ArchR
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20 hours ago •
GreenleafLab
GreenleafLab
Error Report - addGeneIntegrationMatrix()-Attempting to add a different number of cells and/or features
Hi,
I also occurred similar problem. The seRNA is a seurat object. The column name of seRNA@assays$RNA@counts is different from archrproject$Cellnames. I modifies the name by following code:
seRNA_2 <- readRDS("scRNA_Seurat.RDS")
counts <- GetAssayData(seRNA_2, assay = "RNA",slot = "counts")
seuratRNA_rownames <- colnames(counts)
seuratRNA_rownames[-length(seuratRNA_rownames)] <- paste0(seuratRNA_rownames[-length(seuratRNA_rownames)], '-1')
colnames(counts) <- seuratRNA_rownames
seuratRNA_2 <- CreateSeuratObject(
counts = counts,
meta.data = [email protected]
)
Since the gene name from gene scores matrix (ArchR) is different from rna matrix (seuratRNA_2). I changed the ENSEMBL ID to NCBI ID in rna matrix (seuratRNA_2). There are NAs
seuratRNA_2_ENSEMBL <- rownames(seuratRNA_2)
seuratRNA_2_annots <- mapIds(org.Hs.eg.db, keys = seuratRNA_2_ENSEMBL,
column = c('ENTREZID'), keytype = 'ENSEMBL')
rownames(seuratRNA_2@assays$RNA@counts) <- seuratRNA_2_annots
Then I added geneintegrationmatrix to the archr project (projCCD). However, I got the same error message "Error: Attempting to add a different number of cells and/or features"
projCCD <- addGeneIntegrationMatrix(
ArchRProj = projCCD,
useMatrix = "GeneScoreMatrix",
matrixName = "GeneIntegrationMatrix",
reducedDims = "IterativeLSI",
seRNA = seuratRNA_2,
addToArrow = FALSE,
force = TRUE,
verbose = FALSE,
groupRNA = "seurat_clusters",
nameCell = "predictedCell_Un",
nameGroup = "predictedGroup_Un",
nameScore = "predictedScore_Un"
)
Below is the logFile:
___ .______ ______ __ __ .______
/ \ | _ \ / || | | | | _ \
/ ^ \ | |_) | | ,----'| |__| | | |_) |
/ /_\ \ | / | | | __ | | /
/ _____ \ | |\ \\___ | `----.| | | | | |\ \\___.
/__/ \__\ | _| `._____| \______||__| |__| | _| `._____|
Logging With ArchR!
Start Time : 2023-09-22 12:24:48.640335
------- ArchR Info
ArchRThreads = 30
ArchRGenome = Hg38
------- System Info
Computer OS = unix
Total Cores = 32
------- Session Info
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: AlmaLinux 8.8 (Sapphire Caracal)
Matrix products: default
BLAS: /share/pkg.8/r/4.3.1/install/lib64/R/lib/libRblas.so
LAPACK: /share/pkg.8/r/4.3.1/install/lib64/R/lib/libRlapack.so; LAPACK version 3.11.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils datasets
[9] methods base
other attached packages:
[1] biomaRt_2.56.1 TxDb.Hsapiens.UCSC.hg38.knownGene_3.17.0
[3] GenomicFeatures_1.52.1 gridExtra_2.3
[5] uwot_0.1.16 nabor_0.5.0
[7] harmony_1.0.1 Rcpp_1.0.11
[9] ArchR_1.0.0 rhdf5_2.44.0
[11] data.table_1.14.8 SeuratObject_4.9.9.9091
[13] Seurat_4.3.0.1 BSgenome.Hsapiens.UCSC.hg38_1.4.5
[15] BSgenome_1.68.0 rtracklayer_1.60.1
[17] Biostrings_2.68.1 XVector_0.40.0
[19] TFBSTools_1.38.0 JASPAR2018_1.1.1
[21] motifmatchr_1.22.0 GenomicInteractions_1.34.0
[23] InteractionSet_1.28.1 SingleCellExperiment_1.22.0
[25] SummarizedExperiment_1.30.2 GenomicRanges_1.52.0
[27] GenomeInfoDb_1.36.3 MatrixGenerics_1.12.3
[29] matrixStats_1.0.0 clusterProfiler_4.8.3
[31] org.Hs.eg.db_3.17.0 AnnotationDbi_1.62.2
[33] IRanges_2.34.1 S4Vectors_0.38.1
[35] Biobase_2.60.0 BiocGenerics_0.46.0
[37] fclust_2.1.1.1 e1071_1.7-13
[39] Matrix_1.6-0 tibbletime_0.1.8
[41] binr_1.1.1 broom_1.0.5
[43] splitstackshape_1.4.8 reshape2_1.4.4
[45] magrittr_2.0.3 lubridate_1.9.2
[47] forcats_1.0.0 stringr_1.5.0
[49] purrr_1.0.2 readr_2.1.4
[51] tidyr_1.3.0 tibble_3.2.1
[53] tidyverse_2.0.0 dplyr_1.1.3
[55] gghighlight_0.4.0 ggthemes_4.2.4
[57] ggdendro_0.1.23 ggrastr_1.0.2
[59] ggrepel_0.9.3 ggplot2_3.4.3
[61] gplots_3.1.3 patchwork_1.1.3
[63] ComplexHeatmap_2.16.0 circlize_0.4.15
[65] RColorBrewer_1.1-3 wesanderson_0.3.6
[67] viridis_0.6.4 viridisLite_0.4.2
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 dichromat_2.0-1 progress_1.2.2
[4] urlchecker_1.0.1 nnet_7.3-19 poweRlaw_0.70.6
[7] goftest_1.2-3 vctrs_0.6.3 spatstat.random_3.1-6
[10] digest_0.6.33 png_0.1-8 shape_1.4.6
[13] proxy_0.4-27 parallelly_1.36.0 deldir_1.0-9
[16] MASS_7.3-60 httpuv_1.6.11 foreach_1.5.2
[19] qvalue_2.32.0 withr_2.5.0 xfun_0.40
[22] ggfun_0.1.3 survival_3.5-5 ellipsis_0.3.2
[25] memoise_2.0.1 ggbeeswarm_0.7.2 gson_0.1.0
[28] profvis_0.3.8 zoo_1.8-13 tidytree_0.4.2
[31] GlobalOptions_0.1.2 gtools_3.9.4 pbapply_1.7-2
[34] R.oo_1.25.0 Formula_1.2-6 prettyunits_1.1.1
[37] KEGGREST_1.40.0 promises_1.2.1 httr_1.4.7
[40] downloader_0.4 restfulr_0.0.15 rhdf5filters_1.12.1
[43] fitdistrplus_1.1-11 globals_0.16.2 ps_1.7.5
[46] rstudioapi_0.15.0 miniUI_0.1.1.1 generics_0.1.3
[49] DOSE_3.26.1 base64enc_0.1-3 processx_3.8.1
[52] curl_5.0.2 zlibbioc_1.46.0 ggraph_2.1.0
[55] polyclip_1.10-4 GenomeInfoDbData_1.2.10 xtable_1.8-6
[58] desc_1.4.2 pracma_2.4.2 doParallel_1.0.17
[61] evaluate_0.21 S4Arrays_1.0.6 BiocFileCache_2.8.0
[64] hms_1.1.3 irlba_2.3.5.1 colorspace_2.1-1
[67] filelock_1.0.2 ROCR_1.0-11 spatstat.data_3.0-1
