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kharchenkolab / numbat

Haplotype-aware CNV analysis from single-cell RNA-seq
https://kharchenkolab.github.io/numbat/
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`snp_index` must be size 0 or 1, not 2 [pileup_and_phase.R] #47

Open jgarces02 opened 2 years ago

jgarces02 commented 2 years ago

Hi, I'm running the first step (pileup_and_phase.R)...

Rscript pileup_and_phase.R \
    --label x21922_mm8 \
    --samples x21922_mm8 \
    --bams /home/igoroz/jgarces/scCSCMM_sara/data/21922_mm8/outs/per_sample_outs/21922/count/sample_alignments.bam \
    --barcodes /home/igoroz/jgarces/scCSCMM_sara/data/21922_mm8/outs/per_sample_outs/21922/count/sample_filtered_barcodes.csv \
    --outdir x21922_mm8 \
    --gmap Eagle_v2.4.1/tables/genetic_map_hg38_withX.txt.gz \
    --snpvcf genome1K.phase3.SNP_AF5e2.chr1toX.hg38.vcf \
    --paneldir 1000G_hg38 \
    --ncores 10

... and having the following error:

Using genome version: hg38
Running pileup
[I::main] start time: 2022-10-11 16:13:36
[W::check_args] Max depth set to maximum value (2147483647)
[W::check_args] Max pileup set to maximum value (2147483647)
[I::main] global settings after checking:
        num of input files = 1
        out_dir = x21922_mm8/pileup/x21922_mm8
        is_out_zip = 0, is_genotype = 0
        is_target = 0, num_of_pos = 0
        num_of_barcodes = 8122, num_of_samples = 0
        refseq file = (null)
        0 chroms:
        cell-tag = CB, umi-tag = UB
        nthreads = 10, tp_max_open = 51200
        mthreads = 10, tp_errno = 0, tp_ntry = 0
        min_count = 2, min_maf = 0.00, double_gl = 0
        min_len = 30, min_mapq = 20
        rflag_filter = 772, rflag_require = 0
        max_depth = 2147483647, max_pileup = 2147483647, no_orphan = 1
[I::main] loading the VCF file for given SNPs ...
[I::main] fetching 7352497 candidate variants ...
[I::main] mode 1a: fetch given SNPs in 8122 single cells.
[I::csp_fetch_core][Thread-2] 2.00% SNPs processed.
[I::csp_fetch_core][Thread-3] 2.00% SNPs processed.
[I::csp_fetch_core][Thread-5] 2.00% SNPs processed.
[I::csp_fetch_core][Thread-4] 2.00% SNPs processed.
(...)
[I::csp_fetch_core][Thread-7] 92.00% SNPs processed.
[I::csp_fetch_core][Thread-7] 94.00% SNPs processed.
[I::csp_fetch_core][Thread-7] 96.00% SNPs processed.
[I::csp_fetch_core][Thread-7] 98.00% SNPs processed.
[I::main] All Done!
[I::main] Version: 1.2.2 (htslib 1.16)
[I::main] CMD: cellsnp-lite -s /home/igoroz/jgarces/scCSCMM_sara/data/21922_mm8/outs/per_sample_outs/21922/count/sample_alignments.bam -b /home/igoroz/jgarces/scCSCMM_sara/data/21922_mm8/outs/per_sample_outs/21922/count/sample_filtered_barcodes.csv -O x21922_mm8/pileup/x21922_mm8 -R genome1K.phase3.SNP_AF5e2.chr1toX.hg38.vcf -p 10 --minMAF 0 --minCOUNT 2 --UMItag Auto --cellTAG CB
[I::main] end time: 2022-10-11 17:30:50
[I::main] time spent: 4634 seconds.
character(0)
Creating VCFs
Error in `mutate()`:
! Problem while computing `snp_index = 1:n()`.
✖ `snp_index` must be size 0 or 1, not 2.
Backtrace:
     ▆
  1. ├─numbat:::genotype(label, samples, vcfs, glue("{outdir}/phasing"))
  2. │ ├─... %>% arrange(CHROM, POS)
  3. │ └─base::lapply(...)
  4. │   └─numbat (local) FUN(X[[i]], ...)
  5. │     └─numbat:::get_snps(vcf)
  6. │       └─... %>% filter(!is.na(AR))
  7. ├─dplyr::arrange(., CHROM, POS)
  8. ├─dplyr::mutate(., AR = AD/DP)
  9. ├─dplyr::summarise(...)
 10. ├─dplyr::group_by(., CHROM, POS, REF, ALT, snp_id)
 11. ├─dplyr::bind_rows(.)
 12. │ └─rlang::list2(...)
 13. ├─dplyr::filter(., !is.na(AR))
 14. ├─dplyr::mutate(., AR = AD/DP)
 15. ├─dplyr::mutate(., snp_index = 1:n())
 16. ├─dplyr:::mutate.data.frame(., snp_index = 1:n())
 17. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), caller_env = caller_env())
 18. │   ├─base::withCallingHandlers(...)
 19. │   └─mask$eval_all_mutate(quo)
 20. ├─dplyr:::dplyr_internal_error(...)
 21. │ └─rlang::abort(class = c(class, "dplyr:::internal_error"), dplyr_error_data = data)
 22. │   └─rlang:::signal_abort(cnd, .file)
 23. │     └─base::signalCondition(cnd)
 24. └─dplyr (local) `<fn>`(`<dpl:::__>`)
 25.   └─rlang::abort(...)
Execution halted

