Annotating cell types for sc-multiomics dataset
Jing Gu
2025-05-29
Last updated: 2025-05-29
Checks: 6 1
Knit directory: Lung_scMultiomics_paper/
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| Rmd | 0976797 | Jing Gu | 2025-05-25 | cell composition and label transferring |
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| Rmd | 5962bce | Jing Gu | 2025-05-12 | wflow_publish("./identify_cell_types.Rmd") |
Identifying lung and spleen immune cell types
Annotate clusters in RNA-seq data with marker gene expression
UMAP for gene expression of all the cells, split by tissue of origin, with clusters annotated by CellTypist, a machine learning tool developed to predict cell types based on the expression of marker genes.
Dotplot for expression levels of CD69 and marker genes for immune subsets
Annotate clusters of ATAC-seq data with majority voting
Sample QC
COB-5 sample has a small cluster of cells with low number of
fragments, so top 5K cells ranked by number of fragments were retained.
As a result, we see more consistent distribution from the rigid plots of
TSS enrichment and log10 of number of fragments across samples.
Ridge plots for TSS enrichment and log10(nFrags) across samples show a relatively consistent distribution.
Fragment size distributions are variable across samples, but overall enriched for sizes of one or two nucleosomes
Clustering of ATAC-seq data shows no strong bias to one batch or one sample.
| Version | Author | Date |
|---|---|---|
| f1db51c | Jing Gu | 2025-05-25 |
Clustering of ATAC-seq data also shows no distinct clusters for either tissue or disease status.
Cell composition for each cluster of ATAC-seq data
Majority of clusters were dominated by one single cell type from matched RNA-seq, while two clusters show some ambiguity and so labeled as “CD8/CD4_T” and “Th17/CD4_T”.
| Version | Author | Date |
|---|---|---|
| f1db51c | Jing Gu | 2025-05-25 |
UMAP for ATAC-seq data from 100K cells
left plot - cell labels before majority-voting
right plot - cell labels after majority-voting
Validating cell type annotations
Marker genes show high gene scores computed from nearby ATAC-seq peaks for the corresponding cluster
| Version | Author | Date |
|---|---|---|
| f1db51c | Jing Gu | 2025-05-25 |
Annotating clusters with label transferring
Most of the clusters were annotated similarly using both majority voting or label transferring approaches. For the ambiguous clusters, results from label transferring show C16 was mostly Th17 and C14 was mixture of CD8/CD4_T cells. This is due to imperfect clustering or heterogeneity at chromatin levels for cells in those clusters.
| Version | Author | Date |
|---|---|---|
| f1db51c | Jing Gu | 2025-05-25 |
Track plots for all peaks near TSS of each marker gene
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] hexbin_1.28.5
[2] ggridges_0.5.6
[3] SingleCellExperiment_1.20.1
[4] cowplot_1.1.3
[5] ggrepel_0.9.6
[6] eulerr_7.0.2
[7] liftOver_1.22.0
[8] Homo.sapiens_1.3.1
[9] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[10] org.Hs.eg.db_3.16.0
[11] GO.db_3.16.0
[12] OrganismDbi_1.40.0
[13] GenomicFeatures_1.50.4
[14] AnnotationDbi_1.60.2
[15] rtracklayer_1.58.0
[16] gwascat_2.30.0
[17] rhdf5_2.42.1
[18] SummarizedExperiment_1.28.0
[19] Biobase_2.58.0
[20] MatrixGenerics_1.10.0
[21] Rcpp_1.0.14
[22] Matrix_1.6-5
[23] GenomicRanges_1.50.2
[24] GenomeInfoDb_1.34.9
[25] IRanges_2.32.0
[26] S4Vectors_0.36.2
[27] BiocGenerics_0.44.0
[28] matrixStats_1.5.0
[29] data.table_1.17.4
[30] stringr_1.5.1
[31] plyr_1.8.9
[32] magrittr_2.0.3
[33] ggplot2_3.5.2
[34] gtable_0.3.6
[35] gtools_3.9.5
[36] gridExtra_2.3
[37] ArchR_1.0.2
[38] dplyr_1.1.4
loaded via a namespace (and not attached):
[1] rjson_0.2.23 rprojroot_2.0.4 XVector_0.38.0
[4] fs_1.6.6 dichromat_2.0-0.1 rstudioapi_0.17.1
[7] farver_2.1.2 bit64_4.0.5 xml2_1.3.8
[10] codetools_0.2-20 splines_4.2.0 snpStats_1.48.0
[13] cachem_1.1.0 knitr_1.50 jsonlite_2.0.0
[16] workflowr_1.7.1 Cairo_1.6-2 Rsamtools_2.14.0
[19] dbplyr_2.5.0 png_0.1-8 graph_1.76.0
[22] BiocManager_1.30.25 readr_2.1.5 compiler_4.2.0
[25] httr_1.4.7 fastmap_1.2.0 cli_3.6.5
[28] later_1.4.2 htmltools_0.5.8.1 prettyunits_1.2.0
[31] tools_4.2.0 glue_1.8.0 GenomeInfoDbData_1.2.9
[34] rappdirs_0.3.3 jquerylib_0.1.4 vctrs_0.6.5
[37] Biostrings_2.66.0 rhdf5filters_1.10.1 xfun_0.52
[40] lifecycle_1.0.4 restfulr_0.0.15 XML_3.99-0.18
[43] zlibbioc_1.44.0 scales_1.4.0 BSgenome_1.66.3
[46] VariantAnnotation_1.44.1 hms_1.1.3 promises_1.3.2
[49] parallel_4.2.0 RBGL_1.74.0 RColorBrewer_1.1-3
[52] yaml_2.3.10 curl_6.2.3 memoise_2.0.1
[55] sass_0.4.10 biomaRt_2.54.1 stringi_1.8.4
[58] RSQLite_2.3.11 BiocIO_1.8.0 filelock_1.0.3
[61] BiocParallel_1.32.6 rlang_1.1.6 pkgconfig_2.0.3
[64] bitops_1.0-9 evaluate_1.0.3 lattice_0.22-7
[67] Rhdf5lib_1.20.0 labeling_0.4.3 GenomicAlignments_1.34.1
[70] bit_4.6.0 tidyselect_1.2.1 R6_2.6.1
[73] generics_0.1.4 DelayedArray_0.24.0 DBI_1.2.3
[76] pillar_1.10.2 whisker_0.4.1 withr_3.0.2
[79] survival_3.8-3 KEGGREST_1.38.0 RCurl_1.98-1.17
[82] tibble_3.2.1 crayon_1.5.3 BiocFileCache_2.6.1
[85] tzdb_0.5.0 rmarkdown_2.29 progress_1.2.3
[88] blob_1.2.4 git2r_0.33.0 digest_0.6.37
[91] httpuv_1.6.16 bslib_0.9.0