cross-tissue DE analysis with fastTopics
Jing Gu
2024-05-25
Last updated: 2024-05-25
Checks: 7 0
Knit directory: lung_lymph_scMultiomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b7a8317 | Jing Gu | 2024-05-25 | updated result page |
| html | 4e0028d | Jing Gu | 2024-05-23 | Build site. |
| Rmd | 36aceab | Jing Gu | 2024-05-23 | organized results |
| html | 744d4f0 | Jing Gu | 2024-05-22 | Build site. |
| Rmd | 5c6b22f | Jing Gu | 2024-05-22 | updated GSEA results |
| html | 3d73252 | Jing Gu | 2024-05-13 | Build site. |
| Rmd | f26e8ad | Jing Gu | 2024-05-13 | wflow_publish("analysis/cross_tissue_DE_u19_fastTopics.Rmd") |
| html | 95bfdff | Jing Gu | 2024-05-13 | Build site. |
| Rmd | 3e4f015 | Jing Gu | 2024-05-13 | wflow_publish("analysis/cross_tissue_DE_u19_fastTopics.Rmd") |
| html | 2e475c9 | Jing Gu | 2024-05-13 | Build site. |
| Rmd | 306ddb1 | Jing Gu | 2024-05-13 | wflow_publish("analysis/cross_tissue_DE_u19_fastTopics.Rmd") |
| html | f812a40 | Jing Gu | 2024-05-09 | Build site. |
| html | 68d9e18 | Jing Gu | 2024-05-09 | Build site. |
| Rmd | 7ca45f6 | Jing Gu | 2024-05-09 | cross-tissue comparison |
| Rmd | 7a45261 | Jing Gu | 2024-05-08 | cross-tissue comparison with topic modeling |
GoM DE analysis on u19 dataset
Model fitting
Parameters:
N_updates = 150 N_topics = 12
Model evaluation
check the convergence
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
Model overview:
Number of data rows, n: 53647
Number of data cols, m: 17420
Rank/number of topics, k: 12
Evaluation of model fit (170 updates performed):
Poisson NMF log-likelihood: -1.997995557900e+08
Multinomial topic model log-likelihood: -1.995509032083e+08
Poisson NMF deviance: +2.634951598369e+08
Max KKT residual: +1.430262e-02Set show.size.factors = TRUE, show.mixprops = TRUE and/or show.topic.reps = TRUE in print(...) for more information
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
Visualize topics with structural plots
- plot by cell-types
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
- plot by tissue
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
- plot by tissue and cell-type
| Version | Author | Date |
|---|---|---|
| 68d9e18 | Jing Gu | 2024-05-09 |
Validate topics with enrichment test
GO enrichemnt test on top 500 genes ranked by loadings
GO enrichemnt test on GoM DE analysis
Volcano plots for GoM DE results
The z-scores for posterior mean log-fold change estimates
vs. log-fold change
| Version | Author | Date |
|---|---|---|
| 2e475c9 | Jing Gu | 2024-05-13 |
GO Enrichemnt results for upregulated genes with local false sign rates < 0.01
Topic 3,4,5,6 show strong enrichment for the GO terms of T-cell activation, leukocyte differentiation, positive regulation of cell adhesion, adaptive immune response.
From volcano plots, we see topic 6 has high LFC in genes such as IL17A, IL22, which are mainly produced by Th17 cells.
Topic 10 genes do not show enrichment for any GO term.
Topic 11 is strongly enriched for GO terms related to regulation of innate or adaptive immune response.
Topic 12 shows strong enrichment for the GO term of B cell activation.
Identify topics correlated with differences between tissue
- test whether any topic is associated with transcriptional differences across tissue \[ L = \beta X_{\text{tissue}} + \text{Covariates} + \epsilon \]
- perform T-test to see whether topic proportions between two tissues are significantly different
Barplot for topic proportions in cell types
- The topic proportion for k10 is higher in spleens than lungs in both naive B and memory B cells.
- The topic proportions for k11 is higher in spleens than lungs in memory B cells only.
- The topic proportions for k4 is higher in spleens than lungs across cell types.
- The topic proportions for k6 is higher in lungs than spleens across cell types.
