I saw a similar post using ChIPQC package for ATAC-Seq peaks quality check but not answer https://support.bioconductor.org/p/117577/#117670

As I have a different setting than the above post, so I want to detail my problems and ask for help.

I want to use ChIPQC package to check the quality of H3K4me3 and H3K27ac ChIP-seq quality.

library(tidyverse)
library(ChIPQC)

chipqc <- ChIPQC(experiment, 
                 annotation = "mm10", 
                 blacklist = "~/iris/genome/mouseGenomemm10/mm10.blacklist.bed",
                 chromosomes = "chr11")

ChIPQCreport(chipqc)

When I ran the function ChIPQC, I got the following error:

"Error in names(res) <- c("Reads", "Map%", "Filt%", "Dup%", "ReadL", "FragL", : 'names' attribute [9] must be the same length as the vector [7]"

I continued to run the ChIPQCreport, the same error popped up, though some quality plots did manage to generate.

The CoverageHistogramPlot showed all the samples have certain coverage. However, the PeakProfile plot, two of my samples show no signal at all, which is rather strange because the igv shows enrichment.

(in case you can't see the image, the link is next to it.)

https://ibb.co/6XH67Rn

https://ibb.co/6XH67Rn

https://ibb.co/4Jv9G1X

Does anybody have some thoughts? Thanks in advance.

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
 [1] ChIPQC_1.21.0                            DiffBind_2.13.0                         
 [3] forcats_0.4.0                            stringr_1.4.0                           
 [5] dplyr_0.8.1                              purrr_0.3.2                             
 [7] readr_1.3.1                              tidyr_0.8.3                             
 [9] tibble_2.1.3                             ggplot2_3.1.1                           
[11] tidyverse_1.2.1                          chipseq_1.35.0                          
[13] ShortRead_1.43.0                         GenomicAlignments_1.21.2                
[15] SummarizedExperiment_1.15.2              DelayedArray_0.11.0                     
[17] BiocParallel_1.19.0                      matrixStats_0.54.0                      
[19] TxDb.Mmusculus.UCSC.mm10.knownGene_3.4.7 GenomicFeatures_1.37.1                  
[21] AnnotationDbi_1.47.0                     Biobase_2.45.0                          
[23] GenomeInfoDbData_1.2.1                   Rsamtools_2.1.2                         
[25] Biostrings_2.53.0                        XVector_0.25.0                          
[27] GenomicRanges_1.37.11                    GenomeInfoDb_1.21.1                     
[29] IRanges_2.19.10                          S4Vectors_0.23.12                       
[31] BiocGenerics_0.31.4