Hi,

I'm trying to use metagenomSeq to perform something like a one-way ANOVA on 3
groups of samples. I have managed to succesfully normalize the data properly with metagenomeSeq but don´t know to run the anova or kruskall-w test with a metagenomeSeq object.

Thanks,

I'm not sure metagenomeSeq is set up to do a Kruskal-Wallis. The closest it gets to doing non-parametric analysis is a permutation null distribution. Anyway, I think the conventional linear modeling is covered pretty well in section 4.2 of the vignette. Have you read that part? If so, is there something you don't understand?

Ok i see,

I´m not familiar with limma.  I have tried the following:

fit = fitZig(obj,mod) 
fit2 = fit$fit 

topTable(fit2)

Error in topTable(fit2) : Need to run eBayes or treat first

fit2 <- ebayes(fit2)
topTable(fit2)
Error in topTable(fit3) : fit must be an MArrayLM object

Still don´t know how to get the overall pvalue and not the pvalue for all taxa.

Can i get the output of fitZig into a data.frame so i´m free to use aov() , tukeyHSD....

??????

Any idea.

> sessionInfo()
R version 3.2.4 (2016-03-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.4 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                  LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8        LC_MONETARY=ca_AD.UTF-8       LC_MESSAGES=en_US.UTF-8      
 [7] LC_PAPER=ca_AD.UTF-8          LC_NAME=ca_AD.UTF-8           LC_ADDRESS=ca_AD.UTF-8        LC_TELEPHONE=ca_AD.UTF-8      LC_MEASUREMENT=ca_AD.UTF-8    LC_IDENTIFICATION=ca_AD.UTF-8

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

other attached packages:
 [1] xtable_1.8-2               biom_0.3.12                knitr_1.12.3               data.table_1.9.6           vegan_2.3-5                lattice_0.20-33           
 [7] permute_0.9-0              hexbin_1.27.1              vsn_3.38.0                 metagenomeSeq_1.12.1       glmnet_2.0-5               foreach_1.4.3             
[13] Matrix_1.2-6               limma_3.26.9               scales_0.4.0               gridExtra_2.2.1            gplots_3.0.1               xlsx_0.5.7                
[19] xlsxjars_0.6.1             rJava_0.9-8                RColorBrewer_1.1-2         DESeq2_1.10.1              RcppArmadillo_0.6.700.3.0  Rcpp_0.12.4               
[25] SummarizedExperiment_1.0.2 Biobase_2.30.0             GenomicRanges_1.22.4       GenomeInfoDb_1.6.3         IRanges_2.4.8              S4Vectors_0.8.11          
[31] BiocGenerics_0.16.1        phyloseq_1.14.0            psych_1.5.8                reshape2_1.4.1             ggplot2_2.1.0              plyr_1.8.3                
[37] stringr_1.0.0             

loaded via a namespace (and not attached):
 [1] nlme_3.1-127          bitops_1.0-6          matrixStats_0.50.2    tools_3.2.4           affyio_1.40.0         rpart_4.1-10          KernSmooth_2.23-15    Hmisc_3.17-4         
 [9] DBI_0.4               mgcv_1.8-12           colorspace_1.2-6      ade4_1.7-4            nnet_7.3-12           mnormt_1.5-4          compiler_3.2.4        preprocessCore_1.32.0
[17] chron_2.3-47          formatR_1.3           labeling_0.3          caTools_1.17.1        genefilter_1.52.1     affy_1.48.0           digest_0.6.9          foreign_0.8-66       
[25] rmarkdown_0.9.6       XVector_0.10.0        htmltools_0.3.5       highr_0.5.1           RSQLite_1.0.0         BiocInstaller_1.20.1  BiocParallel_1.4.3    gtools_3.5.0         
[33] acepack_1.3-3.3       magrittr_1.5          Formula_1.2-1         futile.logger_1.4.1   munsell_0.4.3         ape_3.4               yaml_2.1.13           stringi_1.0-1        
[41] MASS_7.3-45           RJSONIO_1.3-0         zlibbioc_1.16.0       grid_3.2.4            gdata_2.17.0          Biostrings_2.38.4     splines_3.2.4         multtest_2.26.0      
[49] annotate_1.48.0       locfit_1.5-9.1        igraph_1.0.1          geneplotter_1.48.0    codetools_0.2-14      futile.options_1.0.0  XML_3.98-1.4          evaluate_0.9         
[57] latticeExtra_0.6-28   lambda.r_1.1.7        gtable_0.2.0          survival_2.39-2       iterators_1.0.8       AnnotationDbi_1.32.3  cluster_2.0.4        
> 

Great !!!!

thanks