- "/media/ot/Data/Nelson/refdata-cellranger-GRCh38-3.0.0/fasta"

Instead you should set the genome object to a BSgenome S4 data object from the appropriate BSgenome package. i.e.

genome <- BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38

If you don't already have this package you can download it via the following commands:

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")

BiocManager::install("BSgenome.Hsapiens.UCSC.hg38")

Let us know if this fixes the problem.
Regards,
Dave

[zezhuo]

Hi Dave,

Thanks for your prompt reply. It solved the problem.

But when I move on to SplitBam function, No data found. I have tested several genes. Bam file is from the output of CellRanger. What is the problem with my data set?

Regards,
Nelson

SplitBam function:

> str(cells.df)
'data.frame':	1872 obs. of  2 variables:
 $ celltype: Factor w/ 4 levels "cartilaginous",..: 1 3 3 3 3 3 1 3 3 2 ...
 $ cellbc  : chr  "AAACCTGGTTAAGGGC" "AAACCTGTCGAGAGCA" "AAACGGGAGAACAATC" "AAACGGGAGAATCTCC" ...
> 

> SplitBam(bam = "./L07/possorted_genome_bam.bam",
+          cellbc.df = cells.df,
+          outdir = outdir, 
+          gtf_gr = gtf_gr,
+          geneSymbol = "ENG")
splitting bam file: ./L07/possorted_genome_bam.bam
[1] "cartilaginous" "stress"        "angiogenic"    "fibroblast"   
processing cell type cartilaginous
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
No data found for cartilaginous
processing cell type stress
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
No data found for stress
processing cell type angiogenic
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
No data found for angiogenic
processing cell type fibroblast
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
No data found for fibroblast

Additional scripts:

### Visualization
library(Seurat)
library(stringr)
library(dplyr)

#Previous definitions
out.dir <- "example_TIP_aggregate"

#Read in the counts
peak.counts <- ReadPeakCounts(data.dir = out.dir)

# Correct cell id
colnames(peak.counts) <- colnames(peak.counts) %>% str_split("-") %>% sapply("[[", 1)

#Read in peak annotations
peak.annotations <- read.table("TIP_merged_peak_annotations.txt", 
                               header = TRUE,
                               sep = "\t",
                               row.names = 1,
                               stringsAsFactors = FALSE)
head(peak.annotations)

#Load precompiled gene-level object called 'genes.seurat'
Low_L07 <- readRDS("../scRNAseq/Low_L07.rds")

peaks.seurat <- PeakSeuratFromTransfer(peak.data = peak.counts, 
                                       genes.seurat = Low_L07, 
                                       annot.info = peak.annotations, 
                                       min.cells = 0, min.peaks = 0)

# Testing for differential transcript usage
res.table = DUTest(peaks.seurat, 
                   population.1 = "stress", 
                   population.2 = "fibroblast",
                   exp.thresh = 0.1, 
                   feature.type = c("UTR3", "exon"))

res.table.top <- subset(res.table, abs(Log2_fold_change) > 1)
head(res.table.top)


# Plotting gene expression using the Seurat FeaturePlot function
Seurat::FeaturePlot(Low_L07, "ENG", cols = c("lightgrey", "red"))

# Select top peaks DU within the MMP3 gene
peaks.to.plot <- rownames(subset(res.table.top, gene_name == "ENG"))
PlotRelativeExpressionUMAP(peaks.seurat, peaks.to.plot = peaks.to.plot)


### Sierra coverage plots
cells.df <- Idents(Low_L07) %>% as.data.frame()
cells.df$cellbc <- rownames(cells.df)
colnames(cells.df) <- c("celltype", "cellbc")

gtf_gr <- rtracklayer::import("genes.gtf")
outdir = "bam_subsets/"
dir.create(outdir)
SplitBam(bam = "./L07/possorted_genome_bam.bam",
         cellbc.df = cells.df,
         outdir = outdir, 
         gtf_gr = gtf_gr,
         geneSymbol = "ENG")

[rj-patrick]

Hi Nelson,

If you're using CellRanger, it's possible you need a '-1' character appended to your cell barcodes.

