hello all, i am doing a differential gene expression analysis using limma, i have 3 conditions S0,S1, and S3. I am creating the contrasts and doing the fit function but its giving the error ( Number of rows of contrast matrix must match number of coefficients in fit)

> contrast.matrix <- makeContrasts(
+   S1_vs_S0 = S1 - S0,
+   S3_vs_S0 = S3 - S0,
+   S1_vs_S3 = S1 - S3,
+   levels = design
+ )
> 
> # Fit contrasts
> fit2 <- limma::contrasts.fit(fit, contrast.matrix)
Error in limma::contrasts.fit(fit, contrast.matrix) : 
  Number of rows of contrast matrix must match number of coefficients in fit

the number of row in contrast matrix is 3L  and nb of coefficient in fit 24670L

> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.1.1/lib/libopenblasp-r0.3.18.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] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rtracklayer_1.54.0                       DiffBind_3.4.11                          BiocParallel_1.28.3                     
 [4] BiocManager_1.30.23                      openxlsx_4.2.5.2                         TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0
 [7] GenomicFeatures_1.46.5                   org.Hs.eg.db_3.14.0                      AnnotationDbi_1.56.1                    
[10] ChIPpeakAnno_3.28.0                      ChIPseeker_1.30.3                        magrittr_2.0.3                          
[13] lubridate_1.9.3                          forcats_1.0.0                            stringr_1.5.1                           
[16] dplyr_1.1.4                              purrr_1.0.2                              readr_2.1.5                             
[19] tidyr_1.3.1                              tibble_3.2.1                             tidyverse_2.0.0                         
[22] DESeq2_1.34.0                            SummarizedExperiment_1.24.0              MatrixGenerics_1.6.0                    
[25] matrixStats_0.62.0                       GenomicRanges_1.46.1                     GenomeInfoDb_1.30.1                     
[28] IRanges_2.28.0                           S4Vectors_0.32.4                         Biobase_2.54.0                          
[31] BiocGenerics_0.40.0                      limma_3.50.3                             ggplot2_3.5.1                           

loaded via a namespace (and not attached):
  [1] utf8_1.2.2                              tidyselect_1.2.1                        RSQLite_2.2.8                          
  [4] htmlwidgets_1.5.4                       grid_4.1.1                              scatterpie_0.1.7                       
  [7] munsell_0.5.0                           systemPipeR_2.0.4                       withr_2.5.0                            
 [10] colorspace_2.0-3                        GOSemSim_2.20.0                         filelock_1.0.2                         
 [13] rstudioapi_0.16.0                       DOSE_3.20.1                             labeling_0.4.2                         
 [16] bbmle_1.0.24                            GenomeInfoDbData_1.2.7                  mixsqp_0.3-43                          
 [19] hwriter_1.3.2                           polyclip_1.10-0                         bit64_4.0.5                            
 [22] farver_2.1.1                            coda_0.19-4                             vctrs_0.6.5                            
 [25] treeio_1.18.1                           generics_0.1.3                          lambda.r_1.2.4                         
 [28] timechange_0.3.0                        BiocFileCache_2.2.0                     regioneR_1.26.0                        
 [31] R6_2.5.1                                apeglm_1.16.0                           graphlayouts_0.8.0                     
 [34] invgamma_1.1                            locfit_1.5-9.4                          AnnotationFilter_1.18.0                
 [37] bitops_1.0-7                            cachem_1.0.6                            fgsea_1.20.0                           
 [40] gridGraphics_0.5-1                      DelayedArray_0.20.0                     BiocIO_1.4.0                           
 [43] scales_1.3.0                            ggraph_2.0.5                            enrichplot_1.14.1                      
 [46] gtable_0.3.1                            ensembldb_2.18.4                        tidygraph_1.2.0                        
 [49] rlang_1.1.3                             genefilter_1.76.0                       splines_4.1.1                          
 [52] lazyeval_0.2.2                          yaml_2.3.6                              reshape2_1.4.4                         
 [55] qvalue_2.26.0                           RBGL_1.70.0                             tools_4.1.1                            
 [58] ggplotify_0.1.0                         gplots_3.1.3.1                          RColorBrewer_1.1-3                     
 [61] Rcpp_1.0.9                              plyr_1.8.7                              progress_1.2.2                         
 [64] zlibbioc_1.40.0                         RCurl_1.98-1.9                          prettyunits_1.1.1                      
 [67] viridis_0.6.2                           ashr_2.2-47                             ggrepel_0.9.1                          
 [70] data.table_1.14.2                       futile.options_1.0.1                    DO.db_2.9                              
 [73] truncnorm_1.0-8                         mvtnorm_1.1-3                           SQUAREM_2021.1                         
 [76] amap_0.8-19                             ProtGenerics_1.26.0                     hms_1.1.3                              
 [79] patchwork_1.1.1                         xtable_1.8-4                            XML_3.99-0.9                           
 [82] VennDiagram_1.7.0                       emdbook_1.3.12                          jpeg_0.1-9                             
 [85] gridExtra_2.3                           compiler_4.1.1                          biomaRt_2.50.0                         
 [88] bdsmatrix_1.3-7                         KernSmooth_2.23-20                      crayon_1.5.2                           
 [91] shadowtext_0.0.9                        htmltools_0.5.2                         ggfun_0.0.4                            
 [94] tzdb_0.2.0                              geneplotter_1.72.0                      aplot_0.1.1                            
 [97] DBI_1.2.2                               tweenr_1.0.2                            formatR_1.11                           
[100] dbplyr_2.5.0                            MASS_7.3-54                             rappdirs_0.3.3                         
[103] boot_1.3-28                             ShortRead_1.52.0                        Matrix_1.3-4                           
[106] cli_3.6.2                               parallel_4.1.1                          igraph_1.3.1                           
[109] pkgconfig_2.0.3                         TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicAlignments_1.30.0               
[112] numDeriv_2016.8-1.1                     xml2_1.3.3                              InteractionSet_1.22.0                  
[115] ggtree_3.2.1                            annotate_1.72.0                         multtest_2.50.0                        
[118] XVector_0.34.0                          yulab.utils_0.0.4                       digest_0.6.30                          
[121] graph_1.72.0                            Biostrings_2.62.0                       fastmatch_1.1-3                        
[124] tidytree_0.3.6                          edgeR_3.36.0                            restfulr_0.0.13                        
[127] GreyListChIP_1.26.0                     curl_5.2.1                              Rsamtools_2.10.0                       
[130] gtools_3.9.3                            rjson_0.2.20                            lifecycle_1.0.3                        
[133] nlme_3.1-153                            jsonlite_1.8.8                          futile.logger_1.4.3                    
[136] viridisLite_0.4.1                       BSgenome_1.62.0                         fansi_1.0.3                            
[139] pillar_1.9.0                            lattice_0.20-45                         KEGGREST_1.34.0                        
[142] fastmap_1.1.0                           httr_1.4.7                              plotrix_3.8-2                          
[145] survival_3.2-13                         GO.db_3.14.0                            glue_1.6.2                             
[148] zip_2.2.0                               png_0.1-7                               bit_4.0.4                              
[151] ggforce_0.3.3                           stringi_1.7.8                           blob_1.2.2                             
[154] latticeExtra_0.6-29                     caTools_1.18.2                          memoise_2.0.1                          
[157] irlba_2.3.5.1                           ape_5.5                                
>

I dont really understand the problem becaus ethe concept of coeficient in fit is not so clear to me.

Thank u in advance.

I doubt you have that many contrasts. I think that is probably the number of rows. What does dim(fit) return (and head(fit$coef))?