Asthma project
Last updated: 2021-09-23
Checks: 6 1
Knit directory: funcFinemapping/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e5d6f32 | Jing Gu | 2021-09-23 | added percent of SNPs with annotations |
| html | 53a8774 | Jing Gu | 2021-09-22 | Build site. |
| html | 2a6fc91 | Jing Gu | 2021-09-22 | Build site. |
| Rmd | bbb2944 | Jing Gu | 2021-09-22 | analyzed Asthma GWAS with functional priors from single cell data |
| html | 41f53c8 | Jing Gu | 2021-09-22 | Build site. |
| Rmd | 33d4e5a | Jing Gu | 2021-09-22 | analyzed Asthma GWAS with functional priors from single cell data |
| html | b0b938b | Jing Gu | 2021-09-22 | Build site. |
| Rmd | 8a5656c | Jing Gu | 2021-09-22 | analyzed Asthma GWAS with functional priors from single cell data |
Overall Goal
Identify disease-relevant cell types and genes for Asthma
Enrichment estimates for individual annotation
Adult-onset asthma
- GWAS: Zhu et al. 2019 (case N=22296, control N=347481)
- cell-type specific Annotations: Zhang et al.2021 - single-cell ATAC-Seq data for adult lungs
- Entropy based method used to identify cell-type specific peaks
- For each cell type, peaks that pass 0.1% FDR cutoff were used to generate binary annotations
Enrichment results
| Version | Author | Date |
|---|---|---|
| b0b938b | Jing Gu | 2021-09-22 |
Log2 scaled enrichment estimates for each cell type, which was individually run using Torus. The highlighted bars show significant enrichment, while the bars in grey color do not. The x-axis tickmark labels consist of the annotation name and percent of all SNPs that have that annotation. Asthma-associated variants are significantly enriched in certain sub-clusters of macrophages and fibroblasts, but not in others. The cell types that show the highest enrichment in the chromatin accessibility peaks are immune-related cells.
- Caldero et al. 2019 - ATAC-Seq data for FACS-sorted cells from whole blood
- Significant differentially accessible regions when compared to progenitor cells
- Significant differentially accessible regions under stimulation
| Version | Author | Date |
|---|---|---|
| b0b938b | Jing Gu | 2021-09-22 |
# bg_percent=c()
# for(i in df$term){
# fname<-strsplit(i, "_atac")[[1]][1]
# annot<-fread(sprintf("/home/jinggu/cluster/projects/torus_enrichment/asthma_adult/stimu_DA/torus_annotations_%s.txt.gz",fname))
# bg_percent<-rbind(bg_percent, c(fname, sum(annot[,2])))
# }
# bg_percent<-data.frame(bg_percent)
# bg_percent$X2<-as.numeric(bg_percent$X2)
# bg_percent["percent"]<-paste0(round(bg_percent$X2/dim(gwas)[1]*100,2), "%")
# write.table(bg_percent,"~/projects/funcFinemapping/output/asthma/Caldero2019_stimuDA_annot_percent.txt", quote = F,row.names=F, col.names = F, sep='\t')- Not sure why much fewer cell types show differentially accessiblity during differentiation.
| Version | Author | Date |
|---|---|---|
| b0b938b | Jing Gu | 2021-09-22 |
Upon stimulation, the enrichment signals prevail across a broader range of immune cells. There are more diverse stimulated cell types that show differential accessibility against the unstimulated ones.
Joint enrichment estimates across annotations
immune cells in lungs vs. immune cells in blood
| Version | Author | Date |
|---|---|---|
| b0b938b | Jing Gu | 2021-09-22 |
Both clusters of cell-type specific accessible regions from T lymphocytes in lungs are significantly enriched with risk variants conditional on differentiation-specific regions identified from blood.
The enrichment signal of T lymphocytes remains after conditional on stimulated immune cell types in blood. The additional enrichment signals come from stimulated memoryCD8+ T-cells, T_helpler cells, mast cells, Th1 precursors, and Th2 precursors.
Children-onset Asthma
- GWAS: Zhu et al. 2019
- cell-type specific Annotations: Domcke et al.2020 - single-cell ATAC-Seq data for fetal lungs
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
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
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 pillar_1.5.0 compiler_4.0.4 bslib_0.2.4
[5] later_1.1.0.1 jquerylib_0.1.3 git2r_0.28.0 highr_0.8
[9] tools_4.0.4 digest_0.6.27 jsonlite_1.7.2 evaluate_0.14
[13] lifecycle_1.0.0 tibble_3.0.6 gtable_0.3.0 pkgconfig_2.0.3
[17] rlang_0.4.11 DBI_1.1.1 yaml_2.2.1 xfun_0.21
[21] withr_2.4.2 dplyr_1.0.4 stringr_1.4.0 knitr_1.31
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[29] tidyselect_1.1.1 rprojroot_2.0.2 grid_4.0.4 glue_1.4.2
[33] R6_2.5.1 fansi_0.5.0 rmarkdown_2.7 farver_2.1.0
[37] purrr_0.3.4 magrittr_2.0.1 whisker_0.4 scales_1.1.1
[41] promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.1.1 assertthat_0.2.1
[45] colorspace_2.0-2 httpuv_1.5.5 labeling_0.4.2 utf8_1.2.2
[49] stringi_1.5.3 munsell_0.5.0 crayon_1.4.1