Try the following code, and let me know if that helps:
cells.df$cellbc <- paste0(cells.df$cellbc, "-1")

Cheers,

• Ralph

[zezhuo]

Hi Ralph,

Thanks for your suggestion, but still "No data found". Any other solution? Thanks!

Regards,
Nelson

> cells.df$cellbc <- paste0(cells.df$cellbc, "-1")
> str(cells.df)
'data.frame':	1872 obs. of  2 variables:
 $ celltype: Factor w/ 4 levels "cartilaginous",..: 1 3 3 3 3 3 1 3 3 2 ...
 $ cellbc  : chr  "AAACCTGGTTAAGGGC-1" "AAACCTGTCGAGAGCA-1" "AAACGGGAGAACAATC-1" "AAACGGGAGAATCTCC-1" ...
> SplitBam(bam = "./L07/possorted_genome_bam.bam",
+          cellbc.df = cells.df,
+          outdir = outdir, 
+          gtf_gr = gtf_gr,
+          geneSymbol = "ENG")
splitting bam file: ./L07/possorted_genome_bam.bam
[1] cartilaginous stress        angiogenic    fibroblast   
Levels: cartilaginous angiogenic stress fibroblast
processing cell type cartilaginous
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
Writing to bam_subsets/cartilaginous.ENG.bam
processing cell type stress
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
Writing to bam_subsets/stress.ENG.bam
processing cell type angiogenic
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
Writing to bam_subsets/angiogenic.ENG.bam
processing cell type fibroblast
[W::hts_idx_load2] The index file is older than the data file: ./L07/possorted_genome_bam.bam.bai
chunk0: 8192 length of aln:  0 
Writing to bam_subsets/fibroblast.ENG.bam
> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_HK.UTF-8       LC_NUMERIC=C               LC_TIME=en_HK.UTF-8        LC_COLLATE=en_HK.UTF-8     LC_MONETARY=en_HK.UTF-8   
 [6] LC_MESSAGES=en_HK.UTF-8    LC_PAPER=en_HK.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_HK.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_0.8.3   stringr_1.4.0 Seurat_3.1.1  Sierra_0.1.0 

