From Seurat object to a dataframe
I am doing single-cell RNA analysis and I have Seurat files, but now I would like to transform them into dataframes and I cannot find anything that works. This are all the command that I try to perform. Do you have any idea?
data <- CreateSeuratObject(counts = data, project = "scRNA", min.cells = 3, min.features = 200)
...
G2M_cell <- ScaleData(G2M_cell)
dataf <- as.data.frame(G2M_cell)
Error in as.data.frame.default(G2M_cell) :
coercizione di classe ‘structure("Seurat", package = "SeuratObject")’ in data.frame non possibile
a <- as.data.frame(G2M_cell, genes = Seurat::VariableFeatures(G2M_cell),fix_names = TRUE)
Error in as.data.frame.default(G2M_cell, genes = Seurat::VariableFeatures(G2M_cell), :
coercizione di classe ‘structure("Seurat", package = "SeuratObject")’ in data.frame non possibile
S4_to_dataframe <- function(s4obj) {
nms <- slotNames(s4obj)
lst <- lapply(nms, function(nm) slot(s4obj, nm))
as.data.frame(setNames(lst, nms))
}
S4_to_dataframe(G2M_cell)
Error in as.data.frame.default(x[[i]], optional = TRUE) :
coercizione di classe ‘structure("Assay", package = "SeuratObject")’ in data.frame non possibile
> sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=Italian_Italy.1252 LC_CTYPE=Italian_Italy.1252
[3] LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C
[5] LC_TIME=Italian_Italy.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reticulate_1.26 magrittr_2.0.3 forcats_0.5.2 stringr_1.4.1
[5] purrr_0.3.4 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[9] tidyverse_1.3.2 ggplot2_3.3.6 RaceID_0.2.7 sp_1.5-0
[13] SeuratObject_4.1.2 Seurat_4.2.0 dplyr_1.0.10 R.utils_2.12.1
[17] R.oo_1.25.0 R.methodsS3_1.8.2
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 rtracklayer_1.54.0
[3] scattermore_0.8 princurve_2.1.6
[5] knitr_1.40 bit64_4.0.5
[7] irlba_2.3.5.1 DelayedArray_0.20.0
[9] data.table_1.14.2 rpart_4.1.16
[11] KEGGREST_1.34.0 RCurl_1.98-1.8
[13] generics_0.1.3 BiocGenerics_0.40.0
[15] GenomicFeatures_1.46.5 cowplot_1.1.1
[17] RSQLite_2.2.17 shadowtext_0.1.2
[19] RANN_2.6.1 future_1.29.0
[21] tzdb_0.3.0 bit_4.0.4
[23] enrichplot_1.14.2 lubridate_1.8.0
[25] spatstat.data_3.0-0 xml2_1.3.3
[27] httpuv_1.6.6 SummarizedExperiment_1.24.0
[29] assertthat_0.2.1 gargle_1.2.1
[31] viridis_0.6.2 xfun_0.34
[33] hms_1.1.2 evaluate_0.18
[35] promises_1.2.0.1 fansi_1.0.3
[37] restfulr_0.0.15 progress_1.2.2
[39] readxl_1.4.1 caTools_1.18.2
[41] dbplyr_2.2.1 igraph_1.3.4
[43] DBI_1.1.3 htmlwidgets_1.5.4
[45] spatstat.geom_3.0-3 googledrive_2.0.0
[47] stats4_4.1.3 ellipsis_0.3.2
[49] RSpectra_0.16-1 backports_1.4.1
[51] permute_0.9-7 biomaRt_2.50.3
[53] deldir_1.0-6 MatrixGenerics_1.6.0
[55] SingleCellExperiment_1.16.0 vctrs_0.4.1
[57] Biobase_2.54.0 ROCR_1.0-11
[59] abind_1.4-5 cachem_1.0.6
[61] withr_2.5.0 ggforce_0.3.4
[63] progressr_0.11.0 sctransform_0.3.5
[65] vegan_2.6-4 GenomicAlignments_1.30.0
[67] FateID_0.2.2 treeio_1.18.1
[69] prettyunits_1.1.1 goftest_1.2-3
[71] cluster_2.1.2 DOSE_3.20.1
[73] ape_5.6-2 lazyeval_0.2.2
[75] crayon_1.5.2 labeling_0.4.2
[77] runner_0.4.2 pkgconfig_2.0.3
