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stuart-lab / signac

R toolkit for the analysis of single-cell chromatin data
https://stuartlab.org/signac/
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ClosestFeature function error #394

Closed widsquid closed 3 years ago

widsquid commented 3 years ago

Hello Tim and team, As always thanks for your excellent work. I am in the process of using Signac to analyze some scATAC data and found the ClosestFeature function gave the following error:

open_MPs <- rownames(da_peaksMP[da_peaksMP$avg_log2FC > 0.25, ]) closest_MPs <- ClosestFeature(Efl, open_MPs) Error in validObject(.Object) : invalid class "GRanges" object: 'x@strand' is not parallel to 'x'

Then I noticed

open_MPs character(0)

Looking at da_peaksMP I see

head(da_peaksMP) p_val avg_logFC pct.1 pct.2 p_val_adj chr17-27799561-27801325 2.666494e-41 0.5395016 0.405 0.114 4.618341e-36 chr16-52133995-52136075 4.121350e-34 0.4261114 0.404 0.136 7.138138e-29

Tried this (without the 2 in log2FC) and it works open_MPs <- rownames(da_peaksMP[da_peaksMP$avg_logFC > 0.25, ])

This seems fine except I am getting only 13 da peaks total and no -FC at all profiling ~1000 vs ~4000 cells in the comparison. This seem low so I am wondering if I am missing something.

Now when I saw this error I recalled getting the same error when attempting to run links <- ConnectionsToLinks(conns = conns, ccans = ccans) from the "Finding co-accessible networks with Cicero" vignette so I was hoping it was a similar easy fix that I am missing. Here is a look at those variables

str(conns) 'data.frame': 6658 obs. of 3 variables: $ Peak1 : chr "chr2_10010248_10010642" "chr2_10010248_10010642" "chr2_10010248_10010642" "chr2_10010248_10010642" ... $ Peak2 : Factor w/ 210 levels "chr2_3050002_3050515",..: 190 191 192 193 194 195 197 198 199 200 ... $ coaccess: num 0.095123 0.154584 0 0.306872 0.000782 ...

str(ccans) 'data.frame': 141 obs. of 2 variables: $ Peak: Factor w/ 149 levels "chr2_10010248_10010642",..: 1 2 3 4 5 6 7 8 9 10 ... $ CCAN: num 15 15 1 15 15 1 15 15 1 15 ...

and the object I was hoping to add it to for some of those beauty coverage plots (btw this is a different dataset than the one mentioned regarding the ClosestFeature function)

