minor fixes

README.md

@@ -9,7 +9,8 @@
 <div style="background: #dddddd;">
 
 >**NEWS** Oct., 2024
->- Checkout new cell factor alignment method (function [`centroidAlign()`](https://welch-lab.github.io/liger/reference/centroidAlign.html)), which aligns cell factor loading by moving soft clustering centroids. This method is benchmarked with [*scib*](https://scib.readthedocs.io/en/latest/index.html) datasets and metrics and shows better overall performance than our previous quantile normalization method, especially for the ability to conserve biological information.
+>
+>- Checkout new cell factor alignment method (function [`centroidAlign()`](https://welch-lab.github.io/liger/reference/centroidAlign.html)), which aligns cell factor loading by moving soft clustering centroids. Its overall performance, in terms of batch effect removal and especially biological information conservation, out performs many public well-known methods. [**See benchmarking article here**](https://welch-lab.github.io/liger/articles/benchmark.html).
 >- Checkout Consensus iNMF method (function [`runCINMF()`](https://welch-lab.github.io/liger/reference/runCINMF.html)), which runs regular iNMF multiple times with different random initialization and summarizes a consensus result with better confidence.
 >- Please visit [*rliger* website](https://welch-lab.github.io/liger/index.html) for comprehensive documentation and [revised tutorial](https://welch-lab.github.io/liger/articles/Integrating_multi_scRNA_data.html) that walks through scRNAseq integration and analysis in detail
 >- More [changelogs](https://welch-lab.github.io/liger/news/index.html)

vignettes/articles/Integrating_multi_scRNA_data.rmd

@@ -521,7 +521,7 @@ goResult[["CD14 Mono.stim"]]$result %>%
     mutate(
       p_value = signif(p_value, digits = 3),
     ) %>%
-    DT::datatable(options = list(pageLength = 5))
+    DT::datatable(options = list(pageLength = 5, scrollX = TRUE))
 ```
 
 And similarly, show the GO terms enriched within the down-regulated genes.
@@ -541,7 +541,7 @@ goResult[["CD14 Mono.stim"]]$result %>%
     mutate(
       p_value = signif(p_value, digits = 3),
     ) %>%
-    DT::datatable(options = list(pageLength = 5))
+    DT::datatable(options = list(pageLength = 5, scrollX = TRUE))
 ```
 
 Here's also a simple visualization function that generates a dot plot per test, with dot size representing the interaction size between query gene set and each term, and color representing the significance of the term. Use `group = "CD14 Mono.stim"` to specify the test group, and `query = "Up"` or `query = "Down"` to specify the if the test uses up- or down-regulated genes, respectively.