Hi,
I'm trying to use RIPseeker to to analyze some data (not RIP-seq, but expect similar one-stranded peaks). I can load my data in, generate plots in RIPSeeker (with plotStrandedCoverage), and run the ripSeek command without a control file. However, when I try to run ripSeek with a control file included (input control), I get the following error: "Error in sprintf("***0. Using predefined binSize of %d bp.", binSize) : unsupported type". The error happens regardless of whether I provide a binSize or calculate one within the ripSeek command and is independent of the file assigned as control (can assign a file that works as a RIP file as the control and get the same error). Please help!
Thanks,
Devon
R code:
bamFiles <- c("test1.bam", "test2.bam", "input.bam")
cNAME <- "input"
outDir <- file.path(getwd(), "RIPSeeker_test")
binSize <- NULL # set to NULL to automatically determine bin size
minBinSize <- 50 # min bin size in automatic bin size selection
maxBinSize <- 500 # max bin size in automatic bin size selection
multicore <- TRUE # use multicore
strandType <- "+" # set strand type to minus strand
seekOut.TEST.plus <- ripSeek(bamPath = bamFiles, cNAME = cNAME,
reverseComplement = TRUE,
strandType = strandType, paired = TRUE,
uniqueHit = TRUE, assignMultihits = TRUE,
rerunWithDisambiguatedMultihits = TRUE,
binSize=binSize)
Full output:
*I. Collect alignment files
RIP alignment files:
test1.bam
test2.bam
Control alignment files:
input.bam
*II. Analyzing RIP library:
**A. Process and combine alignment files
Processing test1.bam ... GAlignments object contains 114 out-of-bound ranges located on sequence chr1. Note that ranges located on a sequence whose length is unknown (NA) or on a circular sequence are not
considered out-of-bound (use seqlengths() and isCircular() to get the lengths and circularity flags of the underlying sequences).115 read-pairs with end larger than chromosome length are discardedAll hits are returned with flags.
Processing test2.bam ... GAlignments object contains 40 out-of-bound ranges located on sequence chr1. Note that ranges located on a sequence whose length is unknown (NA) or on a circular sequence are not
considered out-of-bound (use seqlengths() and isCircular() to get the lengths and circularity flags of the underlying sequences).41 read-pairs with end larger than chromosome length are discardedAll hits are returned with flags.
2 BAM files are combined
* Only reads from strand + will be considered.
* Only unique hits are used to compute read count.
* All chromosomes have at least one alignment
**B. Run NB HMM on each chromosome
chr1:
*0. Computing optimal bin size.
Optimal bin size: 200 bp
*1. Traning NB HMM to derive posterior (and Viterbi state sequence:)
*1. Initializing negative binomial HMM (nbh) with 2 states:
Starting NB mixture model (nbm_em) for K=2 clusters:
Iteration 0: -201609.663
Iteration 1: -176794.357
Iteration 2: -171303.981
Iteration 3: -165668.298
Iteration 4: -158963.471
Iteration 5: -153561.867
Iteration 6: -148361.207
Iteration 7: -143409.767
Iteration 8: -138773.494
Iteration 9: -134508.689
Iteration 10: -130658.151
Iteration 11: -127248.477
Iteration 12: -124298.263
Iteration 13: -121838.340
Iteration 14: -119587.219
Iteration 15: -117634.789
Iteration 16: -116229.736
Iteration 17: -115124.218
*2. Traininig nbh with forward-backward algorithm:
Iteration 0: -114272.347
Iteration 1: -113628.000
Iteration 2: -113150.942
Iteration 3: -112805.237
Iteration 4: -112560.435
Iteration 5: -112391.531
Iteration 6: -112278.560
Iteration 7: -112205.970
*3. Deriving maximum-likelihood hidden state sequence with Viterbi algorithm:
done!
*II.2. Analyzing control library:
**A. Process and combine alignment files
Processing input.bam ... GAlignments object contains 28 out-of-bound ranges located on sequence chr1. Note that ranges located on a sequence whose length is unknown (NA) or on a circular sequence are not
considered out-of-bound (use seqlengths() and isCircular() to get the lengths and circularity flags of the underlying sequences).29 read-pairs with end larger than chromosome length are discardedAll hits are returned with flags.