str(Efl) Formal class 'Seurat' [package "Seurat"] with 13 slots ..@ assays :List of 2 .. ..$ peaks:Formal class 'ChromatinAssay' [package "Signac"] with 16 slots .. .. .. ..@ ranges :Formal class 'GRanges' [package "GenomicRanges"] with 7 slots .. .. .. .. .. ..@ seqnames :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 21 levels "chr1","chr2",..: 1 2 3 4 5 6 7 8 9 10 ... .. .. .. .. .. .. .. ..@ lengths : int [1:21] 5053 5994 3968 4943 4601 4194 4632 4309 4206 4239 ... .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ ranges :Formal class 'IRanges' [package "IRanges"] with 6 slots .. .. .. .. .. .. .. ..@ start : int [1:76361] 4426717 4491538 4496151 4571437 4617679 4622381 4768374 4769843 4775004 4785133 ... .. .. .. .. .. .. .. ..@ width : int [1:76361] 592 2332 1635 795 77 477 429 463 628 1137 ... .. .. .. .. .. .. .. ..@ NAMES : NULL .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ strand :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 3 levels "+","-","": 3 .. .. .. .. .. .. .. ..@ lengths : int 76361 .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots .. .. .. .. .. .. .. ..@ seqnames : chr [1:21] "chr1" "chr2" "chr3" "chr4" ... .. .. .. .. .. .. .. ..@ seqlengths : int [1:21] NA NA NA NA NA NA NA NA NA NA ... .. .. .. .. .. .. .. ..@ is_circular: logi [1:21] NA NA NA NA NA NA ... .. .. .. .. .. .. .. ..@ genome : chr [1:21] NA NA NA NA ... .. .. .. .. .. ..@ elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots .. .. .. .. .. .. .. ..@ rownames : NULL .. .. .. .. .. .. .. ..@ nrows : int 76361 .. .. .. .. .. .. .. ..@ listData : Named list() .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. ..@ metadata : list() .. .. .. ..@ motifs : NULL .. .. .. ..@ fragments :List of 1 .. .. .. .. ..$ :Formal class 'Fragment' [package "Signac"] with 3 slots .. .. .. .. .. .. ..@ path : chr "/home/wilder/atactestfilesMarrow/fragments.tsv.gz" .. .. .. .. .. .. ..@ hash : chr [1:2] "a73f5cf7b40cadf584ad2a6db6df1f6b" "b8b21d2d45302a04608fdc558e0eedf2" .. .. .. .. .. .. ..@ cells: Named chr [1:1062] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. .. ..- attr(, "names")= chr [1:1062] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. .. .. ..- attr(, ".match.hash")=Class 'match.hash' .. .. .. .. .. .. .. ..- attr(, ".match.hash")=Class 'match.hash' .. .. .. ..@ seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots .. .. .. .. .. ..@ seqnames : chr [1:66] "chr1" "chr2" "chr3" "chr4" ... .. .. .. .. .. ..@ seqlengths : int [1:66] 195471971 182113224 160039680 156508116 151834684 149736546 145441459 129401213 124595110 130694993 ... .. .. .. .. .. ..@ is_circular: logi [1:66] FALSE FALSE FALSE FALSE FALSE FALSE ... .. .. .. .. .. ..@ genome : chr [1:66] "mm10" "mm10" "mm10" "mm10" ... .. .. .. ..@ annotation :Formal class 'GRanges' [package "GenomicRanges"] with 7 slots .. .. .. .. .. ..@ seqnames :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 22 levels "chr3","chrX",..: 1 2 3 4 5 6 7 8 9 10 ... .. .. .. .. .. .. .. ..@ lengths : int [1:22] 83099 68561 56688 128222 149106 89406 49376 121281 98969 10906 ... .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ ranges :Formal class 'IRanges' [package "IRanges"] with 6 slots .. .. .. .. .. .. .. ..@ start : int [1:1763965] 3508030 3634150 3638059 3638059 3641223 3641223 3642964 3642964 3644536 3644536 ... .. .. .. .. .. .. .. ..@ width : int [1:1763965] 303 198 172 172 95 95 107 107 156 156 ... .. .. .. .. .. .. .. ..@ NAMES : chr [1:1763965] "ENSMUSE00001236884" "ENSMUSE00000676606" "ENSMUSE00001345708" "ENSMUSE00001345708" ... .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ strand :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 3 levels "+","-","": 1 2 1 2 1 2 1 2 1 2 ... .. .. .. .. .. .. .. ..@ lengths : int [1:65507] 69 30 1 6 28 37 15 87 16 1 ... .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots .. .. .. .. .. .. .. ..@ seqnames : chr [1:22] "chr3" "chrX" "chr16" "chr7" ... .. .. .. .. .. .. .. ..@ seqlengths : int [1:22] 160039680 171031299 98207768 145441459 122082543 149736546 120421639 156508116 124595110 91744698 ... .. .. .. .. .. .. .. ..@ is_circular: logi [1:22] FALSE FALSE FALSE FALSE FALSE FALSE ... .. .. .. .. .. .. .. ..@ genome : chr [1:22] "mm10" "mm10" "mm10" "mm10" ... .. .. .. .. .. ..@ elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots .. .. .. .. .. .. .. ..@ rownames : NULL .. .. .. .. .. .. .. ..@ nrows : int 1763965 .. .. .. .. .. .. .. ..@ listData :List of 5 .. .. .. .. .. .. .. .. ..$ tx_id : chr [1:1763965] "ENSMUST00000108393" "ENSMUST00000108394" "ENSMUST00000108393" "ENSMUST00000108394" ... .. .. .. .. .. .. .. .. ..$ gene_name : chr [1:1763965] "Hnf4g" "Hnf4g" "Hnf4g" "Hnf4g" ... .. .. .. .. .. .. .. .. ..$ gene_id : chr [1:1763965] "ENSMUSG00000017688" "ENSMUSG00000017688" "ENSMUSG00000017688" "ENSMUSG00000017688" ... .. .. .. .. .. .. .. .. ..$ gene_biotype: chr [1:1763965] "protein_coding" "protein_coding" "protein_coding" "protein_coding" ... .. .. .. .. .. .. .. .. ..$ type : Factor w/ 4 levels "cds","exon","gap",..: 2 2 2 2 2 2 2 2 2 2 ... .