Identify lung-specific transcriptomic features
2025-05-14
Last updated: 2025-05-14
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Knit directory: Lung_scMultiomics_paper/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c40f0da | Jing Gu | 2025-05-14 | DEG analyses |
Differential gene expression analyses across tissue
Wilcoxon ranksum test at single-cell leveL gives more conservative results.
Summarizing DE genes by selecting p-value cutoffs
A table of cell counts by tissue and cell-type.
Loading required package: SingleCellExperiment
lungs spleens
Other 1654 104
Treg 1336 47
Th17 2732 68
CD4_T 6980 886
CD8_T 12210 421
NK 8067 464
Memory_B 5287 10507
Naive_B 1174 1710Barplots for number or DE genes detected for each cell type
Th17 and Treg were not included in the following analyses due to low number of cells in spleen.
`summarise()` has grouped output by 'cluster'. You can override using the
`.groups` argument.
Joining with `by = join_by(cluster)`
Compare and contrast DE genes across immune subsets
A Venn diagram for DE genes shared across cell types other than memory B cells implies DE genes are cell-type specific.
Attaching package: 'ggVennDiagram'The following object is masked from 'package:tidyr':
unite
A full summary of shared and unique DE genes across cell types
UpSet-style plot only shows the count of elements specific to each intersection.
Lung up-regulated genes
Check the lung up-regulated genes shared across all immune subsets
[1] "HSP90AA1 HSPA1A HSP90AB1 HSPD1 RPS26 DNAJB1 HSPA6 HSPA1B HSPH1 HSPE1 HSPB1 CACYBP HSPA8 UBC DOK2 BAG3 STIP1 ABHD3 PLIN2 ZFAND2A GNLY FKBP4 TNFRSF1B GBP2 CGAS MT2A HSPA4 NKG7 AHSA1 SERPINH1 CCL4L2 GZMB GIPC1"Spleen up-regulated genes
GO enrichment results
Lung-regulated genes [[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>k<\/th>\n <th>description<\/th>\n <th>enrichmentRatio<\/th>\n <th>userId<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":3},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"k","targets":1},{"name":"description","targets":2},{"name":"enrichmentRatio","targets":3},{"name":"userId","targets":4}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
$geneontology_Cellular_Component_noRedundant
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$geneontology_Molecular_Function_noRedundant
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$pathway_KEGG
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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>k<\/th>\n <th>description<\/th>\n <th>enrichmentRatio<\/th>\n <th>userId<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":3},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"k","targets":1},{"name":"description","targets":2},{"name":"enrichmentRatio","targets":3},{"name":"userId","targets":4}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>Spleen-regulated genes
The DE genes down-regulated in lung detected from memory B cells are significantly enriched for asthma risk genes from KEGG pathway. The overlapped genes are HLA genes and CD40. Their function in B cells might be enhancing subsequent interaction with T cells.
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
$`community-contributed_Hallmark50`
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$disease_GLAD4U
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$geneontology_Molecular_Function_noRedundant
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$pathway_KEGG
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We performed K-means clustering over log2FC for all genes with at most one NA across cell types.
Heatmap for average log2FC for each cluster
Clustering for effect sizes does not show cell type specificity except for memory B cells.
Heatmap for average log2FC for each cell-type (Memory B excluded)
Clustering for effect sizes shows stronger cell-type specificity.
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
$`community-contributed_Hallmark50`
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$disease_GLAD4U
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$geneontology_Cellular_Component_noRedundant
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$geneontology_Molecular_Function_noRedundant
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$pathway_KEGG
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`
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<p>
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Session information
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<div id="workflowr-sessioninfo" class="collapse">
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] ggVennDiagram_1.5.2
SingleCellExperiment_1.20.1 [3] ComplexHeatmap_2.14.0 scales_1.3.0
[5] colorRamp2_0.1.0 rtracklayer_1.58.0
[7] tidyr_1.3.1 dplyr_1.1.4
[9] rhdf5_2.42.1 SummarizedExperiment_1.28.0 [11] Biobase_2.58.0
MatrixGenerics_1.10.0
[13] Rcpp_1.0.14 Matrix_1.6-5
[15] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[17] IRanges_2.32.0 S4Vectors_0.36.2
[19] BiocGenerics_0.44.0 matrixStats_1.5.0
[21] data.table_1.17.0 stringr_1.5.1
[23] plyr_1.8.9 magrittr_2.0.3
[25] ggplot2_3.5.2 gtable_0.3.6
[27] gtools_3.9.5 gridExtra_2.3
[29] ArchR_1.0.2
[4] RColorBrewer_1.1-3 rprojroot_2.0.4 tools_4.2.0
[7] bslib_0.9.0 DT_0.33 R6_2.6.1
[10] colorspace_2.1-1 rhdf5filters_1.10.1 GetoptLong_1.0.5
[13] withr_3.0.2 tidyselect_1.2.1 compiler_4.2.0
[16] git2r_0.33.0 cli_3.6.4 Cairo_1.6-2
[19] DelayedArray_0.24.0 labeling_0.4.3 sass_0.4.9
[22] yulab.utils_0.2.0 digest_0.6.37 Rsamtools_2.14.0
[25] rmarkdown_2.29 XVector_0.38.0 pkgconfig_2.0.3
[28] htmltools_0.5.8.1 fastmap_1.2.0 htmlwidgets_1.6.4
[31] rlang_1.1.5 GlobalOptions_0.1.2 rstudioapi_0.17.1
[34] gridGraphics_0.5-1 farver_2.1.2 shape_1.4.6
[37] jquerylib_0.1.4 BiocIO_1.8.0 generics_0.1.3
[40] jsonlite_2.0.0 crosstalk_1.2.1 BiocParallel_1.32.6
[43] RCurl_1.98-1.17 ggplotify_0.1.2 GenomeInfoDbData_1.2.9
[46] patchwork_1.3.0 munsell_0.5.1 Rhdf5lib_1.20.0
[49] lifecycle_1.0.4 stringi_1.8.4 whisker_0.4.1
[52] yaml_2.3.10 zlibbioc_1.44.0 parallel_4.2.0
[55] promises_1.3.2 forcats_1.0.0 crayon_1.5.3
[58] lattice_0.22-7 Biostrings_2.66.0 circlize_0.4.15
[61] knitr_1.50 pillar_1.10.2 rjson_0.2.23
[64] codetools_0.2-20 XML_3.99-0.18 glue_1.8.0
[67] evaluate_1.0.3 ggfun_0.1.8 vctrs_0.6.5
[70] png_0.1-8 httpuv_1.6.15 foreach_1.5.2
[73] purrr_1.0.4 clue_0.3-66 cachem_1.1.0
[76] xfun_0.52 restfulr_0.0.15 later_1.4.2
[79] tibble_3.2.1 aplot_0.2.5 iterators_1.0.14
[82] GenomicAlignments_1.34.1 cluster_2.1.8.1 workflowr_1.7.1
```