How to get scRNA data for integration with scATAC data?

[xiezhan]

hi
in the manual of ArchR,the scRNA data wes provided directly.

https://jeffgranja.s3.amazonaws.com/ArchR/TestData/scRNA-Hematopoiesis-Granja-2019.rds"

What i want to know is how to get this data?in other words,i want to know the process to generate the data

I can't use scRNA' Seurat data for integration

Would you tell me that?

thanks so much

[rcorces]

Please make sure to follow instructions. This is not a bug - it is a question and it belongs in the Discussions. This is clearly indicated when you open a new issue:

The answer to your question is in the manual:

[xiezhan]

thanks so much for your reply

But i got another mistake：

“Defined for objects of type "S4" [[<- can only be used in environment subclasses”

Would you give me some advice?

thanks so much

[xiezhan]

           ___      .______        ______  __    __  .______      
          /   \     |   _  \      /      ||  |  |  | |   _  \     
         /  ^  \    |  |_)  |    |  ,----'|  |__|  | |  |_)  |    
        /  /_\  \   |      /     |  |     |   __   | |      /     
       /  _____  \  |  |\  \\___ |  `----.|  |  |  | |  |\  \\___.
      /__/     \__\ | _| `._____| \______||__|  |__| | _| `._____|
    
Logging With ArchR!

Start Time : 2021-07-06 10:52:05

------- ArchR Info

ArchRThreads = 10
ArchRGenome = Hg38

------- System Info

Computer OS = unix
Total Cores = 72

------- Session Info

R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /BI/zxie/Software/R-4.0.2/lib64/R/lib/libRblas.so
LAPACK: /BI/zxie/Software/R-4.0.2/lib64/R/lib/libRlapack.so

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

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

other attached packages:
 [1] SeuratObject_4.0.0                Rtsne_0.15                       
 [3] uwot_0.1.10                       ggridges_0.5.3                   
 [5] nabor_0.5.0                       Seurat_3.2.2                     
 [7] gridExtra_2.3                     Rsamtools_2.6.0                  
 [9] BSgenome.Hsapiens.UCSC.hg38_1.4.3 BSgenome_1.58.0                  
[11] rtracklayer_1.50.0                Biostrings_2.58.0                
[13] XVector_0.30.0                    ArchR_1.0.1                      
[15] magrittr_2.0.1                    rhdf5_2.34.0                     
[17] Matrix_1.3-2                      data.table_1.14.0                
[19] SummarizedExperiment_1.20.0       Biobase_2.50.0                   
[21] GenomicRanges_1.42.0              GenomeInfoDb_1.26.7              
[23] IRanges_2.24.1                    S4Vectors_0.28.1                 
[25] BiocGenerics_0.36.1               MatrixGenerics_1.2.1             
[27] matrixStats_0.59.0                ggplot2_3.3.3                    