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. ..@ metadata : list() .. .. .. ..@ bias : NULL .. .. .. ..@ positionEnrichment:List of 1 .. .. .. .. ..$ TSS:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. .. .. .. .. .. ..@ i : int [1:1455103] 0 3 5 6 9 10 11 12 14 15 ... .. .. .. .. .. .. ..@ p : int [1:2002] 0 520 1029 1591 2143 2705 3265 3836 4406 4980 ... .. .. .. .. .. .. ..@ Dim : int [1:2] 1064 2001 .. .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. .. ..$ : chr [1:1064] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. .. ..$ : chr [1:2001] "-999" "-998" "-997" "-996" ... .. .. .. .. .. .. ..@ x : num [1:1455103] 0.841 7.229 1.429 0.877 0.218 ... .. .. .. .. .. .. ..@ factors : list() .. .. .. ..@ links :Formal class 'GRanges' [package "GenomicRanges"] with 7 slots .. .. .. .. .. ..@ seqnames :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 0 levels: .. .. .. .. .. .. .. ..@ lengths : int(0) .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ ranges :Formal class 'IRanges' [package "IRanges"] with 6 slots .. .. .. .. .. .. .. ..@ start : int(0) .. .. .. .. .. .. .. ..@ width : int(0) .. .. .. .. .. .. .. ..@ NAMES : NULL .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ strand :Formal class 'Rle' [package "S4Vectors"] with 4 slots .. .. .. .. .. .. .. ..@ values : Factor w/ 3 levels "+","-","": .. .. .. .. .. .. .. ..@ lengths : int(0) .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ seqinfo :Formal class 'Seqinfo' [package "GenomeInfoDb"] with 4 slots .. .. .. .. .. .. .. ..@ seqnames : chr(0) .. .. .. .. .. .. .. ..@ seqlengths : int(0) .. .. .. .. .. .. .. ..@ iscircular: logi(0) .. .. .. .. .. .. .. ..@ genome : chr(0) .. .. .. .. .. ..@ elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots .. .. .. .. .. .. .. ..@ rownames : NULL .. .. .. .. .. .. .. ..@ nrows : int 0 .. .. .. .. .. .. .. ..@ listData : Named list() .. .. .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. .. .. ..@ elementMetadata: NULL .. .. .. .. .. .. .. ..@ metadata : list() .. .. .. .. .. ..@ elementType : chr "ANY" .. .. .. .. .. ..@ metadata : list() .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. .. .. .. .. ..@ i : int [1:7769570] 10 11 17 22 28 30 31 34 36 46 ... .. .. .. .. .. ..@ p : int [1:616] 0 19033 30407 45755 48936 52970 66870 94608 117573 130178 ... .. .. .. .. .. ..@ Dim : int [1:2] 76361 615 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:76361] "chr1-4426717-4427308" "chr1-4491538-4493869" "chr1-4496151-4497785" "chr1-4571437-4572231" ... .. .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. .. ..- attr(, ".match.hash")=Class 'match.hash' .. .. .. .. .. ..@ x : num [1:7769570] 2 4 6 14 4 4 1 10 6 4 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. .. .. .. .. ..@ i : int [1:7769570] 10 11 17 22 28 30 31 34 36 46 ... .. .. .. .. .. ..@ p : int [1:616] 0 19033 30407 45755 48936 52970 66870 94608 117573 130178 ... .. .. .. .. .. ..@ Dim : int [1:2] 76361 615 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:76361] "chr1-4426717-4427308" "chr1-4491538-4493869" "chr1-4496151-4497785" "chr1-4571437-4572231" ... .. .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. .. ..- attr(, ".match.hash")=Class 'match.hash' .. .. .. .. .. ..@ x : num [1:7769570] 0.157 0.167 0.313 0.301 0.154 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ scale.data : num[0 , 0 ] .. .. .. ..@ key : chr "peaks" .. .. .. ..@ assay.orig : NULL .. .. .. ..@ var.features : chr [1:76361] "chr17-39842785-39849049" "chr7-19273715-19311004" "chr7-28370237-28384187" "chr8-84196373-84220978" ... .. .. .. ..@ meta.features :'data.frame': 76361 obs. of 2 variables: .. .. .. .. ..$ count : num [1:76361] 137 583 364 338 13 123 91 82 84 809 ... .. .. .. .. ..$ percentile: num [1:76361] 0.5026 0.8264 0.7539 0.7401 0.0435 ... .. .. .. ..@ misc : NULL .. ..$ RNA :Formal class 'Assay' [package "Seurat"] with 8 slots .. .. .. ..@ counts :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. .. .. .. .. ..@ i : int [1:5416450] 1 2 5 6 7 10 11 12 13 14 ... .. .. .. .. .. ..@ p : int [1:616] 0 11702 19523 30625 33461 37978 48380 62095 75032 83543 ... .. .. .. .. .. ..@ Dim : int [1:2] 21978 615 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:21978] "Hnf4g" "Zfhx4" "Pex2" "UBC" ... .. .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. ..@ x : num [1:5416450] 8 6 1 4 1 2 2 2 8 6 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots .. .. .. .. .. ..@ i : int [1:5416450] 1 2 5 6 7 10 11 12 13 14 ... .. .. .. .. .. ..@ p : int [1:616] 0 11702 19523 30625 33461 37978 48380 62095 75032 83543 ... .. .. .. .. .. ..@ Dim : int [1:2] 21978 615 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:21978] "Hnf4g" "Zfhx4" "Pex2" "UBC" ... .. .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. ..@ x : num [1:5416450] 1.74 1.509 0.462 1.208 0.462 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ scale.data : num[0 , 0 ] .. .. .. ..@ key : chr "rna_" .. .. .. ..@ assay.orig : NULL .. .. .. ..@ var.features : logi(0) .. .. .. ..@ meta.features:'data.frame': 21978 obs. of 0 variables .. .. .. ..@ misc : NULL ..