What could I been missing, please?

(I'm running the docker container from singularity)

teng-gao commented 2 years ago

Hi, did you check if the pileup counts and phased vcfs are not empty?

jgarces02 commented 2 years ago

In the pashed folder there's nothing, the script is not generating any file.

x21922_mm8/
├── [   0]  phasing
├── [206K]  pileup
│   └── [206K]  x21922_mm8
│       ├── [  60]  cellSNP.base.vcf
│       ├── [206K]  cellSNP.samples.tsv
│       ├── [  60]  cellSNP.tag.AD.mtx
│       ├── [  60]  cellSNP.tag.DP.mtx
│       └── [  60]  cellSNP.tag.OTH.mtx
└── [ 369]  run_pileup.sh
teng-gao commented 2 years ago

Are you sure this is a human hg38 bam? Most times this happens when the bam genome version does not match the SNP site VCF.

jgarces02 commented 2 years ago

Totally sure. Data was generated with cellranger pipeline using hg38 reference, so I guess the BAM is correctly built.

teng-gao commented 2 years ago

What is the format of your barcodes? Should be 1 barcode per line. Do they match the barcodes in your bam? I would test with barcode list output by 10x under filtered_feature_bc_matrix/barcodes.tsv.gz.

Since this is clearly an issue with the pileup, you could test with the command running cellsnp-lite instead of the whole script, and make sure the pileup result isn't empty before proceeding.

laijen000 commented 1 year ago

Hi @teng-gao ,

I am getting the same error while running run_numbat() on a single sample from a single individual. run_numbat() worked fine on 4 samples so far. pileup_and_phase ran without errors on this sample, and the pileup and phasing vcfs are not empty. This error occurs just after the "Building phylogeny" step. Do you have suggestions on things to check?

Thank you!

teng-gao commented 1 year ago

Hi @laijen000 ,

I'm not sure which error you're referring to - did you mean to raise this issue on another related thread?

Best, Teng

laijen000 commented 1 year ago

Hi Teng,

Sorry, I mean the snp_index issue reported in this thread/subject line! The error message is the same as reported here.

On Mon, Jan 9, 2023 at 8:10 AM Teng Gao @.***> wrote:

Hi @laijen000 https://github.com/laijen000 ,

I'm not sure which error you're referring to - do you mean to raise this issue on another related thread?

Best, Teng

— Reply to this email directly, view it on GitHub https://github.com/kharchenkolab/numbat/issues/47#issuecomment-1375602500, or unsubscribe https://github.com/notifications/unsubscribe-auth/AOMJVAL3WDVPCWOTDTRJUVTWRQE2VANCNFSM6AAAAAARCNK3JI . You are receiving this because you were mentioned.Message ID: @.***>

teng-gao commented 1 year ago

Hi Teng, Sorry, I mean the snp_index issue reported in this thread/subject line! The error message is the same as reported here.

In run_numbat()? Please raise a separate issue and paste the full error message/logs. Thanks.

stela2502 commented 1 year ago

Just a Question - has this error been fixed? This is still open and we also run into this error. More help would be greatly appreciated.

stela2502 commented 1 year ago

Here is our current error message:

pileup_and_phase.R --label sample10 --samples sample10 --bams /projects/fs1/common/Pavan/nextseq5/S10/outs/possorted_genome_bam.bam  --barcodes cell_ids_Sample_10.txt --outdir numbat_sample10 --ncores 10

During startup - Warning messages:

1: Setting LC_COLLATE failed, using "C"

2: Setting LC_TIME failed, using "C"

3: Setting LC_MESSAGES failed, using "C"

4: Setting LC_MONETARY failed, using "C"

5: Setting LC_PAPER failed, using "C"

6: Setting LC_MEASUREMENT failed, using "C"

Using genome version: hg38

Running pileup

[I::main] start time: 2023-10-06 15:46:10

[W::check_args] Max depth set to maximum value (2147483647)

[W::check_args] Max pileup set to maximum value (2147483647)