[79] tweenr_2.0.2 GenomeInfoDb_1.30.1
[81] nlme_3.1-155 rlang_1.0.6
[83] globals_0.16.1 lifecycle_1.0.3
[85] miniUI_0.1.1.1 downloader_0.4
[87] filelock_1.0.2 BiocFileCache_2.2.1
[89] modelr_0.1.9 cellranger_1.1.0
[91] randomForest_4.7-1.1 polyclip_1.10-0
[93] matrixStats_0.62.0 lmtest_0.9-40
[95] som_0.3-5.1 Matrix_1.5-1
[97] aplot_0.1.8 boot_1.3-28
[99] zoo_1.8-11 reprex_2.0.2
[101] googlesheets4_1.0.1 ggridges_0.5.4
[103] pheatmap_1.0.12 png_0.1-7
[105] viridisLite_0.4.1 rjson_0.2.21
[107] bitops_1.0-7 KernSmooth_2.23-20
[109] Biostrings_2.62.0 blob_1.2.3
[111] qvalue_2.26.0 parallelly_1.32.1
[113] spatstat.random_3.0-1 gridGraphics_0.5-1
[115] S4Vectors_0.32.4 scales_1.2.1
[117] memoise_2.0.1 plyr_1.8.7
[119] ica_1.0-3 gplots_3.1.3
[121] zlibbioc_1.40.0 compiler_4.1.3
[123] scatterpie_0.1.8 BiocIO_1.4.0
[125] RColorBrewer_1.1-3 plotrix_3.8-2
[127] fitdistrplus_1.1-8 Rsamtools_2.10.0
[129] cli_3.3.0 XVector_0.34.0
[131] listenv_0.8.0 patchwork_1.1.2
[133] pbapply_1.5-0 MASS_7.3-55
[135] mgcv_1.8-39 tidyselect_1.2.0
[137] stringi_1.7.6 yaml_2.3.5
[139] GOSemSim_2.20.0 askpass_1.1
[141] locfit_1.5-9.6 ggrepel_0.9.1
[143] grid_4.1.3 fastmatch_1.1-3
[145] tools_4.1.3 future.apply_1.10.0
[147] parallel_4.1.3 rstudioapi_0.14
[149] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 gridExtra_2.3
[151] farver_2.1.1 Rtsne_0.16
[153] ggraph_2.0.6 BiocManager_1.30.19
[155] digest_0.6.29 rgeos_0.5-9
[157] FNN_1.1.3.1 shiny_1.7.3
[159] quadprog_1.5-8 Rcpp_1.0.9
[161] broom_1.0.1 GenomicRanges_1.46.1
[163] later_1.3.0 RcppAnnoy_0.0.20
[165] httr_1.4.4 AnnotationDbi_1.56.2
[167] colorspace_2.0-3 rvest_1.0.3
[169] fs_1.5.2 XML_3.99-0.10
[171] tensor_1.5 umap_0.2.9.0
[173] IRanges_2.28.0 splines_4.1.3
[175] uwot_0.1.14 yulab.utils_0.0.5
[177] tidytree_0.4.1 spatstat.utils_3.0-1
[179] graphlayouts_0.8.1 ggplotify_0.1.0
[181] plotly_4.10.1 xtable_1.8-4
[183] jsonlite_1.8.0 ggtree_3.2.1
[185] tidygraph_1.2.2 ggfun_0.0.8
[187] R6_2.5.1 pillar_1.8.1
[189] htmltools_0.5.3 mime_0.12
[191] glue_1.6.2 fastmap_1.1.0
[193] clusterProfiler_4.2.2 BiocParallel_1.28.3
[195] codetools_0.2-18 ChIPseeker_1.30.3
[197] fgsea_1.20.0 utf8_1.2.2
[199] lattice_0.20-45 spatstat.sparse_3.0-0
[201] curl_4.3.2 leiden_0.4.3
[203] gtools_3.9.3 GO.db_3.14.0
[205] openssl_2.0.4 limma_3.50.3
[207] survival_3.4-0 rmarkdown_2.17
[209] munsell_0.5.0 DO.db_2.9
[211] GenomeInfoDbData_1.2.7 haven_2.5.1
[213] reshape2_1.4.4 gtable_0.3.1
[215] coop_0.6-3 spatstat.core_2.4-4
Thank you!
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A Seurat object is a layered object of unequal dimension that cannot easily be converted to a data.frame as a whole. What exactly do you want to convert to a df? The counts? See https://github.com/satijalab/seurat/wiki/Seurat for getters.
No I would like to do analysis with RaceID but I need a dataframe
data.frame of what?
A data.frame is just a data data type, but the important part is what data you need to store in it. RaceID probably gives you what columns it needs in the data.frame, in which case you can coerce your data to match the required input.