Motif analysis using topic modeling and DA results (v2) for Cusanovich et al (2018) scATAC-seq data

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-02-16

Checks: 7 0

Knit directory: scATACseq-topics/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

Seed: set.seed(20200729)

The command set.seed(20200729) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 33840df

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 33840df. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/plotly/Buenrostro_2018_Chen2019pipeline/
    Ignored:    output/plotly/Buenrostro_2018_Chen2019pipeline_v2/

Untracked files:
    Untracked:  analysis/analysis_Buenrostro2018_k10.Rmd
    Untracked:  analysis/clusters_Buenrostro2018_k10_Chen2019pipeline.Rmd
    Untracked:  analysis/process_data_Buenrostro2018_Chen2019.Rmd
    Untracked:  analysis/selected_figures_Buenrostro2018.Rmd
    Untracked:  analysis/test_structure_plots_Buenrostro2018_k11_Chen2019pipeline.Rmd
    Untracked:  gsea_b_cells.html
    Untracked:  output/clustering-Cusanovich2018.rds
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_10_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_11_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_12_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_13_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_1_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_2_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_3_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_4_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_5_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_6_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_7_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_8_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/gsea_topic_9_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_10_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_11_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_12_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_13_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_1_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_2_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_3_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_4_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_5_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_6_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_7_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_8_tss-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum_files/plotly-main-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_tss-sum_files/crosstalk-1.1.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_tss-sum_files/jquery-3.5.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_tss-sum_files/plotly-binding-4.9.3/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_tss-sum_files/plotly-htmlwidgets-css-1.57.1/
    Untracked:  output/plotly/Cusanovich2018/volcano_topic_9_tss-sum_files/plotly-main-1.57.1/
    Untracked:  paper/
    Untracked:  scripts/fit_all_models_Buenrostro_2018_chromVar_scPeaks_filtered.sbatch
    Untracked:  scripts/fit_models_Cusanovich2018_tissues.sh
    Untracked:  scripts/postfit_Buenrostro2018_Chen2019pipeline_v2.sh
    Untracked:  scripts/postfit_Cusanovich2018_v2.sh
    Untracked:  scripts/postfit_DA_analysis.sbatch
    Untracked:  topic1.html

Unstaged changes:
    Modified:   analysis/clusters_Cusanovich2018_k13.Rmd
    Modified:   analysis/gene_analysis_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/gene_analysis_Cusanovich2018.Rmd
    Modified:   analysis/motif_analysis_Buenrostro2018_Chen2019pipeline.Rmd
    Modified:   analysis/motif_analysis_Buenrostro2018_Chen2019pipeline_v2.Rmd
    Modified:   analysis/motif_analysis_Cusanovich2018.Rmd
    Modified:   analysis/plots_Cusanovich2018.Rmd
    Modified:   analysis/process_data_Cusanovich2018.Rmd
    Modified:   code/functions_for_assessing_fits.R
    Modified:   code/plots.R
    Modified:   output/plotly/Cusanovich2018/gsea_topic_10_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_10_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_11_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_11_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_12_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_12_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_13_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_13_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_1_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_1_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_2_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_2_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_3_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_3_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_4_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_4_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_5_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_5_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_6_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_6_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_7_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_7_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_8_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_8_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_9_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/gsea_topic_9_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_10_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_10_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_11_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_11_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_12_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_12_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_13_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_13_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_1_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_1_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_2_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_2_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_3_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_3_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_4_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_4_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_5_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_5_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_6_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_6_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_7_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_7_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_8_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_8_tss-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_9_genebody-sum.html
    Modified:   output/plotly/Cusanovich2018/volcano_topic_9_tss-sum.html
    Modified:   scripts/DA_analysis.R
    Modified:   scripts/diffcount_genescore.R
    Modified:   scripts/diffcount_genescore_gsea.R
    Modified:   scripts/fit_all_models_Cusanovich2018.sh
    Modified:   scripts/postfit_Buenrostro2018_Chen2019pipeline.sh
    Modified:   scripts/postfit_Cusanovich2018.sh
    Modified:   scripts/postfit_gene_analysis.sbatch
    Modified:   scripts/postfit_genescore.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/motif_analysis_Cusanovich2018_v2.Rmd) and HTML (docs/motif_analysis_Cusanovich2018_v2.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	33840df	kevinlkx	2022-02-16	updated with v2 of the DA analysis

---

Here we perform TF motif analysis for the Cusanovich et al (2018) scATAC-seq result inferred from the multinomial topic model with \(k = 13\).

