perishky
commented
5 years ago If you look closely, you'll notice that the negative values are all extremely close to 0, e.g. the value -8.517630e-18 = -8.5 x 10^-18. They won't show up as negative in the histograms because the histogram doesn't provide that level of precision (the range size of the bars is 0.05). For analysis, these negative values will be no different from 0's.
jnguofa
Hello, Thanks for the great package. I only have pre-processed betas (from cord - see distribution below) so I must use meffil.estimate.cell.counts.from.betas. There are a lot of negative values, especially for NK. Of note, I did remove SNP-related probes, XY chromosome probes and low variability (<5%) probes and batch corrected for plate.
cellcountref = "andrews and bakulski cord blood"
There are fewer negative values with the second reference but the distribution of estimated values between the two are very similar (see below)
Thanks for any thoughts, J
Below is my session info:
Matrix products: default
locale: [1] LC_COLLATE=English_Canada.1252 LC_CTYPE=English_Canada.1252 LC_MONETARY=English_Canada.1252 LC_NUMERIC=C LC_TIME=English_Canada.1252
attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages: [1] FlowSorted.CordBloodNorway.450k_1.4.0 minfi_1.24.0 bumphunter_1.20.0 locfit_1.5-9.1
[5] iterators_1.0.10 foreach_1.4.4 Biostrings_2.46.0 XVector_0.18.0
[9] SummarizedExperiment_1.8.1 DelayedArray_0.4.1 Biobase_2.38.0 GenomicRanges_1.30.3
[13] GenomeInfoDb_1.14.0 IRanges_2.12.0 S4Vectors_0.16.0 BiocGenerics_0.24.0
[17] meffil_1.0.0 statmod_1.4.30 quadprog_1.5-5 DNAcopy_1.52.0
[21] fastICA_1.2-1 lme4_1.1-19 Matrix_1.2-15 multcomp_1.4-8
[25] TH.data_1.0-9 survival_2.43-3 mvtnorm_1.0-8 matrixStats_0.54.0
[29] markdown_0.9 gridExtra_2.3 Cairo_1.5-9 knitr_1.21
[33] reshape2_1.4.3 plyr_1.8.4 ggplot2_3.1.0 sva_3.26.0
[37] BiocParallel_1.12.0 genefilter_1.60.0 mgcv_1.8-26 nlme_3.1-137
[41] limma_3.34.9 MASS_7.3-51.1 illuminaio_0.20.0
loaded via a namespace (and not attached): [1] minqa_1.2.4 colorspace_1.4-0 siggenes_1.52.0 mclust_5.4.2 base64_2.0 rstudioapi_0.9.0 bit64_0.9-7
[8] AnnotationDbi_1.40.0 xml2_1.2.0 codetools_0.2-16 splines_3.4.3 nloptr_1.2.1 Rsamtools_1.30.0 annotate_1.56.2
[15] readr_1.3.1 compiler_3.4.3 httr_1.4.0 assertthat_0.2.0 lazyeval_0.2.1 prettyunits_1.0.2 tools_3.4.3
[22] bindrcpp_0.2.2 gtable_0.2.0 glue_1.3.0 GenomeInfoDbData_1.0.0 dplyr_0.7.8 doRNG_1.7.1 Rcpp_1.0.0
[29] multtest_2.34.0 preprocessCore_1.40.0 rtracklayer_1.38.3 xfun_0.4 stringr_1.3.1 rngtools_1.3.1 XML_3.98-1.16
[36] beanplot_1.2 zlibbioc_1.24.0 zoo_1.8-4 scales_1.0.0 hms_0.4.2 sandwich_2.5-0 GEOquery_2.46.15
[43] RColorBrewer_1.1-2 yaml_2.2.0 memoise_1.1.0 pkgmaker_0.27 biomaRt_2.34.2 reshape_0.8.8 stringi_1.2.4
[50] RSQLite_2.1.1 RMySQL_0.10.16 GenomicFeatures_1.30.3 bibtex_0.4.2 rlang_0.3.1 pkgconfig_2.0.2 bitops_1.0-6
[57] nor1mix_1.2-3 lattice_0.20-38 purrr_0.2.5 bindr_0.1.1 GenomicAlignments_1.14.2 bit_1.1-14 tidyselect_0.2.5
[64] magrittr_1.5 R6_2.3.0 DBI_1.0.0 pillar_1.3.1 withr_2.1.2 RCurl_1.95-4.11 tibble_2.0.1
[71] crayon_1.3.4 progress_1.2.0 grid_3.4.3 data.table_1.12.0 blob_1.1.1 digest_0.6.18 xtable_1.8-3
[78] tidyr_0.8.2 openssl_1.1 munsell_0.5.0 registry_0.5
Below is the histogram of my betas (to show it's bound by zero and 1.)
Histogram of estimated cell counts4
Histogram of estimated cell counts3