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Hello I hope you are doing well; I am running into this issue when I am trying to run dba.count().
This is the code I am using:
#Create a new DBA object using the subset data
myDBA <- dba(sampleSheet = subset_data)
myDBA <- dba.count(myDBA, bUseSummarizeOverlaps=FALSE)
print(myDBA)
It does the following: Computing summits... Re-centering peaks... Reads will be counted as Paired-end. Error: No sites have activity greater than filter value.
However I also tried to do the following:
myDBA <- dba.count(myDBA, bUseSummarizeOverlaps=FALSE)
print(myDBA)
But I get the same error
Re-centering peaks... Error: No sites have activity greater than filter value.
Any ideas on what this error is indicating or how I can fix it?
Code should be placed in three backticks as shown below
sessionInfo( )
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /hpc/packages/minerva-centos7/intel/parallel_studio_xe_2019/compilers_and_libraries_2019.0.117/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.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] reshape2_1.4.4 ggpubr_0.4.0
[3] ggplot2_3.4.1 DESeq2_1.36.0
[5] DiffBind_3.6.3 SummarizedExperiment_1.26.1
[7] Biobase_2.56.0 MatrixGenerics_1.8.1
[9] matrixStats_0.62.0 GenomicRanges_1.48.0
[11] GenomeInfoDb_1.34.9 IRanges_2.30.0
[13] S4Vectors_0.34.0 BiocGenerics_0.42.0
loaded via a namespace (and not attached):
[1] amap_0.8-18 colorspace_2.0-3 ggsignif_0.6.3
[4] rjson_0.2.21 hwriter_1.3.2.1 XVector_0.36.0
[7] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.58.0
[10] fansi_1.0.3 mvtnorm_1.1-3 apeglm_1.18.0
[13] codetools_0.2-18 splines_4.2.0 cachem_1.0.6
[16] geneplotter_1.74.0 Rsamtools_2.12.0 broom_1.0.5
[19] annotate_1.74.0 ashr_2.2-54 png_0.1-7
[22] GreyListChIP_1.28.1 compiler_4.2.0 httr_1.4.3
[25] backports_1.4.1 Matrix_1.5-1 fastmap_1.1.0
[28] limma_3.52.4 cli_3.6.1 htmltools_0.5.5
[31] tools_4.2.0 coda_0.19-4 gtable_0.3.0
[34] glue_1.6.2 GenomeInfoDbData_1.2.8 systemPipeR_2.2.2
[37] dplyr_1.1.2 ShortRead_1.54.0 Rcpp_1.0.10
[40] carData_3.0-5 bbmle_1.0.25 vctrs_0.6.3
[43] Biostrings_2.64.0 rtracklayer_1.56.1 stringr_1.4.0
[46] lifecycle_1.0.3 irlba_2.3.5 restfulr_0.0.15
[49] gtools_3.9.2.2 rstatix_0.7.0 XML_3.99-0.10
[52] zlibbioc_1.42.0 MASS_7.3-56 scales_1.2.1
[55] BSgenome_1.64.0 parallel_4.2.0 RColorBrewer_1.1-3
[58] yaml_2.3.5 memoise_2.0.1 emdbook_1.3.12
[61] bdsmatrix_1.3-6 latticeExtra_0.6-29 stringi_1.7.6
[64] RSQLite_2.2.14 SQUAREM_2021.1 genefilter_1.78.0
[67] BiocIO_1.6.0 caTools_1.18.2 BiocParallel_1.30.3
[70] truncnorm_1.0-8 rlang_1.1.1 pkgconfig_2.0.3
[73] bitops_1.0-7 lattice_0.20-45 invgamma_1.1
[76] purrr_1.0.1 GenomicAlignments_1.32.0 htmlwidgets_1.6.2
[79] bit_4.0.4 tidyselect_1.2.0 plyr_1.8.7
[82] magrittr_2.0.3 R6_2.5.1 gplots_3.1.3
[85] generics_0.1.2 DelayedArray_0.22.0 DBI_1.1.3
[88] pillar_1.9.0 withr_2.5.0 abind_1.4-5
[91] survival_3.3-1 KEGGREST_1.36.2 RCurl_1.98-1.7
[94] mixsqp_0.3-43 tibble_3.2.1 crayon_1.5.1
[97] car_3.1-0 KernSmooth_2.23-20 utf8_1.2.3
[100] jpeg_0.1-9 locfit_1.5-9.5 grid_4.2.0
[103] blob_1.2.3 digest_0.6.29 xtable_1.8-4
[106] tidyr_1.2.0 numDeriv_2016.8-1.1 munsell_0.5.0