Load packages and some functions used in this analysis

library(Matrix)
library(fastTopics)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(plotly)
library(htmlwidgets)
library(DT)
library(reshape2)
library(Logolas)
library(grid)
source("code/motif_analysis.R")
source("code/plots.R")

Load data and topic model results

Load the data and the \(k = 13\) Poisson NMF fit results.

data.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/data/Cusanovich_2018/processed_data/"
load(file.path(data.dir, "Cusanovich_2018.RData"))
cat(sprintf("%d x %d counts matrix.\n",nrow(counts),ncol(counts)))
rm(counts)

# 81173 x 436206 counts matrix.

fit.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018"
fit <- readRDS(file.path(fit.dir, "/fit-Cusanovich2018-scd-ex-k=13.rds"))$fit
fit <- poisson2multinom(fit)

Visualize by Structure plot grouped by tissues

set.seed(10)
colors_topics <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
                   "#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928",
                   "gray")
rows <- sample(nrow(fit$L),4000)
samples$tissue <- as.factor(samples$tissue)

p.structure <- structure_plot(select(fit,loadings = rows),
                     grouping = samples[rows, "tissue"], n = Inf,gap = 40,
                     perplexity = 50,colors = colors_topics,
                     num_threads = 4,verbose = FALSE)

print(p.structure)

Differential accessbility analysis of the ATAC-seq regions for the topics

Load results from differential accessbility analysis for the topics

out.dir <- "/project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2"
cat(sprintf("Load results from %s \n", out.dir))
DA_res <- readRDS(file.path(out.dir, paste0("DAanalysis-Cusanovich2018-k=13-quantile/DA_regions_topics_10000iters.rds")))

# Load results from /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2

Volcano plot of the regions

Topic 1 and topic 4 examples

p.volcano.1 <- volcano_plot(DA_res,k = 1, labels = rep("",nrow(DA_res$z)))

p.volcano.4 <- volcano_plot(DA_res,k = 4, labels = rep("",nrow(DA_res$z)))

plot_grid(p.volcano.1, p.volcano.4)

Motif enrichment analysis using HOMER

• Details about HOMER motif analysis:
• http://homer.ucsd.edu/homer/motif/

• http://homer.ucsd.edu/homer/ngs/peakMotifs.html

• Motif enrichment result using regions with z-score above 99% quantile.

Compile Homer results across topics

homer.dir <- paste0(out.dir, "/motifanalysis-Cusanovich2018-k=13-quantile/HOMER")
cat(sprintf("Directory of motif analysis result: %s \n", homer.dir))
homer_res_topics <- readRDS(file.path(homer.dir, "/homer_knownResults.rds"))
selected_regions <- readRDS(file.path(homer.dir, "/selected_regions.rds"))

# Compile Homer results (pvalue and ranking) across topics
motif_res <- compile_homer_motif_res(homer_res_topics)
saveRDS(motif_res, paste0(homer.dir, "/homer_motif_enrichment_results.rds"))
cat("compiled homer motif results are saved in", paste0(homer.dir, "/homer_motif_enrichment_results.rds"))

motif_table <- data.frame(motif = gsub("/.*", "", rownames(motif_res$mlog10P)),
                          round(motif_res$mlog10P,2))
DT::datatable(motif_table, rownames = F, caption = "Motif enrichment (-log10P)")

# Directory of motif analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER 
# compiled homer motif results are saved in /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/motifanalysis-Cusanovich2018-k=13-quantile/HOMER/homer_motif_enrichment_results.rds

Top 10 motifs in each topic

cat("Number of regions selected for each topic: \n")
print(mapply(nrow, selected_regions[1:(length(selected_regions)-1)]))

colnames_homer <- c("motif_name", "consensus", "P", "log10P", "Padj",  "num_target", "percent_target", "num_bg", "percent_bg")

top_motifs <- data.frame(matrix(nrow=10, ncol = length(homer_res_topics)))
colnames(top_motifs) <- names(homer_res_topics)
for (k in 1:length(homer_res_topics)){
  homer_res <- homer_res_topics[[k]]
  colnames(homer_res) <- colnames_homer
  homer_res <- homer_res %>% separate(motif_name, c("motif", "origin", "database"), "/")
  top_motifs[,k] <- head(homer_res$motif, 10)
}

DT::datatable(data.frame(rank = 1:10, top_motifs), rownames = F, caption = "Top 10 motifs enriched in each topic.")