@ meta.data :'data.frame': 615 obs. of 32 variables: .. ..$ orig.ident : Factor w/ 1 level "ATAC": 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nCount_peaks : num [1:615] 71384 31848 63329 6953 11358 ... .. ..$ nFeature_peaks : int [1:615] 19033 11374 15348 3181 4034 13900 27738 22965 12605 15755 ... .. ..$ total : int [1:615] 171516 102223 223814 12110 24882 130465 371059 249933 106391 170979 ... .. ..$ duplicate : int [1:615] 105957 63610 154491 4601 13045 77208 240052 158621 64129 108047 ... .. ..$ chimeric : int [1:615] 2279 1178 4315 96 436 2607 5744 3367 1513 3050 ... .. ..$ unmapped : int [1:615] 4759 2478 7458 275 949 4249 11488 6256 3322 5356 ... .. ..$ lowmapq : int [1:615] 6965 6452 10939 831 2087 9376 17787 10545 6960 8515 ... .. ..$ mitochondrial : int [1:615] 661 62 113 125 161 12 384 0 448 11 ... .. ..$ passed_filters : int [1:615] 50895 28443 46498 6182 8204 37013 95604 71144 30019 46000 ... .. ..$ cell_id : Factor w/ 1063 levels "_cell_0","_cell_1",..: 1 2 175 286 508 619 952 3 76 87 ... .. ..$ is__cell_barcode : int [1:615] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ TSS_fragments : int [1:615] 24069 9209 23494 1845 4953 19694 38737 32016 10624 22136 ... .. ..$ DNase_sensitive_region_fragments: int [1:615] 34142 16176 28156 3517 4668 23634 59039 44360 17726 29411 ... .. ..$ enhancer_region_fragments : int [1:615] 15208 8536 11994 1965 1116 10067 29714 20905 9000 12426 ... .. ..$ promoter_region_fragments : int [1:615] 22640 7990 22103 1647 4830 18700 35358 29624 9533 21124 ... .. ..$ on_target_fragments : int [1:615] 43641 20471 38719 4408 6435 32046 77125 58814 22370 38184 ... .. ..$ blacklist_region_fragments : int [1:615] 59 36 129 6 20 88 342 56 45 60 ... .. ..$ peak_region_fragments : int [1:615] 36901 16630 32466 3620 5763 27086 64373 49854 18886 31357 ... .. ..$ peak_region_cutsites : int [1:615] 71384 31848 63329 6953 11358 52873 123594 95455 36486 60845 ... .. ..$ nucleosome_signal : num [1:615] 0.588 0.968 0.469 1.088 0.433 ... .. ..$ nucleosome_percentile : num [1:615] 0.57 0.91 0.25 0.94 0.17 0.11 0.62 0.71 0.73 0.49 ... .. ..$ nucleosome_group : chr [1:615] "NS < 4" "NS < 4" "NS < 4" "NS < 4" ... .. ..$ TSS.enrichment : num [1:615] 5.07 4.99 6.07 5.03 11.37 ... .. ..$ TSS.percentile : num [1:615] 0.38 0.36 0.63 0.37 0.97 0.53 0.34 0.21 0.64 0.76 ... .. ..$ high.tss : chr [1:615] "High" "High" "High" "High" ... .. ..$ pct_reads_in_peaks : num [1:615] 72.5 58.5 69.8 58.6 70.2 ... .. ..$ blacklist_ratio : num [1:615] 0.0016 0.00216 0.00397 0.00166 0.00347 ... .. ..$ peaks_snn_res.1.2 : Factor w/ 9 levels "0","1","2","3",..: 3 4 5 4 1 6 2 2 4 7 ... .. ..$ seurat_clusters : Factor w/ 9 levels "0","1","2","3",..: 3 4 5 4 1 6 2 2 4 7 ... .. ..$ nCount_RNA : num [1:615] 41995 19200 39164 4045 7235 ... .. ..$ nFeature_RNA : int [1:615] 11702 7821 11102 2836 4517 10402 13715 12937 8511 11103 ... ..@ active.assay: chr "peaks" ..@ active.ident: Factor w/ 5 levels "BLOOD","CAR",..: 4 2 3 2 3 3 2 2 2 5 ... ..@ graphs :List of 2 .. ..$ peaks_nn :Formal class 'Graph' [package "Seurat"] with 7 slots .. .. .. ..@ assay.used: chr "peaks" .. .. .. ..@ i : int [1:12300] 0 13 30 46 76 105 112 143 150 151 ... .. .. .. ..@ p : int [1:616] 0 39 42 70 80 102 139 164 229 235 ... .. .. .. ..@ Dim : int [1:2] 615 615 .. .. .. ..@ Dimnames :List of 2 .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. ..@ x : num [1:12300] 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..@ factors : list() .. ..$ peaks_snn:Formal class 'Graph' [package "Seurat"] with 7 slots .. .. .. ..@ assay.used: chr "peaks" .. .. .. ..@ i : int [1:40009] 0 13 22 24 30 46 76 84 95 103 ... .. .. .. ..@ p : int [1:616] 0 72 120 178 264 321 380 471 579 625 ... .. .. .. ..@ Dim : int [1:2] 615 615 .. .. .. ..@ Dimnames :List of 2 .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. ..@ x : num [1:40009] 1 0.6667 0.3333 0.0811 0.4815 ... .. .. .. ..@ factors : list() ..@ neighbors : list() ..@ reductions :List of 2 .. ..$ lsi :Formal class 'DimReduc' [package "Seurat"] with 9 slots .. .. .. ..@ cell.embeddings : num [1:615, 1:50] 0.877 0.634 0.177 -1.387 -1.907 ... .. .. .. .. ..- attr(, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. .. ..- attr(, ".match.hash")=Class 'match.hash' .. .. .. .. .. ..$ : chr [1:50] "LSI_1" "LSI_2" "LSI_3" "LSI_4" ... .. .. .. ..@ feature.loadings : num [1:76361, 1:50] 0.00536 0.00587 0.00629 0.00609 0.00605 ... .. .. .. .. ..- attr(, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:76361] "chr17-39842785-39849049" "chr7-19273715-19311004" "chr7-28370237-28384187" "chr8-84196373-84220978" ... .. .. .. .. .. ..$ : chr [1:50] "LSI_1" "LSI_2" "LSI_3" "LSI4" ... .. .. .. ..@ feature.loadings.projected: num[0 , 0 ] .. .. .. ..@ assay.used : chr "peaks" .. .. .. ..@ global : logi FALSE .. .. .. ..@ stdev : num [1:50] 2.819 1.453 0.815 0.717 0.647 ... .. .. .. ..@ key : chr "LSI" .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "Seurat"] with 4 slots .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ] .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ] .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ] .