[70] reticulate_1.32.0 lmtest_0.9-40 later_1.3.1
[73] ggtree_3.8.0 lattice_0.21-8 spatstat.geom_3.2-5
[76] future.apply_1.11.0 scattermore_1.2 XML_3.99-0.14
[79] shadowtext_0.1.2 cowplot_1.1.1 RcppAnnoy_0.0.21
[82] class_7.3-22 Hmisc_5.1-0 pillar_1.9.0
[85] nlme_3.1-162 iterators_1.0.14 caTools_1.18.2
[88] compiler_4.3.1 stringi_1.7.12 tensor_1.5
[91] devtools_2.4.5 GenomicAlignments_1.36.0 plyr_1.8.8
[94] crayon_1.5.2 abind_1.4-7 BiocIO_1.10.0
[97] gridGraphics_0.5-1 sp_2.0-0 graphlayouts_1.0.0
[100] bit_4.0.5 fastmatch_1.1-4 codetools_0.2-19
[103] biovizBase_1.48.0 GetoptLong_1.0.5 plotly_4.10.2
[106] mime_0.12 splines_4.3.1 dbplyr_2.3.2
[109] HDO.db_0.99.1 interp_1.1-4 knitr_1.44
[112] blob_1.2.4 utf8_1.2.3 clue_0.3-64
[115] seqLogo_1.66.0 AnnotationFilter_1.24.0 fs_1.6.3
[118] listenv_0.9.0 checkmate_2.2.0 pkgbuild_1.4.1
[121] Gviz_1.44.1 ggplotify_0.1.2 callr_3.7.3
[124] tzdb_0.4.0 tweenr_2.0.2 pkgconfig_2.0.3
[127] tools_4.3.1 cachem_1.0.8 RhpcBLASctl_0.23-42
[130] RSQLite_2.3.1 DBI_1.1.3 fastmap_1.1.1
[133] rmarkdown_2.25 scales_1.2.1 usethis_2.2.0
[136] ica_1.0-3 Rsamtools_2.16.0 BiocManager_1.30.21
[139] dotCall64_1.0-2 VariantAnnotation_1.46.0 RANN_2.6.1
[142] rpart_4.1.19 farver_2.1.1 tidygraph_1.2.3
[145] scatterpie_0.2.1 yaml_2.3.7 latticeExtra_0.6-30
[148] foreign_0.8-84 cli_3.6.1 leiden_0.4.3
[151] lifecycle_1.0.3 sessioninfo_1.2.2 backports_1.4.1
[154] BiocParallel_1.34.2 annotate_1.78.0 timechange_0.2.0
[157] gtable_0.3.4 rjson_0.2.21 ggridges_0.5.4
[160] progressr_0.14.0 ape_5.7-1 jsonlite_1.8.7
[163] bitops_1.0-7 bit64_4.0.5 Rtsne_0.16
[166] yulab.utils_0.1.0 spatstat.utils_3.0-3 CNEr_1.36.0
[169] GOSemSim_2.26.1 R.utils_2.12.2 lazyeval_0.2.2
[172] shiny_1.7.5 htmltools_0.5.6 enrichplot_1.20.3
[175] sctransform_0.3.5 GO.db_3.17.0 rappdirs_0.3.3
[178] ensembldb_2.24.0 glue_1.6.2 TFMPvalue_0.0.9
[181] spam_2.9-1 RCurl_1.98-1.12 rprojroot_2.0.3
[184] treeio_1.24.1 jpeg_0.1-10 igraph_1.5.1
[187] R6_2.5.1 labeling_0.4.3 cluster_2.1.4
[190] Rhdf5lib_1.22.1 pkgload_1.3.2 aplot_0.2.1
[193] DirichletMultinomial_1.42.0 DelayedArray_0.26.7 tidyselect_1.2.0
[196] vipor_0.4.5 ProtGenerics_1.32.0 htmlTable_2.4.1
[199] ggforce_0.4.1 xml2_1.3.4 future_1.33.0
[202] munsell_0.5.0 KernSmooth_2.23-21 htmlwidgets_1.6.2
[205] fgsea_1.26.0 spatstat.sparse_3.0-2 rlang_1.1.1
[208] spatstat.explore_3.2-3 remotes_2.4.2 Cairo_1.6-0
[211] fansi_1.0.4 beeswarm_0.4.0
------- Log Info
2023-09-22 12:24:49.022809 : Running Seurat's Integration Stuart* et al 2019, 0.006 mins elapsed.
