Dear all,
I have performed a time-course RNA-seq experiment using maSigPro. I have found differentially expressed genes, and now intend to cluster those by their expression pattern over the time. But I got an error when trying to use the function see.genes(), cf below. I should mention that I had a warning message (glm.fit: algorithm did not converge) when running the p.vector() and T.fit() functions.
> see.genes(sigs$sig.genes$BCCvsControl, show.fit = T, dis =design$dis,cluster.method="hclust" ,cluster.data = 1, k = 9) Error in repvect[1:length(repvect)] - c(0, repvect[1:(length(repvect) - : non-numeric argument to binary operator > sessionInfo() R version 3.3.1 (2016-06-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago) 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 parallel stats graphics grDevices utils datasets [8] methods base other attached packages: [1] dplyr_0.5.0 plyr_1.8.4 [3] plotrix_3.6-4 RColorBrewer_1.1-2 [5] Hmisc_3.17-4 ggplot2_2.2.1 [7] Formula_1.2-1 survival_2.41-3 [9] lattice_0.20-35 stringr_1.2.0 [11] maptools_0.9-2 sp_1.2-4 [13] DESeq2_1.14.1 SummarizedExperiment_1.4.0 [15] GenomicRanges_1.26.4 GenomeInfoDb_1.10.3 [17] IRanges_2.8.2 S4Vectors_0.12.2 [19] maSigPro_1.48.0 MASS_7.3-45 [21] Biobase_2.32.0 BiocGenerics_0.20.0 loaded via a namespace (and not attached): [1] genefilter_1.56.0 locfit_1.5-9.1 splines_3.3.1 [4] colorspace_1.3-2 chron_2.3-47 XML_3.98-1.4 [7] foreign_0.8-66 DBI_0.6-1 BiocParallel_1.8.2 [10] zlibbioc_1.18.0 munsell_0.4.3 gtable_0.2.0 [13] latticeExtra_0.6-28 geneplotter_1.50.0 AnnotationDbi_1.36.2 [16] Rcpp_0.12.10 acepack_1.4.1 xtable_1.8-2 [19] scales_0.4.1 annotate_1.52.1 XVector_0.14.1 [22] gridExtra_2.2.1 stringi_1.1.5 grid_3.3.1 [25] bitops_1.0-6 tools_3.3.1 magrittr_1.5 [28] lazyeval_0.2.0 RCurl_1.95-4.8 tibble_1.3.0 [31] RSQLite_1.0.0 cluster_2.0.4 venn_1.2 [34] Matrix_1.2-6 data.table_1.9.6 assertthat_0.2.0 [37] R6_2.2.0 rpart_4.1-10 mclust_5.2.3 [40] nnet_7.3-12
Thanks for your help.
Regards,
Valentine Murigneux