1 BAM files are combined
* Only reads from strand + will be considered.
* Only unique hits are used to compute read count.
* All chromosomes have at least one alignment
**B. Run NB HMM on each chromosome
chr1:
Error in sprintf("*0. Using predefined binSize of %d bp.", binSize) :
unsupported type
Session info:
> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] grid parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] ChIPpeakAnno_3.14.0 VennDiagram_1.6.20 futile.logger_1.4.3 RIPSeekerData_1.16.0 RIPSeeker_1.20.0 rtracklayer_1.40.2 biomaRt_2.36.0
[8] BiocInstaller_1.30.0 ggbio_1.28.0 GenomicAlignments_1.16.0 SummarizedExperiment_1.10.1 DelayedArray_0.6.0 BiocParallel_1.14.1 matrixStats_0.53.1
[15] Biobase_2.40.0 Rsamtools_1.32.0 Biostrings_2.48.0 XVector_0.20.0 ggplot2_2.2.1 hiAnnotator_1.14.0 Gviz_1.24.0
[22] GenomicRanges_1.32.3 GenomeInfoDb_1.16.0 IRanges_2.14.10 S4Vectors_0.18.2 BiocGenerics_0.26.0
loaded via a namespace (and not attached):
[1] nlme_3.1-137 ProtGenerics_1.12.0 bitops_1.0-6 bit64_0.9-7 RColorBrewer_1.1-2 progress_1.1.2 httr_1.3.1 tools_3.5.0
[9] backports_1.1.2 R6_2.2.2 rpart_4.1-13 mgcv_1.8-23 Hmisc_4.1-1 DBI_1.0.0 lazyeval_0.2.1 colorspace_1.3-2
[17] ade4_1.7-11 nnet_7.3-12 gridExtra_2.3 prettyunits_1.0.2 GGally_1.3.2 bit_1.1-13 curl_3.2 compiler_3.5.0
[25] graph_1.58.0 formatR_1.5 htmlTable_1.11.2 labeling_0.3 scales_0.5.0 checkmate_1.8.5 RBGL_1.56.0 stringr_1.3.1
[33] digest_0.6.15 foreign_0.8-70 pkgconfig_2.0.1 base64enc_0.1-3 dichromat_2.0-0 htmltools_0.3.6 regioneR_1.12.0 limma_3.36.1
[41] ensembldb_2.4.1 BSgenome_1.48.0 htmlwidgets_1.2 rlang_0.2.0 rstudioapi_0.7 RSQLite_2.1.1 bindr_0.1.1 acepack_1.4.1
[49] dplyr_0.7.4 VariantAnnotation_1.26.0 RCurl_1.95-4.10 magrittr_1.5 GO.db_3.6.0 GenomeInfoDbData_1.1.0 Formula_1.2-3 Matrix_1.2-14
[57] Rcpp_0.12.16 munsell_0.4.3 stringi_1.2.2 yaml_2.1.19 MASS_7.3-50 zlibbioc_1.26.0 plyr_1.8.4 blob_1.1.1
[65] lattice_0.20-35 splines_3.5.0 multtest_2.36.0 GenomicFeatures_1.32.0 knitr_1.20 pillar_1.2.2 seqinr_3.4-5 reshape2_1.4.3
[73] codetools_0.2-15 futile.options_1.0.1 glue_1.2.0 XML_3.98-1.11 biovizBase_1.28.0 latticeExtra_0.6-28 lambda.r_1.2.2 data.table_1.11.2
[81] idr_1.2 foreach_1.4.4 gtable_0.2.0 reshape_0.8.7 assertthat_0.2.0 AnnotationFilter_1.4.0 survival_2.42-3 tibble_1.4.2
[89] OrganismDbi_1.22.0 iterators_1.0.9 AnnotationDbi_1.42.1 memoise_1.1.0 bindrcpp_0.2.2 cluster_2.0.7-1