loaded via a namespace (and not attached):
  [1] colorspace_2.0-1         deldir_0.2-10            ellipsis_0.3.1          
  [4] spatstat.data_1.7-0      rstudioapi_0.13          leiden_0.3.7            
  [7] listenv_0.8.0            farver_2.1.0             ggrepel_0.9.1           
 [10] RSpectra_0.16-0          fansi_0.5.0              codetools_0.2-18        
 [13] splines_4.0.2            polyclip_1.10-0          jsonlite_1.7.2          
 [16] Cairo_1.5-12.2           ica_1.0-2                cluster_2.1.2           
 [19] png_0.1-7                sctransform_0.3.2        shiny_1.6.0             
 [22] compiler_4.0.2           httr_1.4.2               assertthat_0.2.1        
 [25] fastmap_1.1.0            lazyeval_0.2.2           cli_2.5.0               
 [28] later_1.1.0.1            htmltools_0.5.1.1        tools_4.0.2             
 [31] rsvd_1.0.3               igraph_1.2.6             gtable_0.3.0            
 [34] glue_1.4.2               GenomeInfoDbData_1.2.4   reshape2_1.4.4          
 [37] RANN_2.6.1               dplyr_1.0.5              spatstat_1.64-1         
 [40] Rcpp_1.0.6               vctrs_0.3.7              rhdf5filters_1.2.1      
 [43] nlme_3.1-151             lmtest_0.9-38            stringr_1.4.0           
 [46] globals_0.14.0           mime_0.10                miniUI_0.1.1.1          
 [49] lifecycle_1.0.0          irlba_2.3.3              gtools_3.9.2            
 [52] goftest_1.2-2            XML_3.99-0.6             future_1.21.0           
 [55] zlibbioc_1.36.0          MASS_7.3-54              zoo_1.8-8               
 [58] scales_1.1.1             spatstat.utils_2.0-0     promises_1.1.1          
 [61] RColorBrewer_1.1-2       reticulate_1.18          pbapply_1.4-3           
 [64] rpart_4.1-15             stringi_1.5.3            BiocParallel_1.24.1     
 [67] rlang_0.4.11             pkgconfig_2.0.3          bitops_1.0-7            
 [70] lattice_0.20-44          tensor_1.5               ROCR_1.0-11             
 [73] purrr_0.3.4              Rhdf5lib_1.12.1          GenomicAlignments_1.26.0
 [76] patchwork_1.1.1          htmlwidgets_1.5.3        labeling_0.4.2          
 [79] cowplot_1.1.1            tidyselect_1.1.0         parallelly_1.26.0       
 [82] RcppAnnoy_0.0.18         plyr_1.8.6               R6_2.5.0                
 [85] generics_0.1.0           DelayedArray_0.16.3      DBI_1.1.1               
 [88] mgcv_1.8-33              pillar_1.6.0             withr_2.4.1             
 [91] fitdistrplus_1.1-3       abind_1.4-5              survival_3.2-7          
 [94] RCurl_1.98-1.3           tibble_3.1.0             future.apply_1.7.0      
 [97] crayon_1.4.1             KernSmooth_2.23-20       utf8_1.2.1              
[100] plotly_4.9.3             digest_0.6.27            xtable_1.8-4            
[103] tidyr_1.1.3              httpuv_1.5.5             munsell_0.5.0           
[106] viridisLite_0.4.0       


------- Log Info

2021-07-06 10:52:05 : Running Seurat's Integration Stuart* et al 2019, 0.004 mins elapsed.

2021-07-06 10:52:05 : Input-Parameters, Class = list

Input-Parameters$: length = 1
                       
1 function (name)      
2 .Internal(args(name))


Input-Parameters$ArchRProj: length = 1

Input-Parameters$useMatrix: length = 1
[1] "GeneScoreMatrix"


Input-Parameters$matrixName: length = 1
[1] "GeneIntegrationMatrix"


Input-Parameters$reducedDims: length = 1
[1] "IterativeLSI"


Input-Parameters$seRNA: length = 1

Input-Parameters$groupATAC: length = 0
NULL


Input-Parameters$groupRNA: length = 1
[1] "celltype_anno"


Input-Parameters$groupList: length = 0
NULL


Input-Parameters$sampleCellsATAC: length = 1
[1] 10000


Input-Parameters$sampleCellsRNA: length = 1
[1] 10000


Input-Parameters$embeddingATAC: length = 0
NULL


Input-Parameters$embeddingRNA: length = 0
NULL


Input-Parameters$dimsToUse: length = 30
[1] 1 2 3 4 5 6


Input-Parameters$scaleDims: length = 0
NULL


Input-Parameters$corCutOff: length = 1
[1] 0.75


Input-Parameters$plotUMAP: length = 1
[1] TRUE


Input-Parameters$nGenes: length = 1
[1] 2000


Input-Parameters$useImputation: length = 1
[1] TRUE


Input-Parameters$reduction: length = 1
[1] "cca"


Input-Parameters$addToArrow: length = 1
[1] FALSE


Input-Parameters$scaleTo: length = 1
[1] 10000


Input-Parameters$genesUse: length = 0
NULL


Input-Parameters$nameCell: length = 1
[1] "predictedCell_Un"


Input-Parameters$nameGroup: length = 1
[1] "predictedGroup_Un"


Input-Parameters$nameScore: length = 1
[1] "predictedScore_Un"


Input-Parameters$threads: length = 1
[1] 10


Input-Parameters$verbose: length = 1
[1] TRUE


Input-Parameters$force: length = 1
[1] FALSE


Input-Parameters$logFile: length = 1
[1] "ArchRLogs/ArchR-addGeneIntegrationMatrix-14c2248eae8c-Date-2021-07-06_Time-10-52-05.log"


2021-07-06 10:52:06 : Checking ATAC Input, 0.01 mins elapsed.
2021-07-06 10:52:06 : Checking RNA Input, 0.01 mins elapsed.