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ] .. .. .. ..@ misc :List of 5 .. .. .. .. ..$ d : num [1:50] 779 402 225 198 179 ... .. .. .. .. ..$ u : num [1:615, 1:50] 0.0475 0.0452 0.0409 0.0261 0.0212 ... .. .. .. .. ..$ v : num [1:76361, 1:50] 0.00536 0.00587 0.00629 0.00609 0.00605 ... .. .. .. .. ..$ iter : int 3 .. .. .. .. ..$ mprod: int 626 .. ..$ umap:Formal class 'DimReduc' [package "Seurat"] with 9 slots .. .. .. ..@ cell.embeddings : num [1:615, 1:2] -3.373 -0.364 0.194 -0.452 3.668 ... .. .. .. .. ..- attr(, "scaled:center")= num [1:2] 1.6 3.24 .. .. .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. .. .. ..$ : chr [1:615] "AAACGAAGTACGCAAG-1" "AAACGAATCCGTGCAG-1" "AAACTCGAGAATATCG-1" "AAACTCGAGGTTGTTC-1" ... .. .. .. .. .. ..$ : chr [1:2] "UMAP_1" "UMAP2" .. .. .. ..@ feature.loadings : num[0 , 0 ] .. .. .. ..@ feature.loadings.projected: num[0 , 0 ] .. .. .. ..@ assay.used : chr "peaks" .. .. .. ..@ global : logi TRUE .. .. .. ..@ stdev : num(0) .. .. .. ..@ key : chr "UMAP" .. .. .. ..@ jackstraw :Formal class 'JackStrawData' [package "Seurat"] with 4 slots .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ] .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ] .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ] .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ] .. .. .. ..@ misc : list() ..@ images : list() ..@ project.name: chr "ATAC" ..@ misc : list() ..@ version :Classes 'package_version', 'numericversion' hidden list of 1 .. ..$ : int [1:3] 3 2 1 ..@ commands :List of 4 .. ..$ RunUMAP.peaks.lsi :Formal class 'SeuratCommand' [package "Seurat"] with 5 slots .. .. .. ..@ name : chr "RunUMAP.peaks.lsi" .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2021-01-09 18:19:05" .. .. .. ..@ assay.used : chr "peaks" .. .. .. ..@ call.string: chr "RunUMAP(object = Efl, reduction = \"lsi\", dims = 2:30)" .. .. .. ..@ params :List of 22 .. .. .. .. ..$ dims : int [1:29] 2 3 4 5 6 7 8 9 10 11 ... .. .. .. .. ..$ reduction : chr "lsi" .. .. .. .. ..$ assay : chr "peaks" .. .. .. .. ..$ slot : chr "data" .. .. .. .. ..$ umap.method : chr "uwot" .. .. .. .. ..$ return.model : logi FALSE .. .. .. .. ..$ n.neighbors : int 30 .. .. .. .. ..$ n.components : int 2 .. .. .. .. ..$ metric : chr "cosine" .. .. .. .. ..$ learning.rate : num 1 .. .. .. .. ..$ min.dist : num 0.3 .. .. .. .. ..$ spread : num 1 .. .. .. .. ..$ set.op.mix.ratio : num 1 .. .. .. .. ..$ local.connectivity : int 1 .. .. .. .. ..$ repulsion.strength : num 1 .. .. .. .. ..$ negative.sample.rate: int 5 .. .. .. .. ..$ uwot.sgd : logi FALSE .. .. .. .. ..$ seed.use : int 42 .. .. .. .. ..$ angular.rp.forest : logi FALSE .. .. .. .. ..$ verbose : logi TRUE .. .. .. .. ..$ reduction.name : chr "umap" .. .. .. .. ..$ reduction.key : chr "UMAP" .. ..$ FindNeighbors.peaks.lsi:Formal class 'SeuratCommand' [package "Seurat"] with 5 slots .. .. .. ..@ name : chr "FindNeighbors.peaks.lsi" .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2021-01-09 18:19:07" .. .. .. ..@ assay.used : chr "peaks" .. .. .. ..@ call.string: chr "FindNeighbors(object = Efl, reduction = \"lsi\", dims = 2:30)" .. .. .. ..@ params :List of 13 .. .. .. .. ..$ reduction : chr "lsi" .. .. .. .. ..$ dims : int [1:29] 2 3 4 5 6 7 8 9 10 11 ... .. .. .. .. ..$ assay : chr "peaks" .. .. .. .. ..$ k.param : num 20 .. .. .. .. ..$ compute.SNN : logi TRUE .. .. .. .. ..$ prune.SNN : num 0.0667 .. .. .. .. ..$ nn.method : chr "rann" .. .. .. .. ..$ annoy.metric: chr "euclidean" .. .. .. .. ..$ nn.eps : num 0 .. .. .. .. ..$ verbose : logi TRUE .. .. .. .. ..$ force.recalc: logi FALSE .. .. .. .. ..$ do.plot : logi FALSE .. .. .. .. ..$ graph.name : chr [1:2] "peaks_nn" "peaks_snn" .. ..$ FindClusters :Formal class 'SeuratCommand' [package "Seurat"] with 5 slots .. .. .. ..@ name : chr "FindClusters" .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2021-01-09 18:19:11" .. .. .. ..@ assay.used : chr "peaks" .. .. .. ..@ call.string: chr [1:2] "FindClusters(object = Efl, algorithm = 3, resolution = 1.2, " " verbose = FALSE)" .. .. .. ..@ params :List of 10 .. .. .. .. ..$ graph.name : chr "peaks_snn" .. .. .. .. ..$ modularity.fxn : num 1 .. .. .. .. ..$ resolution : num 1.2 .. .. .. .. ..$ method : chr "matrix" .. .. .. .. ..$ algorithm : num 3 .. .. .. .. ..$ n.start : num 10 .. .. .. .. ..$ n.iter : num 10 .. .. .. .. ..$ random.seed : num 0 .. .. .. .. ..$ group.singletons: logi TRUE .. .. .. .. ..$ verbose : logi FALSE .. ..$ NormalizeData.RNA :Formal class 'SeuratCommand' [package "Seurat"] with 5 slots .. .. .. ..@ name : chr "NormalizeData.RNA" .. .. .. ..@ time.stamp : POSIXct[1:1], format: "2021-01-09 18:20:54" .. .. .. ..@ assay.used : chr "RNA" .. .. .. ..@ call.string: chr [1:2] "NormalizeData(object = Efl, assay = \"RNA\", normalization.method = \"LogNormalize\", " " scale.factor = median(Efl$nCount_RNA))" .. .. .. ..@ params :List of 5 .. .. .. .. ..$ assay : chr "RNA" .. .. .. .. ..$ normalization.method: chr "LogNormalize" .. .. .. .. ..$ scale.factor : num 24647 .. .. .. .. ..$ margin : num 2 .. .. .. .. ..$ verbose : logi TRUE ..@ tools : list()

