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LinkPeaks() function not working. Unable to make coverage plots #1902

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zgb963 opened this issue Feb 20, 2025 · 0 comments
Open

LinkPeaks() function not working. Unable to make coverage plots #1902

zgb963 opened this issue Feb 20, 2025 · 0 comments
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@zgb963
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zgb963 commented Feb 20, 2025 •
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Hi @timoast I created a new issue with the simplified code I ran up until the error I get with `LinkPeaks()'. I followed Joint RNA and ATAC analysis: 10x multiomic tutorial to process output from the 10X Genomics Cellranger ARC multiomics pipeline. I'm unable to link peaks to genes in order to create coverage plots. Everything works up until that point. Any insight on how to fix this would be appreciated, thanks.

STEP 1 read in count matrices & atac fragments path for sample 1MI5

gc_liftoff_1MI5_matrix <- Read10X_h5("~/macaque_multiomics/cr-arc-1_MI5_V1_240716_macfas6liftoff_hg38gencodev47_basic/outs/filtered_feature_bc_matrix.h5")
gc_liftoff_1MI5_fragpath <- "~/macaque_multiomics/cr-arc-1_MI5_V1_240716_macfas6liftoff_hg38gencodev47_basic/outs/atac_fragments.tsv.gz"

STEP 2 create Seurat object

# create a Seurat object containing the RNA adata
gc_liftoff_1MI5_SO <- CreateSeuratObject(
  counts = gc_liftoff_1MI5_matrix$`Gene Expression`,
  assay = "RNA"
)
 
# create ATAC assay and add it to the object
gc_liftoff_1MI5_SO[["ATAC"]] <- CreateChromatinAssay(
  counts = gc_liftoff_1MI5_matrix$Peaks,
  sep = c(":", "-"),
  fragments = gc_liftoff_1MI5_fragpath
)

STEP 3 add macfas6 gencodev47 liftoff gtf to seurat object so TSS.enrichment score can be calculated
Mapped multiomics reads (fastq) with 10x cellranger arc using the following fasta & gtf
input_fasta: ["mac_ref/GCA_011100615.1_Macaca_fascicularis_6.0_genomic.fna"]
input_gtf: ["mac_ref/macFas6-hg38-gencode.v47.basic.sorted.gtf"]. Gtf made with liftoff software. More details here.

IMPORTANT: chromosomes in liftoff gtf aren't listed as 'chr1', 'chr2', 'chr3' in UCSC style or '1', '2', '3' in NCBI style. They are listed as genbank seq accession numbers 'CM021939.1' for chrom 1, 'CM021940.1' for chrom 2, 'CM021941.1' for chrom 3, etc (has chromosomes 1-20, chromosome X, then the rest are 'Un' chromosomes)

# add liftoff annotation to object
# Step 3a: Load the GTF file
gencode_gtf_path <- '~/macaque_multiomics/mac_ref/macFas6-hg38-gencode.v47.basic.sorted.gtf'
gencode_gtf <- rtracklayer::import(gencode_gtf_path)
 
# Step 3b: Map gene_type to gene_biotype
# Check if gene_type exists, and then map it to gene_biotype
if ("gene_type" %in% colnames(mcols(gencode_gtf))) {
  mcols(gencode_gtf)$gene_biotype <- NA
  mcols(gencode_gtf)$gene_biotype[mcols(gencode_gtf)$gene_type == "protein_coding"] <- "protein_coding"
} else {
  stop("The GTF file does not contain a gene_type field.")
}
 
# did this because TSSenrichment function requires gene_biotype info. In liftoff gtf attribute listed as gene_type
# i.e gene_type "protein_coding", gene_type "lncRNA", etc
 
# Step 3c: Filter out entries with missing or invalid data (filtered out all NA's that were in the liftoff gtf GRanges object before calculating TSS score)
gencode_gtf <- gencode_gtf[
  !is.na(mcols(gencode_gtf)$gene_biotype) &
  !is.na(seqnames(gencode_gtf)) &
  !is.na(start(gencode_gtf)) &
  !is.na(end(gencode_gtf)) &
  !is.na(strand(gencode_gtf))
]
 
# Step 3d: Make default assay ATAC
DefaultAssay(gc_liftoff_1MI5_SO) <- "ATAC"
 
# Step 3e: Assign the gencode_gtf to the Seurat object
Annotation(gc_liftoff_1MI5_SO) <- gencode_gtf
 
# Step 3f: Run TSSEnrichment() function
gc_liftoff_1MI5_SO <- TSSEnrichment(gc_liftoff_1MI5_SO)

STEP 4 calculate nucleosome signal

gc_liftoff_1MI5_SO <- NucleosomeSignal(gc_liftoff_1MI5_SO)

STEP 5 Make QC violin plots

VlnPlot(
  object = gc_liftoff_1MI5_SO,
  features = c("nCount_RNA", "nCount_ATAC", "TSS.enrichment", "nucleosome_signal"),
  ncol = 4,
  pt.size = 0
)

