u19_h2g_enrichment_analysis
Jing Gu
2024-06-05
Last updated: 2024-06-05
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Knit directory: lung_lymph_scMultiomics/
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| Rmd | 8e7a5ac | Jing Gu | 2024-06-05 | h2g analysis |
Outlines
- Evaluate how much additional information we can get from our multi-omics dataset
- characterize and compare our identified open chromatin peaks with Wang et al.
- estimate heritability enrichment for annotations derived from our
chromatin data across many traits
- cell-type based annotations
- topic-based annotations
- Identify relevant cell-types and tissues for Asthma
- find peaks in our dataset that overlap with enhancers with high ePIPs based on GWAS and other single-cell ATAC-seq data and then get the corresponding cell-type and tissue information
- compute ePIP using our own data set and then identify relevant
cell-types or tissues
- Compute ePIPs by summing up PIPs of causal variants that overlap with each enhancer
Evaluate the contribution of our multi-omics dataset to disease heritability
Characterize and compare peaks from T cells between two dataset
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.
Test heritability enrichment through S-LDSC
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.
Test one annotation at a time
- GWAS: a broad range of triats
- Baseline annotation: BaselineLD_v2.2
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.
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.
Identify relevant cell-types and tissues for Asthma
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
- X-axis denotes enhancer-gene pair, ranked by either AOA ePIP or COA ePIP.
- Color of heatmap is the log2 value of mean significant scores for peaks that overlap with enhancer of high PIPs.
AOA enhancers ranked by ePIPs
COA enhancers ranked by ePIPs
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