My guess was it had something to do with the annotation of the object but I have not been able to get to the bottom of it yet.

One more thing, when I run without the ccans i get a different error:

links <- ConnectionsToLinks(conns = conns) Error in $<-.data.frame(*tmp*, "group", value = NA) : replacement has 1 row, data has 0

Any clues or insights would be very much appreciated.

Thanks so much as always. W

sessionInfo() R version 3.6.3 (2020-02-29) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.3 LTS ... other attached packages: [1] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.8.1 AnnotationFilter_1.8.0 GenomicFeatures_1.36.4 AnnotationDbi_1.46.1 Biobase_2.44.0 ggplot2_3.3.1
[8] future_1.15.1 patchwork_1.0.0 BSgenome.Mmusculus.UCSC.mm10_1.4.0 BSgenome_1.52.0 rtracklayer_1.44.4 Biostrings_2.52.0 XVector_0.24.0
[15] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.3 S4Vectors_0.22.1 BiocGenerics_0.30.0 TFBSTools_1.22.0 JASPAR2018_1.1.1
[22] Seurat_3.2.1 Signac_1.1.0

ps let me know if there is anything you would need to see.

widsquid commented 3 years ago

Perhaps this would help. This is pertaining to the second part of my question. I am starting to wonder if it is related to line 2 of the traceback "-" vs "_"

links <- ConnectionsToLinks(conns = conns, ccans = ccans) Error in validObject(.Object) : invalid class "GRanges" object: 'x@strand' is not parallel to 'x' traceback() 10: stop(msg, ": ", errors, domain = NA) 9: validObject(.Object) 8: initialize(value, ...) 7: initialize(value, ...) 6: new(Class, seqnames = seqnames, ranges = ranges, strand = strand, elementMetadata = mcols, seqinfo = seqinfo) 5: new_GRanges("GRanges", seqnames = seqnames, ranges = ranges, strand = strand, mcols = mcols, seqlengths = seqlengths, seqinfo = seqinfo) 4: GRanges(ans_seqnames, ans_ranges, strand = ans_strand, ans_mcols, seqinfo = ans_seqinfo) 3: makeGRangesFromDataFrame(df = ranges.df, ...) 2: StringToGRanges(regions = conns$Peak1, sep = c("-", "-")) 1: ConnectionsToLinks(conns = conns, ccans = ccans)

sessionInfo() R version 3.6.3 (2020-02-29) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 18.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

Random number generation: RNG: Mersenne-Twister Normal: Inversion Sample: Rounding

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 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