2023-09-22 12:24:49.031734 : 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
orig.ident nCount_RNA nFeature_RNA sample_sampleId_short
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 hft 1397 677 <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 hft 14338 4301 <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 hft 9260 3481 <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 hft 4025 1969 <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 hft 7131 2930 <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 hft 6532 2712 <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 hft 8764 3642 <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 hft 953 523 <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 hft 2382 1087 <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 hft 5857 2500 <NA>
sample_name_at sample_time sample_cellLine
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 <NA> <NA> <NA>
sample_sampleType sample_diseaseType
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 <NA> <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 <NA> <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 <NA> <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 <NA> <NA>
sample_differentiation sample_assay sample_batch
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 <NA> <NA> <NA>
sample_barcode sample_barcodeName sample_seqrunDir
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 <NA> <NA> <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 <NA> <NA> <NA>
percentMT percentRibo cell_barcode
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA <NA>
CR_Estimated.Number.of.Cells CR_Mean.Reads.per.Cell
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Median.Genes.per.Cell CR_Number.of.Reads
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Valid.Barcodes CR_Sequencing.Saturation
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Q30.Bases.in.Barcode CR_Q30.Bases.in.RNA.Read
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Q30.Bases.in.Sample.Index CR_Q30.Bases.in.UMI
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Reads.Mapped.to.Genome
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Confidently.to.Genome
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Confidently.to.Intergenic.Regions
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Confidently.to.Intronic.Regions
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Confidently.to.Exonic.Regions
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Confidently.to.Transcriptome
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Reads.Mapped.Antisense.to.Gene
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA
CR_Fraction.Reads.in.Cells CR_Total.Genes.Detected
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA
CR_Median.UMI.Counts.per.Cell DF_pANN DF_classification
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA <NA>
DF_pANN_quantile nCount_spliced nFeature_spliced
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA NA
nCount_unspliced nFeature_unspliced nCount_ambiguous
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA NA NA
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA NA NA
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA NA NA
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA NA NA
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA NA NA
nFeature_ambiguous RNA_snn_res.0.5 seurat_clusters
hft_w20_p3_r1_AAACCCAAGCTGCGAA-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAAGGTAGTAT-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCACAACTCCAA-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCACATAGTCAC-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACAGGTG-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACGGTTT-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTACTCGCG-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTATGTCCA-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTGTATTCG-1 NA <NA> <NA>
hft_w20_p3_r1_AAACCCAGTTGCTCAA-1 NA <NA> <NA>
Input-Parameters$groupATAC: length = 0
NULL
Input-Parameters$groupRNA: length = 1
[1] "seurat_clusters"
Input-Parameters$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$scaleTo: length = 1
[1] 10000
Input-Parameters$nameCell: length = 1
[1] "predictedCell_Un"
Input-Parameters$nameGroup: length = 1
[1] "predictedGroup_Un"
Input-Parameters$nameScore: length = 1
[1] "predictedScore_Un"
Input-Parameters$threads: length = 1
[1] 30
Input-Parameters$verbose: length = 1
[1] FALSE
Input-Parameters$force: length = 1
[1] TRUE
Input-Parameters$logFile: length = 1
[1] "ArchRLogs/ArchR-addGeneIntegrationMatrix-106bc864faf635-Date-2023-09-22_Time-12-24-48.614146.log"
2023-09-22 12:24:49.070918 : Checking ATAC Input, 0.007 mins elapsed.
2023-09-22 12:24:49.080642 : Checking RNA Input, 0.007 mins elapsed.
Hi @yetingliytl! Thanks for using ArchR! Please make sure that your post belongs in the Issues section. Only bugs and error reports belong in the Issues section. Usage questions and feature requests should be posted in the Discussions section, not in Issues.
It is worth noting that there are very few actual bugs in ArchR. If you are getting an error, it is probably something specific to your dataset, usage, or computational environment, all of which are extremely challenging to troubleshoot. As such, we require reproducible examples (preferably using the tutorial dataset) from users who want assistance. If you cannot reproduce your error, we will not be able to help.
Before going through the work of making a reproducible example, search the previous Issues, Discussions, function definitions, or the ArchR manual and you will likely find the answers you are looking for.
If your post does not contain a reproducible example, it is unlikely to receive a response.
__In addition to a reproducible example, you must do the following things before we help you, unless your original post already contained this information:
1. If you've encountered an error, have you already searched previous Issues to make sure that this hasn't already been solved?
2. Did you post your log file? If not, add it now.
3. Remove any screenshots that contain text and instead copy and paste the text using markdown's codeblock syntax (three consecutive backticks). You can do this by editing your original post.