loaded via a namespace (and not attached):
  [1] reticulate_1.13                   R.utils_2.9.0                     tidyselect_0.2.5                  RSQLite_2.1.3                    
  [5] AnnotationDbi_1.48.0              htmlwidgets_1.5.1                 grid_3.6.1                        BiocParallel_1.20.0              
  [9] Rtsne_0.15                        munsell_0.5.0                     codetools_0.2-16                  ica_1.0-2                        
 [13] statmod_1.4.32                    future_1.15.1                     colorspace_1.4-1                  Biobase_2.46.0                   
 [17] knitr_1.26                        rstudioapi_0.10                   stats4_3.6.1                      SingleCellExperiment_1.8.0       
 [21] ROCR_1.0-7                        gbRd_0.4-11                       listenv_0.7.0                     labeling_0.3                     
 [25] Rdpack_0.11-0                     GenomeInfoDbData_1.2.2            hwriter_1.3.2                     bit64_0.9-7                      
 [29] farver_2.0.1                      vctrs_0.2.0                       xfun_0.11                         biovizBase_1.34.0                
 [33] BiocFileCache_1.10.2              R6_2.4.1                          GenomeInfoDb_1.22.0               rsvd_1.0.2                       
 [37] locfit_1.5-9.1                    AnnotationFilter_1.10.0           bitops_1.0-6                      DelayedArray_0.12.0              
 [41] assertthat_0.2.1                  SDMTools_1.1-221.2                scales_1.1.0                      nnet_7.3-12                      
 [45] gtable_0.3.0                      npsurv_0.4-0                      globals_0.12.4                    ensembldb_2.10.2                 
 [49] rlang_0.4.2                       zeallot_0.1.0                     genefilter_1.68.0                 splines_3.6.1                    
 [53] rtracklayer_1.46.0                lazyeval_0.2.2                    acepack_1.4.1                     dichromat_2.0-0                  
 [57] checkmate_1.9.4                   reshape2_1.4.3                    GenomicFeatures_1.38.0            backports_1.1.5                  
 [61] Hmisc_4.3-0                       tools_3.6.1                       ggplot2_3.2.1                     gplots_3.0.1.1                   
 [65] RColorBrewer_1.1-2                BiocGenerics_0.32.0               ggridges_0.5.1                    Rcpp_1.0.3                       
 [69] plyr_1.8.4                        base64enc_0.1-3                   progress_1.2.2                    zlibbioc_1.32.0                  
 [73] purrr_0.3.3                       RCurl_1.95-4.12                   prettyunits_1.0.2                 rpart_4.1-15                     
 [77] openssl_1.4.1                     pbapply_1.4-2                     cowplot_1.0.0                     S4Vectors_0.24.1                 
 [81] zoo_1.8-6                         SummarizedExperiment_1.16.0       ggrepel_0.8.1                     cluster_2.1.0                    
 [85] magrittr_1.5                      data.table_1.12.6                 lmtest_0.9-37                     RANN_2.6.1                       
 [89] ProtGenerics_1.18.0               fitdistrplus_1.0-14               matrixStats_0.55.0                hms_0.5.2                        
 [93] lsei_1.2-0                        xtable_1.8-4                      XML_3.98-1.20                     IRanges_2.20.1                   
 [97] gridExtra_2.3                     compiler_3.6.1                    biomaRt_2.42.0                    tibble_2.1.3                     
[101] KernSmooth_2.23-16                crayon_1.3.4                      R.oo_1.23.0                       htmltools_0.4.0                  
[105] Formula_1.2-3                     tidyr_1.0.0                       geneplotter_1.64.0                RcppParallel_4.4.4               
[109] DBI_1.0.0                         dbplyr_1.4.2                      MASS_7.3-51.4                     rappdirs_0.3.1                   
[113] Matrix_1.2-18                     R.methodsS3_1.7.1                 gdata_2.18.0                      parallel_3.6.1                   
[117] Gviz_1.30.0                       metap_1.1                         igraph_1.2.4.2                    GenomicRanges_1.38.0             
[121] pkgconfig_2.0.3                   GenomicAlignments_1.22.1          foreign_0.8-72                    plotly_4.9.1                     
[125] foreach_1.4.7                     annotate_1.64.0                   XVector_0.26.0                    bibtex_0.4.2                     
[129] DEXSeq_1.32.0                     VariantAnnotation_1.32.0          BSgenome.Hsapiens.UCSC.hg38_1.4.1 digest_0.6.23                    
[133] sctransform_0.2.0                 RcppAnnoy_0.0.14                  tsne_0.1-3                        Biostrings_2.54.0                
[137] leiden_0.3.1                      htmlTable_1.13.2                  uwot_0.1.4                        curl_4.3                         
[141] Rsamtools_2.2.1                   gtools_3.8.1                      lifecycle_0.1.0                   nlme_3.1-142                     
[145] jsonlite_1.6                      viridisLite_0.3.0                 askpass_1.1                       BSgenome_1.54.0                  
[149] pillar_1.4.2                      lattice_0.20-38                   httr_1.4.1                        survival_2.44-1.1                
[153] glue_1.3.1                        png_0.1-7                         iterators_1.0.12                  bit_1.1-14                       
[157] stringi_1.4.3                     blob_1.2.0                        DESeq2_1.26.0                     latticeExtra_0.6-28              
[161] caTools_1.17.1.3                  memoise_1.1.0                     irlba_2.3.3                       future.apply_1.3.0               
[165] ape_5.3                          

[rj-patrick]

Hi Nelson,

I don't see the 'no data found' message in your output after the cell barcodes were updated - the messages indicate the BAM files should have been written. Can you clarify if the BAM files have not been produced in your output directory?

Cheers,

• Ralph

[zezhuo]

Hi Ralph,

Sorry. I check the output dir and get the bam files. It did solve the problem.

Thanks you so much for your kindly help. I love the coverage plot.

Regards,
Nelson

bam.files <- paste0(outdir, dir(outdir, "ENG.bam$"))

PlotCoverage(genome_gr = gtf_gr, 
             geneSymbol = "ENG", 
             genome = "mm10",
             bamfiles = bam.files)