STEP 6 make density scatter plot

DensityScatter(gc_liftoff_1MI5_SO, x = 'nCount_ATAC', y = 'TSS.enrichment', log_x = TRUE, quantiles = TRUE)

STEP 7 subset/filter based on tutorial cutoffs

# filter out low quality cells
gc_liftoff_1MI5_SO <- subset(
  x = gc_liftoff_1MI5_SO,
  subset = nCount_ATAC < 100000 &
	nCount_RNA < 25000 &
	nCount_ATAC > 1800 &
	nCount_RNA > 1000 &
	nucleosome_signal < 2 &
	TSS.enrichment > 1
)

STEP 8 normalize the gene expression data using SCTransform, and reduce the dimensionality using PCA

DefaultAssay(gc_liftoff_1MI5_SO) <- "RNA"
gc_liftoff_1MI5_SO <- SCTransform(gc_liftoff_1MI5_SO)
gc_liftoff_1MI5_SO <- RunPCA(gc_liftoff_1MI5_SO)

STEP 9 process the DNA accessibility assay the same way we would process a scATAC-seq dataset, by performing latent semantic indexing (LSI)

DefaultAssay(gc_liftoff_1MI5_SO) <- "ATAC"
gc_liftoff_1MI5_SO <- FindTopFeatures(gc_liftoff_1MI5_SO, min.cutoff = 5)
gc_liftoff_1MI5_SO <- RunTFIDF(gc_liftoff_1MI5_SO)
gc_liftoff_1MI5_SO <- RunSVD(gc_liftoff_1MI5_SO)

STEP 10 make snRNAseq, snATACseq, & snRNAseq + ATACseq UMAPs

# snRNAseq UMAP
gc_liftoff_1MI5_SO <- FindNeighbors(gc_liftoff_1MI5_SO, dims = 1:30)
gc_liftoff_1MI5_SO <- FindClusters(gc_liftoff_1MI5_SO, graph.name = "SCT_snn", resolution = 0.4)
gc_liftoff_1MI5_SO <- RunUMAP(gc_liftoff_1MI5_SO, dims = 1:30)
DimPlot(gc_liftoff_1MI5_SO, reduction = "umap", group.by = "SCT_snn_res.0.4", label = TRUE) + ggtitle("1MI5 snRNAseq res.0.4")
 
# snATACseq UMAP
gc_liftoff_1MI5_SO <- RunUMAP(gc_liftoff_1MI5_SO, reduction = 'lsi', dims = 2:30, reduction.name = "umap.atac", reduction.key = "atacUMAP_")
DimPlot(gc_liftoff_1MI5_SO, reduction = "umap.atac", label = TRUE, label.size = 2.5, repel = TRUE) + ggtitle("1MI5 snATAC res.0.4")
 
# snRNAseq + snATACseq UMAP
gc_liftoff_1MI5_SO <- FindMultiModalNeighbors(gc_liftoff_1MI5_SO, reduction.list = list("pca", "lsi"), dims.list = list(1:30, 2:30))
gc_liftoff_1MI5_SO <- RunUMAP(gc_liftoff_1MI5_SO, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_")
gc_liftoff_1MI5_SO <- FindClusters(gc_liftoff_1MI5_SO, graph.name = "wsnn", algorithm = 3, verbose = FALSE)
DimPlot(gc_liftoff_1MI5_SO, reduction = "wnn.umap", label = TRUE, label.size = 2.5, repel = TRUE) + ggtitle("1MI5 snRNA + snATAC res.0.4")

STEP 11 compute GC content for each peak using fasta instead of Bsgenome object

DefaultAssay(gc_liftoff_1MI5_SO) <- "ATAC"
macfas6_fasta_path <- "~/macaque_multiomics/mac_ref/GCA_011100615.1_Macaca_fascicularis_6.0_genomic.fna"
macfas6_fasta_file <- FaFile(macfas6_fasta_path)  # Create FaFile object
 
# compute the GC content for each peak (works, no warnings or errors)
gc_liftoff_1MI5_SO <- RegionStats(gc_liftoff_1MI5_SO, genome = macfas6_fasta_file)

STEP 12 Link peaks to genes (where I'm getting error)

# link peaks to genes
 gc_liftoff_1MI5_SO <- LinkPeaks(
   object = gc_liftoff_1MI5_SO,
   peak.assay = "ATAC",
   expression.assay = "SCT",
   genes.use = c("LYZ", "MS4A1")
 )
 
Testing 2 genes and 206443 peaks
  |                                                  | 0 % ~calculating  Warning: Requested more features than present in supplied data.
        	Returning 0 featuresError in density.default(x = query.feature[[featmatch]], kernel = "gaussian",  :
  argument 'x' must be numeric

R session info

devtools::session_info()

─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.0 (2024-04-24)
 os       Ubuntu 24.04.1 LTS
 system   x86_64, linux-gnu
 ui       RStudio
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       /UTC
 date     2025-02-06
 rstudio  2024.09.0+375 Cranberry Hibiscus (server)
 pandoc   3.2 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/x86_64/ (via rmarkdown)

─ Packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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 scales                            1.3.0      2023-11-28 [2] CRAN (R 4.4.0)
 scattermore                       1.2        2023-06-12 [2] CRAN (R 4.4.0)
 sctransform                       0.4.1      2023-10-19 [2] CRAN (R 4.4.0)
 sessioninfo                       1.2.2      2021-12-06 [2] CRAN (R 4.4.0)
 Seurat                          * 5.1.0      2024-05-10 [2] CRAN (R 4.4.0)
 SeuratDisk                      * 0.0.0.9021 2024-10-07 [1] Github (mojaveazure/seurat-disk@877d4e1)
 SeuratObject                    * 5.0.2      2024-05-08 [2] CRAN (R 4.4.0)
 SeuratWrappers                    0.4.0      2024-12-05 [1] Github (satijalab/seurat-wrappers@a1eb0d8)
 shiny                             1.9.1      2024-08-01 [2] CRAN (R 4.4.0)
 Signac                          * 1.14.9002  2025-01-17 [1] Github (stuart-lab/signac@39df167)
 SingleCellExperiment              1.26.0     2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
 SoupX                           * 1.6.2      2022-11-01 [2] CRAN (R 4.4.0)
 sp                              * 2.1-4      2024-04-30 [2] CRAN (R 4.4.0)
 spam                            * 2.11-0     2024-10-03 [2] CRAN (R 4.4.0)
 SparseArray                       1.4.8      2024-05-24 [1] Bioconductor 3.19 (R 4.4.0)
 spatstat.data                     3.1-2      2024-06-21 [2] CRAN (R 4.4.0)
 spatstat.explore                  3.3-2      2024-08-21 [2] CRAN (R 4.4.0)
 spatstat.geom                     3.3-3      2024-09-18 [2] CRAN (R 4.4.0)
 spatstat.random                   3.3-2      2024-09-18 [2] CRAN (R 4.4.0)
 spatstat.sparse                   3.1-0      2024-06-21 [2] CRAN (R 4.4.0)
 spatstat.univar                   3.0-1      2024-09-05 [2] CRAN (R 4.4.0)
 spatstat.utils                    3.1-0      2024-08-17 [2] CRAN (R 4.4.0)
 stringi                           1.8.4      2024-05-06 [2] CRAN (R 4.4.0)
 stringr                         * 1.5.1      2023-11-14 [2] CRAN (R 4.4.0)
 SummarizedExperiment              1.34.0     2024-05-01 [1] Bioconductor 3.19 (R 4.4.0)
 survival                          3.7-0      2024-06-05 [2] CRAN (R 4.4.0)
 tensor                            1.5        2012-05-05 [2] CRAN (R 4.4.0)
 tibble                          * 3.2.1      2023-03-20 [2] CRAN (R 4.4.0)
 tidyr                           * 1.3.1      2024-01-24 [2] CRAN (R 4.4.0)
 tidyselect                        1.2.1      2024-03-11 [2] CRAN (R 4.4.0)
 tidyverse                       * 2.0.0      2023-02-22 [2] CRAN (R 4.4.0)
 timechange                        0.3.0      2024-01-18 [2] CRAN (R 4.4.0)
 txdbmaker                       * 1.0.1      2024-06-23 [1] Bioconductor 3.19 (R 4.4.0)
 tzdb                              0.4.0      2023-05-12 [2] CRAN (R 4.4.0)
 UCSC.utils                        1.0.0      2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
 urlchecker                        1.0.1      2021-11-30 [2] CRAN (R 4.4.0)
 usethis                         * 3.0.0      2024-07-29 [2] CRAN (R 4.4.0)
 utf8                              1.2.4      2023-10-22 [2] CRAN (R 4.4.0)
 uwot                              0.2.2      2024-04-21 [2] CRAN (R 4.4.0)
 vctrs                             0.6.5      2023-12-01 [2] CRAN (R 4.4.0)
 viridisLite                     * 0.4.2      2023-05-02 [2] CRAN (R 4.4.0)
 withr                             3.0.1      2024-07-31 [2] CRAN (R 4.4.0)
 xfun                              0.48       2024-10-03 [2] CRAN (R 4.4.0)
 XML                               3.99-0.17  2024-06-25 [1] CRAN (R 4.4.0)
 xml2                              1.3.6      2023-12-04 [2] CRAN (R 4.4.0)
 xtable                            1.8-4      2019-04-21 [2] CRAN (R 4.4.0)
 XVector                         * 0.44.0     2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
 yaml                              2.3.10     2024-07-26 [2] CRAN (R 4.4.0)
 zlibbioc                          1.50.0     2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
 zoo                               1.8-12     2023-04-13 [2] CRAN (R 4.4.0)

 [1] ~/R/x86_64-pc-linux-gnu-library/4.4
 [2] /library
 [3] /usr/local/lib/R/site-library
 [4] /usr/lib/R/site-library
 [5] /usr/lib/R/library
@zgb963 zgb963 added the bug Something isn't working label Feb 20, 2025
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