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

other attached packages: [1] future_1.15.1 svglite_1.2.2 patchwork_1.0.0 ggplot2_3.3.1 Matrix_1.2-18 monocle3_0.2.0 SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.1 [9] DelayedArray_0.10.0 BiocParallel_1.18.1 matrixStats_0.56.0 GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.3 S4Vectors_0.22.1 Biobase_2.44.0
[17] BiocGenerics_0.30.0 SeuratWrappers_0.3.0 Seurat_3.2.1 Signac_1.1.0

loaded via a namespace (and not attached): [1] reticulate_1.13 tidyselect_1.1.0 RSQLite_2.1.2 AnnotationDbi_1.46.1 htmlwidgets_1.5.1 grid_3.6.3 Rtsne_0.15 munsell_0.5.0 codetools_0.2-16
[10] ica_1.0-2 miniUI_0.1.1.1 withr_2.1.2 colorspace_1.4-1 OrganismDbi_1.26.0 knitr_1.26 rstudioapi_0.11 ROCR_1.0-7 tensor_1.5
[19] listenv_0.7.0 GenomeInfoDbData_1.2.1 polyclip_1.10-0 bit64_0.9-7 farver_2.0.3 vctrs_0.3.4 generics_0.0.2 xfun_0.11 biovizBase_1.32.0
[28] lsa_0.73.2 ggseqlogo_0.1 R6_2.4.1 rsvd_1.0.3 AnnotationFilter_1.8.0 bitops_1.0-6 spatstat.utils_1.17-0 reshape_0.8.8 promises_1.1.0
[37] scales_1.1.0 nnet_7.3-12 gtable_0.3.0 npsurv_0.4-0 globals_0.12.4 goftest_1.2-2 ggbio_1.32.0 ensembldb_2.8.1 rlang_0.4.7
[46] systemfonts_0.1.1 RcppRoll_0.3.0 splines_3.6.3 rtracklayer_1.44.4 lazyeval_0.2.2 acepack_1.4.1 dichromat_2.0-0 checkmate_1.9.4 BiocManager_1.30.10
[55] reshape2_1.4.3 abind_1.4-5 GenomicFeatures_1.36.4 backports_1.1.5 httpuv_1.5.2 Hmisc_4.3-0 RBGL_1.60.0 tools_3.6.3 gplots_3.0.3
[64] RColorBrewer_1.1-2 ggridges_0.5.1 Rcpp_1.0.3 plyr_1.8.4 base64enc_0.1-3 progress_1.2.2 zlibbioc_1.30.0 purrr_0.3.3 RCurl_1.98-1.1
[73] prettyunits_1.1.1 rpart_4.1-15 deldir_0.1-23 viridis_0.5.1 pbapply_1.4-2 cowplot_1.0.0 zoo_1.8-6 ggrepel_0.8.1 cluster_2.1.0
[82] magrittr_1.5 data.table_1.12.8 lmtest_0.9-37 RANN_2.6.1 SnowballC_0.7.0 ProtGenerics_1.16.0 fitdistrplus_1.0-14 hms_0.5.2 lsei_1.2-0
[91] mime_0.7 xtable_1.8-4 XML_3.98-1.20 gridExtra_2.3 compiler_3.6.3 biomaRt_2.40.5 tibble_2.1.3 KernSmooth_2.23-16 crayon_1.3.4
[100] htmltools_0.4.0 mgcv_1.8-31 later_1.0.0 Formula_1.2-3 tidyr_1.0.0 DBI_1.0.0 tweenr_1.0.1 MASS_7.3-51.5 gdata_2.18.0
[109] igraph_1.2.4.2 pkgconfig_2.0.3 GenomicAlignments_1.20.1 foreign_0.8-74 plotly_4.9.1 XVector_0.24.0 stringr_1.4.0 VariantAnnotation_1.30.1 digest_0.6.25
[118] sctransform_0.2.0 RcppAnnoy_0.0.16 graph_1.62.0 spatstat.data_1.4-3 Biostrings_2.52.0 leiden_0.3.1 fastmatch_1.1-0 htmlTable_1.13.2 uwot_0.1.8
[127] gdtools_0.2.1 curl_4.2 shiny_1.4.0 Rsamtools_2.0.3 gtools_3.8.1 lifecycle_0.2.0 nlme_3.1-143 jsonlite_1.6 viridisLite_0.3.0
[136] BSgenome_1.52.0 pillar_1.4.3 lattice_0.20-38 GGally_1.4.0 fastmap_1.0.1 httr_1.4.1 survival_3.1-8 glue_1.3.2 remotes_2.1.0
[145] spatstat_1.64-1 png_0.1-7 bit_1.1-14 ggforce_0.3.1 stringi_1.4.6 blob_1.2.0 latticeExtra_0.6-28 caTools_1.18.0 memoise_1.1.0
[154] dplyr_1.0.2 irlba_2.3.3 future.apply_1.3.0

widsquid commented 3 years ago

forgive me one more thing:

Above is when I run ConnectionsToLinks after creating the cds by inputing the 10x data into monocle3 however if I convert to cds using the vignette I run into the following snag. Any idea why I am introducing NAs here? I assume that is what causes the final error.