# Number of regions selected for each topic: 
#   k1   k2   k3   k4   k5   k6   k7   k8   k9  k10  k11  k12  k13 
# 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137 4137

Heatmap of motif enrichment across topics

Heatmap of motif enrichment -log10(p-value).

create_motif_enrichment_heatmap(motif_res, enrichment = "-log10(p-value)", 
                                cluster_motifs = TRUE, cluster_topics = TRUE, motif_filter = 10, horizontal = FALSE,
                                enrichment_range = c(0,100), method_cluster = "average", font.size.motifs = 4, font.size.topics = 9)

# 250 out of 439 motifs included the heatmap

Scatterplots of motif enrichment

Plot motif enrichment (-log10 p-value) and the ranking

# Plot enrichment (-log10 p-value) and ranking of the motifs

plots <- vector("list", ncol(motif_res$mlog10P))
names(plots) <- colnames(motif_res$mlog10P)

for( i in 1:length(plots)){
  plots[[i]] <- create_motif_enrichment_ranking_plot(motif_res, k = i, 
                                                     max.overlaps = 20, subsample = FALSE)
}

do.call(plot_grid,plots)

Topic 1 example

print(plots[[1]])

Motif enrichment vs gene score

Load pre-computed gene scores

gene.dir <- paste0(out.dir, "/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2")
cat(sprintf("Directory of gene analysis result: %s \n", gene.dir))
genescore_res <- readRDS(file.path(gene.dir, "genescore_result.rds"))

# Directory of gene analysis result: /project2/mstephens/kevinluo/scATACseq-topics/output/Cusanovich_2018/postfit_v2/geneanalysis-Cusanovich2018-k=13-TSS-absZ-l2

Get TF genes

motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
common_genes <- intersect(toupper(motif_names), toupper(gene_names))
cat(sprintf("%s TF genes mapped between motif names and gene symbol. \n", length(common_genes)))

motif_gene_table <- data.frame(motif = motif_names[match(common_genes, toupper(motif_names))], 
                                      gene = gene_names[match(common_genes, toupper(gene_names))])

# 247 TF genes mapped between motif names and gene symbol.

Compute correlation between motif enrichment z-score and gene score:

Topic 1 example

• Compute motif enrichment z-scores from the motif enrichment p-values
• Plot motif enrichment (-log10 p-value) and correlation between motif enrichment z-scores and gene scores
• Rank motifs by motif enrichment (-log10 p-value) and correlation between motif enrichment z-score and gene scores

motif_gene_mapping <- create_motif_gene_cor_scatterplot(motif_res, genescore_res, motif_gene_table, 
                                                        k = 1, cor.motif = "z-score")

motif_gene_mapping <- motif_gene_mapping[with(motif_gene_mapping, order(motif_mlog10P*cor_zscore, decreasing = T)),]
rownames(motif_gene_mapping) <- 1:nrow(motif_gene_mapping)

cat("Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: \n")
print(head(motif_gene_mapping[,c("motif","motif_mlog10P", "gene_score", "cor_zscore")], 10))

# Top 10 motifs by motif enrichment (-log10 p-value) and correlation to gene scores: 
#          motif motif_mlog10P gene_score cor_zscore
# 1    Gata2(Zf)     405.97848  2.6620405  0.6191297
# 2    Gata4(Zf)     402.37384  0.1782368  0.1705583
# 3    Gata1(Zf)     405.76133 13.0433214  0.1630853
# 4    Gata6(Zf)     415.88040  0.3492756  0.1087562
# 5      Sp2(Zf)      45.34034  6.2994971  0.4073351
# 6     KLF3(Zf)      43.77688  6.4292462  0.4064971
# 7     KLF1(Zf)      53.93937 20.9038040  0.3298559
# 8     KLF5(Zf)      51.33361  0.1760143  0.3047540
# 9     KLF6(Zf)      43.41642  0.2608836  0.3209959
# 10 Bach1(bZIP)      24.38998  9.9906945  0.4985966

GATA family

motif_names <- motif_res$motifs$motif
gene_names <- genescore_res$genes$SYMBOL
TF_motifs <- sort(unique(grep("^GATA\\d*$", motif_names, ignore.case=T, value=T)))
TF_genes <- sort(unique(grep("^GATA\\d*$", gene_names, ignore.case=T, value=T)))
common_genes <- intersect(toupper(TF_motifs), toupper(TF_genes))

motif_gene_table <- data.frame(motif = TF_motifs[match(common_genes, toupper(TF_motifs))], 
                                      gene = TF_genes[match(common_genes, toupper(TF_genes))])
print(motif_gene_table)

#   motif  gene
# 1 Gata1 Gata1
# 2 Gata2 Gata2
# 3 GATA3 Gata3
# 4 Gata4 Gata4
# 5 Gata6 Gata6

Plot GATA motifs in topic 1

# Plot GATA motifs in topic 1
k = 1
motif_order <- order(motif_res$mlog10P[,k], decreasing = T)
motifs <- rownames(motif_res$motifs[motif_order,])
motif_names <- motif_res$motifs[motif_order, "motif"]
selected_motifs <- unique(motifs[match(toupper(motif_gene_table$motif), toupper(motif_names))])
motif.dir <- paste0(homer.dir, "/homer_result_topic_", k, "/knownResults/")

for (i in 1:length(selected_motifs)){
  plot_motif_logo(homer_res_topics, selected_motifs[i], k, motif.dir, type = "both")
}

