From 704a92dc043785f6f1e81e6e28c9e602f5da415e Mon Sep 17 00:00:00 2001 From: Yichen Wang Date: Fri, 25 Oct 2024 16:10:07 -0400 Subject: [PATCH] minor fixes --- README.md | 3 ++- vignettes/articles/Integrating_multi_scRNA_data.rmd | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b2e59b3..fe1d214 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,8 @@
>**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) diff --git a/vignettes/articles/Integrating_multi_scRNA_data.rmd b/vignettes/articles/Integrating_multi_scRNA_data.rmd index ed4218b..ecbec28 100644 --- a/vignettes/articles/Integrating_multi_scRNA_data.rmd +++ b/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.