Last updated: 2024-06-05
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Knit directory: lung_lymph_scMultiomics/
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Rmd | 32fec27 | Jing Gu | 2024-06-05 | update h2g analysis |
html | 71a11fc | Jing Gu | 2024-06-05 | Build site. |
Rmd | 8e7a5ac | Jing Gu | 2024-06-05 | h2g analysis |
A barplot of summarizing peak counts by types show Wang et al. identified more peaks in distal and exonic regions but fewer ones in promoters compared to our dataset. The color indicates whether each query peak in U19 overlaps with the one in Wang et al. We see more than 50% peaks in U19 overlapped with ones in Wang et al., while these overlapped peaks take up less than 50% of peaks in Wang et al.
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71a11fc | Jing Gu | 2024-06-05 |
The union set of peaks from U19 atac-seq data were first lifted over to hg19. The following table shows the change in genome builds only made 1% of peaks have inconsistent width.
nPeak_difference nPeak_hg19
lungs_Type_17_helper_T_cells 0 1208
lungs_Tem_Trm_cytotoxic_T_cells -58 10539
lungs_Tcm_Naive_helper_T_cells -35 13234
lungs_Regulatory_T_cells -44 12322
lungs_Naive_B_cells -85 18411
spleens_Regulatory_T_cells -16 8348
percentPeak_unequal_width
lungs_Type_17_helper_T_cells 0.017
lungs_Tem_Trm_cytotoxic_T_cells 0.011
lungs_Tcm_Naive_helper_T_cells 0.013
lungs_Regulatory_T_cells 0.010
lungs_Naive_B_cells 0.012
spleens_Regulatory_T_cells 0.014
Test one annotation at a time
Overall, we observed immune cells in both tissues are significantly enriched for genetic risks of immune diseases but not those of other traits. T cells from both tissues show significant enrichment for risk variants of immune diseases, while only lung B cells show enrichment.
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71a11fc | Jing Gu | 2024-06-05 |
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71a11fc | Jing Gu | 2024-06-05 |
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71a11fc | Jing Gu | 2024-06-05 |
Jointly test multiple annotations
The chromatin accessible peaks from T cells, B cells, and NK cells were jointly tested for each tissue. The results show that lung immune cells explain more disease heritability than spleen immmune cells. Conditional on other lung immune cells, Treg OCRs have the most contribution in Allergy and AOA, followed by B cells and NK cells. For COA, the disease heritability was explained evenly by Treg, CD4+ T and naive B cells. The CD8+ T cells only contributes to COA heritability.
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71a11fc | Jing Gu | 2024-06-05 |
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71a11fc | Jing Gu | 2024-06-05 |
Tissue-specific analyses
Based on the list of enhancers ranked by the causal signals from GWAS and other functional data, I looked for peaks in our dataset that overlapped with high-confident enhancers. For enhancers overlapped by multiple peaks, the peak scores were computed by the average of their significant scores from peak calling.
Heatmap for peak scores
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rhdf5_2.42.1 SummarizedExperiment_1.28.0
[3] Biobase_2.58.0 MatrixGenerics_1.10.0
[5] Rcpp_1.0.12 Matrix_1.6-5
[7] matrixStats_1.2.0 stringr_1.5.1
[9] plyr_1.8.9 magrittr_2.0.3
[11] gtable_0.3.5 gtools_3.9.5
[13] gridExtra_2.3 ArchR_1.0.2
[15] rtracklayer_1.58.0 data.table_1.15.4
[17] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[19] IRanges_2.32.0 S4Vectors_0.36.2
[21] BiocGenerics_0.44.0 dplyr_1.1.4
[23] ggplot2_3.4.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] httr_1.4.7 sass_0.4.9 jsonlite_1.8.8
[4] bslib_0.7.0 getPass_0.2-2 highr_0.10
[7] GenomeInfoDbData_1.2.9 Rsamtools_2.14.0 yaml_2.3.8
[10] pillar_1.9.0 lattice_0.22-5 glue_1.7.0
[13] digest_0.6.35 promises_1.3.0 XVector_0.38.0
[16] colorspace_2.1-0 htmltools_0.5.8.1 httpuv_1.6.14
[19] XML_3.99-0.16.1 pkgconfig_2.0.3 zlibbioc_1.44.0
[22] scales_1.3.0 processx_3.8.3 whisker_0.4.1
[25] later_1.3.2 BiocParallel_1.32.6 git2r_0.33.0
[28] tibble_3.2.1 farver_2.1.1 generics_0.1.3
[31] cachem_1.0.8 withr_3.0.0 cli_3.6.2
[34] crayon_1.5.2 evaluate_0.23 ps_1.7.6
[37] fs_1.6.4 fansi_1.0.6 tools_4.2.0
[40] BiocIO_1.8.0 lifecycle_1.0.4 Rhdf5lib_1.20.0
[43] munsell_0.5.1 DelayedArray_0.24.0 callr_3.7.3
[46] Biostrings_2.66.0 compiler_4.2.0 jquerylib_0.1.4
[49] rlang_1.1.3 RCurl_1.98-1.14 rhdf5filters_1.10.1
[52] rstudioapi_0.15.0 rjson_0.2.21 labeling_0.4.3
[55] bitops_1.0-7 rmarkdown_2.26 restfulr_0.0.15
[58] codetools_0.2-19 R6_2.5.1 GenomicAlignments_1.34.1
[61] knitr_1.46 fastmap_1.1.1 utf8_1.2.4
[64] rprojroot_2.0.4 stringi_1.7.6 parallel_4.2.0
[67] vctrs_0.6.5 tidyselect_1.2.1 xfun_0.43