• Plot motif enrichment (-log10 p-value) and gene scores

plots <- create_motif_gene_scatterplot(motif_res, genescore_res, 
                                       motif_gene_table,
                                       k = 1, 
                                       y = "-log10(p-value)", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

• Plot motif enrichment (zscore) and gene scores

plots <- create_motif_gene_scatterplot(motif_res, genescore_res, 
                                       motif_gene_table,
                                       k = 1, 
                                       y = "z-score", 
                                       colors = colors_topics,
                                       max.overlaps = 10)

do.call(plot_grid,plots)

Session information

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] grid      stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
#  [1] Logolas_1.3.1     reshape2_1.4.4    DT_0.20           htmlwidgets_1.5.4
#  [5] plotly_4.10.0     cowplot_1.1.1     ggrepel_0.9.1     ggplot2_3.3.5    
#  [9] tidyr_1.1.4       dplyr_1.0.7       fastTopics_0.6-97 Matrix_1.4-0     
# [13] workflowr_1.7.0  
# 
# loaded via a namespace (and not attached):
#   [1] Rtsne_0.15           colorspace_2.0-2     seqinr_4.2-8        
#   [4] ellipsis_0.3.2       class_7.3-20         rprojroot_2.0.2     
#   [7] fs_1.5.2             rstudioapi_0.13      farver_2.1.0        
#  [10] listenv_0.8.0        MatrixModels_0.5-0   bit64_4.0.5         
#  [13] prodlim_2019.11.13   fansi_1.0.2          lubridate_1.8.0     
#  [16] codetools_0.2-18     splines_4.0.4        knitr_1.37          
#  [19] ade4_1.7-18          jsonlite_1.7.3       pROC_1.18.0         
#  [22] mcmc_0.9-7           caret_6.0-90         gridBase_0.4-7      
#  [25] Rmpfr_0.8-7          ashr_2.2-47          uwot_0.1.11         
#  [28] compiler_4.0.4       httr_1.4.2           assertthat_0.2.1    
#  [31] fastmap_1.1.0        lazyeval_0.2.2       cli_3.1.1           
#  [34] later_1.3.0          prettyunits_1.1.1    htmltools_0.5.2     
#  [37] quantreg_5.86        tools_4.0.4          gmp_0.6-2.1         
#  [40] coda_0.19-4          gtable_0.3.0         glue_1.6.1          
#  [43] Rcpp_1.0.8           jquerylib_0.1.4      vctrs_0.3.8         
#  [46] ape_5.6-1            nlme_3.1-155         conquer_1.2.1       
#  [49] crosstalk_1.2.0      iterators_1.0.13     timeDate_3043.102   
#  [52] CVXR_1.0-10          gower_0.2.2          xfun_0.29           
#  [55] stringr_1.4.0        globals_0.14.0       ps_1.6.0            
#  [58] lifecycle_1.0.1      irlba_2.3.5          future_1.23.0       
#  [61] getPass_0.2-2        MASS_7.3-55          scales_1.1.1        
#  [64] ipred_0.9-12         hms_1.1.1            promises_1.2.0.1    
#  [67] parallel_4.0.4       SparseM_1.81         yaml_2.2.2          
#  [70] pbapply_1.5-0        sass_0.4.0           rpart_4.1-15        
#  [73] stringi_1.7.6        SQUAREM_2021.1       highr_0.9           
#  [76] foreach_1.5.1        lava_1.6.10          truncnorm_1.0-8     
#  [79] rlang_1.0.0          pkgconfig_2.0.3      matrixStats_0.61.0  
#  [82] evaluate_0.14        lattice_0.20-45      invgamma_1.1        
#  [85] purrr_0.3.4          labeling_0.4.2       recipes_0.1.17      
#  [88] bit_4.0.4            processx_3.5.2       tidyselect_1.1.1    
#  [91] parallelly_1.30.0    plyr_1.8.6           magrittr_2.0.2      
#  [94] R6_2.5.1             generics_0.1.1       DBI_1.1.2           
#  [97] pillar_1.6.5         whisker_0.4          withr_2.4.3         
# [100] survival_3.2-13      mixsqp_0.3-43        nnet_7.3-17         
# [103] tibble_3.1.6         future.apply_1.8.1   crayon_1.4.2        
# [106] utf8_1.2.2           rmarkdown_2.11       progress_1.2.2      
# [109] data.table_1.14.2    callr_3.7.0          git2r_0.29.0        
# [112] ModelMetrics_1.2.2.2 digest_0.6.29        httpuv_1.6.5        
# [115] MCMCpack_1.6-0       RcppParallel_5.1.5   stats4_4.0.4        
# [118] munsell_0.5.0        viridisLite_0.4.0    bslib_0.3.1         
# [121] quadprog_1.5-8