[I::main] global settings after checking:

               num of input files = 1

               out_dir = numbat_sample10/pileup/sample10

               is_out_zip = 0, is_genotype = 0

               is_target = 0, num_of_pos = 0

               num_of_barcodes = 1, num_of_samples = 0

               refseq file = (null)

               0 chroms:

               cell-tag = CB, umi-tag = UB

               nthreads = 10, tp_max_open = 4096

               mthreads = 10, tp_errno = 0, tp_ntry = 0

               min_count = 2, min_maf = 0.00, double_gl = 0

               min_len = 30, min_mapq = 20

               rflag_filter = 772, rflag_require = 0

               max_depth = 2147483647, max_pileup = 2147483647, no_orphan = 1

[I::main] loading the VCF file for given SNPs ...

[I::main] fetching 7352497 candidate variants ...

[I::main] mode 1a: fetch given SNPs in 1 single cells.

[I::csp_fetch_core][Thread-5] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 2.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 4.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 6.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 8.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 22.00% SNPs processed.
0% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.
[I::csp_fetch_core][Thread-6] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 10.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 12.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 14.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 16.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 18.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 20.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 22.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 24.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 26.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 28.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 30.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-2] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 80.00% SNPs processed.

[I::csp_fetch_core][T0% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.hread-6] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 58.00% SNPs processed.

[I::csp_fetch_core][T0% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.hread-0] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 70.00% SNPs processed.
0% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.
[I::csp_fetch_core][Thread-6] 60.00% SNPs processed.
0% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.
[I::csp_fetch_core][Thread-1] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 32.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-1] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 34.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 36.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 38.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-5] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-7] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 40.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 42.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 44.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-4] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 46.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 48.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 50.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-6] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 52.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 54.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 56.00% SNPs processed.

[I::csp_fetch_core][Thread-0] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 58.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 60.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-3] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 62.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 64.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 66.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 68.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 70.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 72.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 74.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 76.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 78.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 80.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 82.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 84.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 86.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 88.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 90.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 92.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 94.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-9] 98.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 96.00% SNPs processed.

[I::csp_fetch_core][Thread-8] 98.00% SNPs processed.

[I::main] All Done!

[I::main] Version: 1.2.3 (htslib 1.13+ds)

[I::main] CMD: cellsnp-lite -s /projects/fs1/common/Pavan/nextseq5/S10/outs/possorted_genome_bam.bam -b cell_ids_Sample_10.txt -O numbat_sample10/pileup/sample10 -R /data/genome1K.phase3.SNP_AF5e2.chr1toX.hg38.vcf -p 10 --minMAF 0 --minCOUNT 2 --UMItag Auto --cellTAG CB

[I::main] end time: 2023-10-06 17:19:18

[I::main] time spent: 5588 seconds.

character(0)

Creating VCFs

Error in `mutate()`:

! Problem while computing `snp_index = 1:n()`.

\u2716 `snp_index` must be size 0 or 1, not 2.

Backtrace:

     \u2586

  1. \u251c\u2500numbat:::genotype(...)

  2. \u2502 \u251c\u2500... %>% arrange(CHROM, POS)

  3. \u2502 \u2514\u2500base::lapply(...)

  4. \u2502   \u2514\u2500numbat (local) FUN(X[[i]], ...)

  5. \u2502     \u2514\u2500numbat:::get_snps(vcf)

  6. \u2502       \u2514\u2500... %>% filter(!is.na(AR))

  7. \u251c\u2500dplyr::arrange(., CHROM, POS)

  8. \u251c\u2500dplyr::mutate(., AR = AD/DP)

  9. \u251c\u2500dplyr::summarise(...)

10. \u251c\u2500dplyr::group_by(., CHROM, POS, REF, ALT, snp_id)

11. \u251c\u2500dplyr::bind_rows(.)

12. \u2502 \u2514\u2500rlang::list2(...)

13. \u251c\u2500dplyr::filter(., !is.na(AR))

14. \u251c\u2500dplyr::mutate(., AR = AD/DP)

15. \u251c\u2500dplyr::mutate(., snp_index = 1:n())

16. \u251c\u2500dplyr:::mutate.data.frame(., snp_index = 1:n())

17. \u2502 \u2514\u2500dplyr:::mutate_cols(.data, dplyr_quosures(...), caller_env = caller_env())

18. \u2502   \u251c\u2500base::withCallingHandlers(...)

19. \u2502   \u2514\u2500mask$eval_all_mutate(quo)

20. \u251c\u2500dplyr:::dplyr_internal_error(...)

