/var/spool/slurmd/job2672946/slurm_script: line 2: --output=/data/reddylab/Hazel/troubleshoot/processing/chip_seq/analysis/logs/diffbind.out: No such file or directory
Loading required package: GenomicRanges
Loading required package: methods
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

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    IQR, mad, xtabs

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    anyDuplicated, append, as.data.frame, cbind, colnames, do.call,
    duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit, which, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

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    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
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    Vignettes contain introductory material; view with
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CAL51_P300_DMSO.rep1 CAL51 P300 DMSO  1 narrow
CAL51_P300_DMSO.rep2 CAL51 P300 DMSO  2 narrow
CAL51_P300_DMSO.rep3 CAL51 P300 DMSO  3 narrow
CAL51_P300_JQ1.rep1 CAL51 P300 JQ1  1 narrow
CAL51_P300_JQ1.rep2 CAL51 P300 JQ1  2 narrow
CAL51_P300_JQ1.rep3 CAL51 P300 JQ1  3 narrow
CAL51_P300_THZ531.rep1 CAL51 P300 THZ531  1 narrow
CAL51_P300_THZ531.rep2 CAL51 P300 THZ531  2 narrow
CAL51_P300_THZ531.rep3 CAL51 P300 THZ531  3 narrow
converting counts to integer mode
gene-wise dispersion estimates
mean-dispersion relationship
Error in estimateDispersionsFit(object, fitType = fitType, quiet = quiet) : 
  all gene-wise dispersion estimates are within 2 orders of magnitude
  from the minimum value, and so the standard curve fitting techniques will not work.
  One can instead use the gene-wise estimates as final estimates:
  dds <- estimateDispersionsGeneEst(dds)
  dispersions(dds) <- mcols(dds)$dispGeneEst
  ...then continue with testing using nbinomWaldTest or nbinomLRT
Calls: dba.analyze ... <Anonymous> -> <Anonymous> -> .local -> estimateDispersionsFit
Execution halted