- The topic proportions for k5 is higher in lungs than spleens across T cell subsets.
NK other Memory_B Naive_B Treg CD4_T CD8_T Th17
lungs 8067 1654 5287 1174 1336 6980 12210 2732
spleens 464 104 10507 1710 47 886 421 68
| Version | Author | Date |
|---|---|---|
| 744d4f0 | Jing Gu | 2024-05-22 |
Comparing GO terms between K4 and K5:
All k4 terms included in k5.
Comparing GO terms between K10 and K11:
All K10 terms included in K11.
Perform t-test while adjusting for confounders
Procedure
Test mean difference between tissue one donor at a time and then do meta-analysis with Fisher’s method
Results
X-axis denotes cell types and y-axis denotes the topics. For major cell types, we saw majority of topics have significant differences in proportions between tissue.
| Version | Author | Date |
|---|---|---|
| 744d4f0 | Jing Gu | 2024-05-22 |
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 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.14.0 colorRamp2_0.1.0 tidyr_1.3.1
[4] dplyr_1.1.4 poolr_1.1-1 cowplot_1.1.3
[7] ggplot2_3.5.1 fastTopics_0.6-175 Matrix_1.6-5
[10] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] matrixStats_1.2.0 fs_1.6.4 RColorBrewer_1.1-3
[4] doParallel_1.0.17 progress_1.2.3 httr_1.4.7
[7] rprojroot_2.0.4 tools_4.2.0 bslib_0.7.0
[10] DT_0.33 utf8_1.2.4 R6_2.5.1
[13] irlba_2.3.5.1 BiocGenerics_0.44.0 uwot_0.2.2
[16] lazyeval_0.2.2 colorspace_2.1-0 GetoptLong_1.0.5
[19] withr_3.0.0 tidyselect_1.2.1 prettyunits_1.2.0
[22] processx_3.8.3 compiler_4.2.0 git2r_0.33.0
[25] cli_3.6.2 Cairo_1.6-2 plotly_4.10.4
[28] labeling_0.4.3 sass_0.4.9 scales_1.3.0
[31] SQUAREM_2021.1 quadprog_1.5-8 callr_3.7.3
[34] pbapply_1.7-2 mixsqp_0.3-54 stringr_1.5.1
[37] digest_0.6.35 rmarkdown_2.26 RhpcBLASctl_0.23-42
[40] pkgconfig_2.0.3 htmltools_0.5.8.1 highr_0.10
[43] fastmap_1.1.1 invgamma_1.1 GlobalOptions_0.1.2
[46] htmlwidgets_1.6.4 rlang_1.1.3 rstudioapi_0.15.0
[49] farver_2.1.1 shape_1.4.6 jquerylib_0.1.4
[52] generics_0.1.3 jsonlite_1.8.8 crosstalk_1.2.1
[55] gtools_3.9.5 magrittr_2.0.3 S4Vectors_0.36.2
[58] Rcpp_1.0.12 munsell_0.5.1 fansi_1.0.6
[61] lifecycle_1.0.4 stringi_1.7.6 whisker_0.4.1
[64] yaml_2.3.8 mathjaxr_1.6-0 Rtsne_0.17
[67] parallel_4.2.0 promises_1.3.0 ggrepel_0.9.5
[70] crayon_1.5.2 lattice_0.22-5 circlize_0.4.15
[73] hms_1.1.3 knitr_1.46 ps_1.7.6
[76] pillar_1.9.0 rjson_0.2.21 stats4_4.2.0
[79] codetools_0.2-19 glue_1.7.0 evaluate_0.23
[82] getPass_0.2-2 data.table_1.15.4 RcppParallel_5.1.7
[85] vctrs_0.6.5 png_0.1-8 httpuv_1.6.14
[88] foreach_1.5.2 gtable_0.3.5 purrr_1.0.2
[91] clue_0.3-65 ashr_2.2-63 cachem_1.0.8
[94] xfun_0.43 later_1.3.2 viridisLite_0.4.2
[97] truncnorm_1.0-9 tibble_3.2.1 iterators_1.0.14
[100] IRanges_2.32.0 cluster_2.1.6