load("atactestfilesMarrow/testOBJ.Rdata") #(the same obj shown above)

ls() [1] "Efl" Efl_cds <- as.cell_data_set(Efl) Efl_cds <- cluster_cells(cds = Efl_cds, reduction_method = "UMAP") Efl_cds <- learn_graph(Efl_cds, use_partition = TRUE) Efl_cds <- order_cells(Efl_cds, reduction_method = "UMAP", root_cells = "TCACAAGCAACGACAG-1") plot_cells(cds = Efl_cds, color_cells_by = "pseudotime", show_trajectory_graph = TRUE) library(cicero) Loading required package: Gviz Loading required package: grid Efl_cicero <- make_cicero_cds(Efl_cds, reduced_coordinates = reducedDims(Efl_cds)$UMAP) Overlap QC metrics: Cells per bin: 50 Maximum shared cells bin-bin: 44 Mean shared cells bin-bin: 5.53208749818919 Median shared cells bin-bin: 0 Warning messages: 1: In make_cicero_cds(Efl_cds, reduced_coordinates = reducedDims(Efl_cds)$UMAP) : On average, more than 10% of cells are shared between paired bins. 2: In df_for_coords(row.names(fData(cicero_cds))) : NAs introduced by coercion 3: In df_for_coords(row.names(fData(cicero_cds))) : NAs introduced by coercion genome <- seqlengths(Efl) Error in slot(object = x[[assay]], name = "seqinfo") : no slot of name "seqinfo" for this object of class "Assay" DefaultAssay(Efl) <- 'peaks' genome <- seqlengths(Efl) genome <- genome[1] genome.df <- data.frame("chr" = names(genome), "length" = genome) genome.df chr length chr1 chr1 195471971 conns <- run_cicero(Efl_cicero, genomic_coords = genome.df, sample_num = 2) [1] "Starting Cicero" [1] "Calculating distance_parameter value" Error: logical subscript contains NAs

Thanks again!

widsquid commented 3 years ago

Ok I think I found an answer to the second issue ConnectionsToLinks ("-" vs "_")

head(conns) Peak1 Peak2 coaccess 1 chr2_10010248_10010642 chr2_9867056_9867863 0.0951225084 2 chr2_10010248_10010642 chr2_9876787_9880109 0.1545838931 3 chr2_10010248_10010642 chr2_9881168_9881244 0.0000000000 4 chr2_10010248_10010642 chr2_9881908_9884600 0.3068717284 5 chr2_10010248_10010642 chr2_9887745_9887903 0.0007816135 6 chr2_10010248_10010642 chr2_9889122_9889862 0.0243885814

links <- ConnectionsToLinks(conns = conns, ccans = ccans) Error in validObject(.Object) : invalid class "GRanges" object: 'x@strand' is not parallel to 'x'

so test the line 2

test <- StringToGRanges(regions = conns$Peak1, sep = c("-", "-")) Error in .get_data_frame_col_as_numeric(df, granges_cols[["start"]]) : some values in the "start" column cannot be turned into numeric values In addition: Warning message: Expected 3 pieces. Missing pieces filled with NA in 6658 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

However,

test <- StringToGRanges(regions = conns$Peak1, sep = c("", ""))

head(test) GRanges object with 6 ranges and 0 metadata columns: seqnames ranges strand

[1] chr2 10010248-10010642 * [2] chr2 10010248-10010642 * [3] chr2 10010248-10010642 * [4] chr2 10010248-10010642 * [5] chr2 10010248-10010642 * [6] chr2 10010248-10010642 *

or

conns Peak1 Peak2 coaccess 1 chr2-10010248-10010642 chr2-9867056-9867863 0.0951225084 2 chr2-10010248-10010642 chr2-9876787-9880109 0.1545838931 3 chr2-10010248-10010642 chr2-9881168-9881244 0.0000000000 4 chr2-10010248-10010642 chr2-9881908-9884600 0.3068717284 5 chr2-10010248-10010642 chr2-9887745-9887903 0.0007816135 6 chr2-10010248-10010642 chr2-9889122-9889862 0.0243885814

links <- ConnectionsToLinks(conns = conns, ccans = ccans) links GRanges object with 5 ranges and 2 metadata columns: seqnames ranges strand | score group

| [1] chr2 9867460-10010445 * | 0.0951225084 15 [2] chr2 9878448-10010445 * | 0.1545838931 15 [3] chr2 9883254-10010445 * | 0.3068717284 15 [4] chr2 9887824-10010445 * | 0.0007816135 15 [5] chr2 9889492-10010445 * | 0.0243885814 15 ------- seqinfo: 1 sequence from an unspecified genome; no seqlengths

seems solid yes?

Links(Efl) <- links

W

widsquid commented 3 years ago

** So to sum everything up: avg_log2FC vs avg_logFC when subsetting dapeaks for the function ClosestFeature - resolved "-" vs "" in the ConnectionsToLinks function - resolved

Just still wondering why the NAs are introduced when converting Seurat to Cicero Objs using make_cicero_cds and seemingly causes run_cicero to fail (this happens with both the test and large datasets)

Thanks so much. W**

timoast commented 3 years ago

If you're still having issues, please open a new issue with a concise description of the problem, all relevant code, and the output of sessionInfo()

widsquid commented 3 years ago

Ok thanks I will and sorry this one turned in to a bit of a mess. W