updated README rendering action

.github/workflows/render-README.yaml

@@ -21,7 +21,7 @@ jobs:
       - uses: r-lib/actions/setup-r-dependencies@v2
       - uses: r-lib/actions/setup-pandoc@v2
       - name: install CRAN packages
-        run: Rscript -e 'install.packages(c("rmarkdown","ggplot2", "dplyr", "purrr", "remotes", "devtools", "BiocManager", "Seurat"), force = TRUE)'
+        run: Rscript -e 'install.packages(c("rmarkdown", "ggplot2", "dplyr", "purrr", "remotes", "devtools", "BiocManager", "Seurat"), force = TRUE)'
       - name: Extra dependencies
         id: extra-deps
         run: |
@@ -36,6 +36,8 @@ jobs:
         run: Rscript -e 'remotes::install_github("jr-leary7/scLANE")'
       - name: render README
         run: Rscript -e 'rmarkdown::render("README.Rmd", output_format = "github_document", output_file = "README.md")'
+      - name: clean up README.md 
+        run: Rscript -e 'lines <- readLines("README.md"); lines <- lines[!grepl("^====+$", lines)]; writeLines(lines, "README.md")'
       - name: commit rendered README
         run: |
           git config --local user.name "jr-leary7"

README.Rmd

@@ -34,15 +34,15 @@ knitr::opts_chunk$set(warning = FALSE,
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 <!-- badges: end -->
 
-## Installation
+# Installation
 
 You can install the most recent version of `scLANE` using:
 
 ```{r install-scLANE, eval=FALSE}
 remotes::install_github("jr-leary7/scLANE")
 ```
 
-## Model structure
+# Model structure
 
 The `scLANE` package enables users to accurately determine differential expression of genes over pseudotime or latent time, and to characterize gene's dynamics using interpretable model coefficients. `scLANE` builds upon the `marge` modeling framework([GitHub](https://github.com/JakubStats/marge), [paper](https://doi.org/10.1080/10618600.2017.1360780)), allowing users to characterize their trajectory's effects on gene expression using negative binomial GLMs, GEEs, or GLMMs depending on the experimental design & biological questions of interest. This modeling framework is an extension of the [Multivariate Adapative Regression Splines (MARS)](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline) method, which builds nonlinear models out of piecewise linear components. `scLANE` is agnostic with respect to the ordering estimation method used, and can be implemented downstream of any pseudotime or RNA velocity method. 
 

README.md

@@ -1,5 +1,5 @@
 <img src='man/figures/scLANE_logo.png' align='right' height='20%' width='20%'/>
-================
+
 
 - [Installation](#installation)
 - [Model structure](#model-structure)
@@ -28,15 +28,15 @@ commit](https://img.shields.io/github/last-commit/jr-leary7/scLANE/main?color=da
 MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 <!-- badges: end -->
 
-## Installation
+# Installation
 
 You can install the most recent version of `scLANE` using:
 
 ``` r
 remotes::install_github("jr-leary7/scLANE")
 ```
 
-## Model structure
+# Model structure
 
 The `scLANE` package enables users to accurately determine differential
 expression of genes over pseudotime or latent time, and to characterize
@@ -171,7 +171,7 @@ scLANE_models_glm <- testDynamic(sim_data,
 #> Registered S3 method overwritten by 'bit':
 #>   method   from  
 #>   print.ri gamlss
-#> scLANE testing in GLM mode completed for 100 genes across 1 lineage in 26.604 secs
+#> scLANE testing in GLM mode completed for 100 genes across 1 lineage in 26.593 secs
 ```
 
 After the function finishes running, we use `getResultsDE()` to generate
@@ -218,7 +218,7 @@ scLANE_models_gee <- testDynamic(sim_data,
                                  cor.structure = "ar1", 
                                  n.cores = 6L, 
                                  verbose = FALSE)
-#> scLANE testing in GEE mode completed for 100 genes across 1 lineage in 1.499 mins
+#> scLANE testing in GEE mode completed for 100 genes across 1 lineage in 1.503 mins
 ```
 
 We again generate the table of DE test results. The variance of the
@@ -264,7 +264,7 @@ scLANE_models_glmm <- testDynamic(sim_data,
                                   id.vec = sim_data$subject, 
                                   n.cores = 6L, 
                                   verbose = FALSE)
-#> scLANE testing in GLMM mode completed for 100 genes across 1 lineage in 3.386 mins
+#> scLANE testing in GLMM mode completed for 100 genes across 1 lineage in 3.314 mins
 ```
 
 **Note:** The GLMM mode is still under development, as we are working on