One of the base methods failed on this dataset

[stemangiola]

Hello,
I get plenty of these errors while it was not happening before.

Log fold changes are estimated using limma package ... 
limma DE analysis is carried out ... 
EGSEA is running on the provided data and h collection

EGSEA is running on the provided data and c1 collection

Error in runegsea(voom.results = voom.results, contrast = contrast, limma.tops = limma.tops,  : 
  ERROR: One of the base methods failed on this dataset (zscore).
Remove it and try again.
See error messages for more information.

Could you please add a running example in the README file here, so users can check whether the baseline algorithm is working, and check the input formats.

egsea(
					voom.results=v, 
					contrasts=contrasts, 
					gs.annots=buildIdx(entrezIDs=rownames(v), species="human"), 
					symbolsMap=
						v %$% 
						genes %>% 
						dplyr::select(1:2) %>%
						setNames(c("FeatureID", "Symbols")),
					baseGSEAs = egsea.base()[-c(8, 5, 4)],
					sort.by="med.rank",
					num.threads = 16
				)


Browse[1]> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.4 (Final)

Matrix products: default
BLAS: /wehisan/general/system/bioinf-software/bioinfsoftware/R/R-3.5.0/lib64/R/lib/libRblas.so
LAPACK: /wehisan/general/system/bioinf-software/bioinfsoftware/R/R-3.5.0/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] EGSEA_1.9.1          EGSEAdata_1.8.0      foreach_1.4.6        RColorBrewer_1.1-2   pathview_1.20.0      topGO_2.32.0        
 [7] SparseM_1.77         GO.db_3.6.0          graph_1.58.0         gage_2.30.0          BiocInstaller_1.30.0 bindrcpp_0.2.2      
[13] ggdendro_0.1-20      ruv_0.9.7            sva_3.28.0           BiocParallel_1.13.3  genefilter_1.61.1    mgcv_1.8-23         
[19] nlme_3.1-137         magrittr_1.5         forcats_0.3.0        stringr_1.3.1        dplyr_0.7.6          purrr_0.2.5         
[25] readr_1.1.1          tidyr_0.8.1          tibble_1.4.2         ggplot2_3.0.0.9000   tidyverse_1.2.1      hgu95av2.db_3.2.3   
[31] org.Hs.eg.db_3.6.0   AnnotationDbi_1.42.1 IRanges_2.14.11      S4Vectors_0.18.3     Biobase_2.40.0       BiocGenerics_0.26.0 
[37] edgeR_3.22.3         limma_3.36.3        

loaded via a namespace (and not attached):
  [1] utf8_1.1.3               R.utils_2.6.0            tidyselect_0.2.4         RSQLite_2.1.0            htmlwidgets_1.2         
  [6] grid_3.5.0               KEGG.db_3.2.3            R2HTML_2.3.2             devtools_1.13.6          munsell_0.5.0           
 [11] codetools_0.2-15         DT_0.4                   KEGGdzPathwaysGEO_1.18.0 withr_2.1.2              colorspace_1.4-0        
 [16] knitr_1.20               rstudioapi_0.7           gbRd_0.4-11              Rdpack_0.8-0             labeling_0.3            
 [21] git2r_0.21.0             KEGGgraph_1.40.0         org.Rn.eg.db_3.6.0       mnormt_1.5-5             hwriter_1.3.2           
 [26] bit64_0.9-7              R6_2.2.2                 Glimma_1.8.2             locfit_1.5-9.1           bitops_1.0-6            
 [31] assertthat_0.2.0         promises_1.0.1           scales_1.0.0             gtable_0.2.0             org.Mm.eg.db_3.6.0      
 [36] rlang_0.2.1              splines_3.5.0            lazyeval_0.2.1           PADOG_1.22.0             broom_0.4.4             
 [41] yaml_2.1.18              reshape2_1.4.3           modelr_0.1.2             httpuv_1.4.5             tools_3.5.0             
 [46] psych_1.8.3.3            gplots_3.0.3             HTMLUtils_0.1.7          Rcpp_0.12.18             plyr_1.8.4              
 [51] zlibbioc_1.25.0          RCurl_1.95-4.10          hgu133plus2.db_3.2.3     haven_1.1.1              ggrepel_0.7.0           
 [56] data.table_1.10.4-3      R.cache_0.13.0           matrixStats_0.53.1       hms_0.4.2                mime_0.5                
 [61] GSVA_1.28.0              xtable_1.8-3             globaltest_5.33.0        XML_3.98-1.11            hgu133a.db_3.2.3        
 [66] readxl_1.1.0             gridExtra_2.3            compiler_3.5.0           safe_3.20.0              KernSmooth_2.23-15      
 [71] crayon_1.3.4             R.oo_1.22.0              htmltools_0.3.6          later_0.7.3              geneplotter_1.57.0      
 [76] lubridate_1.7.4          DBI_1.0.0                MASS_7.3-50              Matrix_1.2-14            cli_1.0.0               
 [81] R.methodsS3_1.7.1        gdata_2.18.0             metap_1.0                bindr_0.1.1              pkgconfig_2.0.1         
 [86] registry_0.5             foreign_0.8-70           plotly_4.8.0             xml2_1.2.0               annotate_1.57.3         
 [91] rngtools_1.2.4           pkgmaker_0.25.8          XVector_0.19.9           bibtex_0.4.2             rvest_0.3.2             
 [96] R.rsp_0.42.0             doRNG_1.6.6              digest_0.6.16            Biostrings_2.47.12       cellranger_1.1.0        
[101] GSEABase_1.42.0          curl_3.2                 shiny_1.1.0              gtools_3.5.0             GSA_1.03                
[106] jsonlite_1.5             viridisLite_0.3.0        pillar_1.2.2             lattice_0.20-35          KEGGREST_1.19.2         
[111] httr_1.3.1               survival_2.42-3          glue_1.3.0               png_0.1-7                shinythemes_1.1.1       
[116] iterators_1.0.9          bit_1.1-13               Rgraphviz_2.23.0         stringi_1.2.3            blob_1.1.1              
[121] caTools_1.17.1           memoise_1.1.0  

Thanks

[stemangiola]

Here is my query data

Browse[1]> v
An object of class "EList"
$genes
    ENTREZID       SYMBOL length
1     653635       WASH7P   1769
3     645520      FAM138A   1130
5     729737    LOC729737   3402
7  100132287 LOC100132287   4370
13 100133331 LOC100133331   4273
18804 more rows ...

