Hi,

I am very new to R and DeSeq2 and i've been facing difficulties importing my dataset which is a taxon count table i obtained from running diamond blast and Megan6 (a metagenome analyzer tool). I've been trying top follow guideline on http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#count-matrix-input, but have trouble understanding where im going wrong. Help will be much appreciated. Thank you

Code should be placed in three backticks as shown below

    countData<- read.csv('/media/combio7/SONY_64M1/deseq2/out.csv', header = TRUE, sep = ",")
    metaData <- read.csv('/media/combio7/SONY_64M1/deseq2/Comparison_bs-season2_70622-metadata.csv', header = TRUE, sep = ",")

# include your problematic code here with any corresponding output 

cts <- as.matrix(read.csv(countData,sep=",",row.names="Datasets"))

Error is as below:
Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 'as.matrix': 'file' must be a character string or connection


# please also include the results of running the following in an R session 
![this is how my metadata looks][1]
![here is a screenshot of my dataframe (taxon count table)[1]

sessionInfo( )
> sessionInfo( )
R version 4.1.3 (2022-03-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /home/combio7/anaconda3/envs/r4-base/lib/libopenblasp-r0.3.21.so

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

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

other attached packages:
 [1] ggplot2_3.3.6               DESeq2_1.34.0              
 [3] SummarizedExperiment_1.24.0 Biobase_2.54.0             
 [5] MatrixGenerics_1.6.0        matrixStats_0.62.0         
 [7] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
 [9] IRanges_2.28.0              S4Vectors_0.32.4           
[11] BiocGenerics_0.40.0        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9             locfit_1.5-9.6         lattice_0.20-45       
 [4] png_0.1-7              Biostrings_2.62.0      assertthat_0.2.1      
 [7] utf8_1.2.2             R6_2.5.1               RSQLite_2.2.15        
[10] httr_1.4.3             pillar_1.8.0           zlibbioc_1.40.0       
[13] rlang_1.0.4            annotate_1.72.0        blob_1.2.3            
[16] Matrix_1.4-1           splines_4.1.3          BiocParallel_1.28.3   
[19] geneplotter_1.72.0     RCurl_1.98-1.8         bit_4.0.4             
[22] munsell_0.5.0          DelayedArray_0.20.0    compiler_4.1.3        
[25] pkgconfig_2.0.3        tidyselect_1.1.2       KEGGREST_1.34.0       
[28] tibble_3.1.8           GenomeInfoDbData_1.2.7 XML_3.99-0.10         
[31] fansi_1.0.3            withr_2.5.0            crayon_1.5.1          
[34] dplyr_1.0.9            bitops_1.0-7           grid_4.1.3            
[37] xtable_1.8-4           gtable_0.3.0           lifecycle_1.0.1       
[40] DBI_1.1.3              magrittr_2.0.3         scales_1.2.0          
[43] cli_3.3.0              cachem_1.0.6           XVector_0.34.0        
[46] genefilter_1.76.0      vctrs_0.4.1            generics_0.1.3        
[49] RColorBrewer_1.1-3     tools_4.1.3            bit64_4.0.5           
[52] glue_1.6.2             purrr_0.3.4            parallel_4.1.3        
[55] fastmap_1.1.0          survival_3.4-0         AnnotationDbi_1.56.2  
[58] colorspace_2.0-3       memoise_2.0.1

You already imported countData with read.csv(), so it has no sense to make read.csv again on countData. Simply do cts <- as.matrix(countData)