21. \u2502 \u2514\u2500rlang::abort(class = c(class, "dplyr:::internal_error"), dplyr_error_data = data)

22. \u2502   \u2514\u2500rlang:::signal_abort(cnd, .file)

23. \u2502     \u2514\u2500base::signalCondition(cnd)

24. \u2514\u2500dplyr (local) `<fn>`(`<dpl:::__>`)

25.   \u2514\u2500rlang::abort(...)

Execution halted
stela2502 commented 1 year ago

Oh and that is how we got ran the analysis:

cellranger count --id=sample_2  --transcriptome=/projects/fs1/common/genome/lunarc/10Xindexes/cellranger/3.0/human/refdata-cellranger-GRCh38-3.0.0/ --fastqs=./HGFMNBGXB --sample sample_2 --expect-cells 19000
teng-gao commented 1 year ago

Hi @stela2502,

I'm still not convinced that this is a bug rather than problem with input.. the error message says:

[I::main] mode 1a: fetch given SNPs in 1 single cells.

This cannot be right with 10x data. What's the format of your --barcodes cell_ids_Sample_10.txt? It should be a list of single cell barcodes, each on its separate line.

stela2502 commented 1 year ago

Thank you for the fast reply. I came to know that this sample is not a cancer sample. And the problems cause is likely also that the previous step did not identify any SNPS. So it might be of help if you could actually capture this error and replace it by something more meaningful like "Sorry, but there are no SNPS I could analyze in your set -> exiting". This would actually get you rid of this 'issue'. So please be kind to us inexperienced ones and produce a better error message than this!

stela2502 commented 1 year ago

About the file format - I'll check, but would expect it to be correct.

teng-gao commented 1 year ago

Hmm, please let me know what specifically you see in your output folder. Does the pileup folder indeed contain empty files? A normal sample is totally ok and should help with germline SNP genotyping.

stela2502 commented 1 year ago

Really strange. My co-worker has still not checked the cell ids. Which exact version of the cell-ids do you need? the ones with /-1$/ or the ones without?

stela2502 commented 1 year ago

Hi teng-gao. Thank you for asking again and - yes the file format was of cause wrong. Would be cool to have a more informative error message - especially without all the python error before. What is the point of running the whole first step if you only get one line of data and that line of data even contains some clearly not cell_id specific items like ,; or \t. That are two checks that could easily be included into the first program - or? So why spend a lot of time on processing a bam file when the likelihood of a result == 0.

teng-gao commented 1 year ago

Ok, leaving a note here for better error handling and input format checking

coldhart88 commented 1 year ago

I am the colleague of Stela2502 thank you for the advice it ran as expected. But we ran into a wall following this tutorial: https://kharchenkolab.github.io/numbat/articles/results.html The problem occurred while reading in the data from the numbat run - the first step!

The error message was:

Could not read the input file ./numbat_sample10/joint_post_2.tsv with data.table::fread(). Please check that the file is valid.

Could not read the input file ./numbat_sample10/exp_post_2.tsv with data.table::fread(). Please check that the file is valid.

Could not read the input file ./numbat_sample10/allele_post_2.tsv with data.table::fread(). Please check that the file is valid.

Could not read the input file ./numbat_sample10/bulk_clones_final.tsv.gz with data.table::fread(). Please check that the file is valid.

Could not read the input file ./numbat_sample10/segs_consensus_2.tsv with data.table::fread(). Please check that the file is valid.

Could not read the input file ./numbat_sample10/geno_2.tsv with data.table::fread(). Please check that the file is valid.

Error in (function (cond) : error in evaluating the argument 'x' in selecting a method for function 'as.matrix': is.data.frame(.data) is not TRUE
Traceback:

1. Numbat$new(out_dir = "./numbat_sample10")
2. initialize(...)
3. private$fetch_results(out_dir, i = i)
4. read_file(inputfile = glue("{out_dir}/geno_{i}.tsv"), header = TRUE) %>% 
 .     tibble::column_to_rownames("cell") %>% as.matrix
5. as.matrix(.)
6. tibble::column_to_rownames(., "cell")
7. stopifnot(is.data.frame(.data))
8. stop(simpleError(msg, call = if (p <- sys.parent(1L)) sys.call(p)))
9. (function (cond) 
 . .Internal(C_tryCatchHelper(addr, 1L, cond)))(structure(list(message = "is.data.frame(.data) is not TRUE", 
 .     call = tibble::column_to_rownames(., "cell")), class = c("simpleError", 
 . "error", "condition")))

But these files were not produced by the numbar run. What did we do wrong? The output from a find <outfolder> is linked to this post. numbat_sample10.txt

Thank you for your help!

teng-gao commented 1 year ago

@coldhart88 I think you only ran the preprocessing step and not Numbat workflow itself. Also please open separate issues for different errors. Thanks https://kharchenkolab.github.io/numbat/articles/numbat.html#running-numbat