$targets
                                       group lib.size norm.factors
alignment_hg19.10922PP.Aligned.out.sam     0 55574256     1.014363
alignment_hg19.10935PP.Aligned.out.sam     0 51958569     1.025027
alignment_hg19.10973PP.Aligned.out.sam     0 52816361     0.893730
alignment_hg19.10976PP.Aligned.out.sam     0 59232135     1.075921
alignment_hg19.10985PP.Aligned.out.sam     0 58639448     1.075331
16 more rows ...

$E
            10922PP   10935PP   10973PP   10976PP   10985PP   11026PP   11045PP   11082PP   11086PP   11103PP   11104PP   11126PP
653635     5.604081  5.039951  5.584493  4.890869  5.011973  5.010928  5.117168  4.799015  4.797974  5.050032  4.945128  4.829888
645520    -4.211382 -4.198488 -3.697884 -3.696114 -4.528971 -3.397952 -3.705004 -6.925601 -3.960871 -4.930459 -4.716059 -5.092687
729737     3.824993  4.609482  3.465280  4.259380  4.203964  4.313154  4.567350  6.089856  3.833042  5.503673  5.119955  5.297468
100132287  4.548351  5.029626  4.109502  4.412113  4.275909  4.654243  4.267375  5.075666  3.979327  4.621285  4.725020  4.915434
100133331  4.975838  5.506088  4.510359  4.908113  4.725369  5.005160  4.690004  5.545907  4.543993  5.173705  5.358027  5.275150
            11132PP   11147PP   11160PP   11164PP   11165PP   11182PP   11184PP   11204PP   11218PP
653635     5.039247  4.844473  4.755644  5.588582  5.120050  4.905859  4.843288  4.928274  4.949579
645520    -4.644474 -4.395389 -4.113634 -4.043132 -2.753740 -4.921397 -4.688232 -4.325610 -6.532291
729737     4.773614  4.379706  4.460666  6.712473  4.466282  4.207648  6.322472  3.998379  4.176103
100132287  4.644773  4.208029  4.400928  5.938243  4.711997  4.029551  5.341357  3.689751  4.669379
100133331  5.139895  4.664548  4.926419  6.186561  4.909360  4.738785  5.559488  4.325637  5.058539
18804 more rows ...

$weights
           [,1]       [,2]       [,3]      [,4]       [,5]       [,6]      [,7]       [,8]       [,9]      [,10]      [,11]
[1,] 21.2429199 20.9413056 21.2037933 21.394000 21.4156158 21.5281834 21.325997 21.1120350 21.1827658 20.9007900 20.9647229
[2,]  0.5964591  0.5779403  0.6111605  0.603389  0.6122545  0.6070699  0.601531  0.5020441  0.5243727  0.4967035  0.5045715
[3,] 17.5303286 17.0085108 12.1745389 19.669321 17.8613489 21.3841895 18.405990 21.9550455 18.5751998 21.4896710 20.6941503
[4,] 18.5823782 18.0794009 15.6762277 19.836502 18.9379667 20.9334772 19.112087 21.0734817 18.3766811 20.1605086 19.5222645
[5,] 20.5738587 20.1943243 18.6399366 21.272716 20.7955789 21.7787045 20.872070 21.8859496 20.7145407 21.5333704 21.2654398
          [,12]     [,13]      [,14]      [,15]      [,16]      [,17]      [,18]      [,19]     [,20]      [,21]
[1,] 20.7337806 21.089438 21.2500715 21.7570302 21.3857468 20.1645606 20.7628281 19.3392450 20.600847 20.0333440
[2,]  0.5535584  0.586521  0.5935915  0.5595733  0.5167748  0.4788058  0.5068143  0.4177498  0.491397  0.4742921
[3,] 20.1917869 17.410882 18.5830424 21.8887852 21.8404216 16.6642665 16.1872050 20.4650266 18.684049 16.3105150
[4,] 19.6582995 18.400080 19.1214933 21.2068422 21.5432738 16.5803805 16.7483484 21.7934442 17.979862 16.2933146
[5,] 21.0696960 20.427425 20.8602313 21.9425776 21.9555523 19.4046912 19.6475793 21.9604710 20.370399 19.1927803
18804 more rows ...

$design
  high neoadjuvant          W
1    1           0 -0.1290363
2    1           0 -0.1270002
3    1           0 -0.4260002
4    1           0 -0.0167777
5    1           0 -0.1356399
16 more rows ...

Browse[1]> contrasts
             Contrasts
Levels        MUvsWT
  high            -1
  neoadjuvant      1
  W                       0

Browse[1]> v %$% 
+ 						genes %>% 
+ 						dplyr::select(1:2) %>%
+ 						setNames(c("FeatureID", "Symbols")) %>% head()
   FeatureID      Symbols
1     653635       WASH7P
3     645520      FAM138A
5     729737    LOC729737
7  100132287 LOC100132287
13 100133331 LOC100133331
14 100288069 LOC100288069

Cheers