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| Rmd | 6528782 | Jing Gu | 2025-11-03 | correct batches |
Previous results were due to a mistake in filtering DE results by requiring expressed in at least three cells. For pseudobulk samples, we only have three samples for spleens. So genes not expressed in spleens were filtered out.
After batch correction, the numbers of DE genes are similar between two approaches.
Pseudo-bulk appraoch
CD4-T CD8-T NK Memory-B Naive-B
lung_up 233 201 242 1329 138
spleen_up 182 129 108 859 74
Single-cell approach
CD4_T CD8_T NK Memory_B Naive_B
lung_up 216 111 152 647 131
spleen_up 257 452 678 1163 172
Top 10 lung-upregulated genes in each cell type
Somehow spleen-upregulated genes in T cells are enriched for B cell activation.

In summary, genes with higher expression in lungs are broadly enriched for T cell related functions, while those in spleens are broadly enriched for B cell related functions. We still see lung up-regulated genes enriched in pathways of heat-shock protein genes. Overall, the GO enrichment results are consistent between bulk or single-cell approach.
We have shown that chromatin accessibility for cross-tissue DE genes are very similar. Thus, some other mechanisms may drive the differences, which motivates us to test whether TF activity is responsible for the up-regulation of genes in lung.
Cell composition for both tissues 
We examined GRNs for B cells because both tissues have relatively similar number of cells. Though half amount of memory B cells found in lung compared to spleen, we still detected several lung TFs with higher activity relative to spleen. The differences in naive B cells are small, which is consistent with lack of differences in cross-tissue DE genes in naive B cells.
[[1]]

[[2]]
Let’s use TFs with >= 100 targets genes in lung memory B cells and
ask whether they explain up-regulated genes in lungs
Here I performed the tests on DE genes identified using single-cell or bulk approach.
[1] "Results for single-cell approach DE genes:"
[1] p_value odds_ratio conf_low conf_high FDR
<0 rows> (or 0-length row.names)
[1] "Results for bulk approach DE genes:"
p_value odds_ratio conf_low conf_high FDR
GATA3 2.8e-03 2.28 1.30 3.81 0.06906667
RUNX3 4.4e-03 2.00 1.21 3.16 0.09302857
As we identified lung-specific TFs enriched for DE genes, we further asked if TFs that have stronger links tend to have DE genes with larger differences between lung and spleen. Let’s use gene expression in spleen as baseline. If specific lung TFs drive gene expression in lung to be higher than baseline, we would expect to higher TF activity leads to higher expression.
Method For each lung-specific TF, we identified lung up-regulated genes in its network. Then we plotted TF strength and differential expression and showed the fitted line.
Bulk DE genes
[[1]]

[[2]]

No clear positive trends for the two TFs enriched for bulk-level DE genes.
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
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[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
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[5] tidyr_1.3.1 dplyr_1.1.4
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