Fixed legend p<0.05

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: EnrichIntersect
 Type: Package
 Title: Enrichment Analysis and Intersecting Sankey Diagram
-Version: 0.6
-Date: 2023-02-24
+Version: 0.7
+Date: 2024-03-14
 URL: https://github.com/ocbe-uio/EnrichIntersect
 BugReports: https://github.com/ocbe-uio/EnrichIntersect/issues
 Authors@R: c(person("Zhi", "Zhao", email = "zhi.zhao@medisin.uio.no", role = c("aut","cre")), person("Manuela", "Zucknick", email = "manuela.zucknick@medisin.uio.no", role = c("aut")), person("Tero", "Aittokallio", email = "t.a.aittokallio@medisin.uio.no", role = c("ctb")))
@@ -17,7 +17,7 @@ Suggests: knitr
 LazyData: true
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2023-02-24 22:30:53 UTC; zhiz
+Packaged: 2024-03-14 15:18:31 UTC; zhiz
 Author: Zhi Zhao [aut, cre],
   Manuela Zucknick [aut],
   Tero Aittokallio [ctb]

NEWS.md

@@ -1,3 +1,8 @@
+# EnrichIntersect 0.7
+
+- Fixed issue with the legend of p<0.05
+- Added REAMD.md
+
 # EnrichIntersect 0.6
 
 - Automatically filter out non-edge data from the input object of `intersectSankey()`

R/enrichment.R

@@ -227,15 +227,18 @@ enrichment <- function(x, custom.set, alpha = 0, normalize = TRUE,
       guides(colour = guide_legend(override.aes = list(size = 5))) +
       scale_size(name = ES.name, range = c(1, 5), breaks = c(0, 1, 2, 3), 
                  guide = guide_legend(keyheight = .8)) +
-      theme(axis.text.x = element_text(size = 8, angle = angle, 
-                                       vjust = 1, hjust = 1)) +
+      theme(axis.text.x = 
+              element_text(size = 8, angle = angle, vjust = 1, hjust = 1), 
+            legend.margin = margin(-0.1,0,0,0, unit="cm")) +
       xlab("") + ylab("")
 
     if (pvalue.cutoff == 0.05) {
-      g <- g + scale_color_manual(name = NULL, values = c("p<0.05" = "red"))
+      g <- g + scale_color_manual(name = NULL, 
+                                  values = c(`red` = "red"), labels = "p<0.05")
     } else {
       if (pvalue.cutoff == 0.1) {
-        g <- g + scale_color_manual(name = NULL, values = c("p<0.1" = "red"))
+        g <- g + scale_color_manual(name = NULL, 
+                                    values = c(`red` = "red"), labels = "p<0.1")
       } else {
         g <- g + scale_color_manual(name = NULL, values = c("gray", "red"), 
                                     labels = paste0("p", c(">=", "<"), 

README.md

@@ -4,18 +4,6 @@
 
 This is a flexible R package for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. EnrichIntersect also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model ([Zhao et al., 2022](https://doi.org/10.1016/j.isci.2022.104767)) or similar methods. A user-manual description of features, function calls and examples can be found from the [vignette](https://cran.r-project.org/web/packages/EnrichIntersect/vignettes/EnrichIntersect.html) of our package.
 
-## Citation
-
-> Zhi Zhao, Manuela Zucknick, Tero Aittokallio (2022).
-> EnrichIntersect: an R package for custom set enrichment analysis and
-> interactive visualization of intersecting sets. _Bioinformatics Advances_,
-> vbac073. DOI: [10.1093/bioadv/vbac073](https://doi.org/10.1093/bioadv/vbac073).
-
-> Zhi Zhao, Shixiong Wang, Manuela Zucknick, Tero Aittokallio (2022).
-> Tissue-specific identification of multi-omics features for pan-cancer
-> drug response prediction. _iScience_, 25(8): 104767. DOI:
-> [10.1016/j.isci.2022.104767](https://doi.org/10.1016/j.isci.2022.104767).
-
 ## Installation
 
 Install the latest released version from [CRAN](https://CRAN.R-project.org/package=EnrichIntersect)
@@ -46,7 +34,7 @@ set.seed(123)
 enrich <- enrichment(x, custom.set, permute.n = 5)
 ```
 
-![](https://github.com/zhizuio/EnrichIntersect/blob/main/README_plot_enrich.png)<!-- -->
+<img src="man/figures/plot_enrich.png" width="90%" />
 
 ### Plot Sankey diagram for intersecting set through an array
 
@@ -57,4 +45,18 @@ data(cancers_genes_drugs, package = "EnrichIntersect")
 intersectSankey(cancers_genes_drugs, step.names = c("Cancers","Genes","Drugs"))
 ```
 
-![](https://github.com/zhizuio/EnrichIntersect/blob/main/README_plot_sankey.png)<!-- -->
+<img src="man/figures/plot_sankey.png" width="90%" />
+
+
+
+## Citation
+
+> Zhi Zhao, Manuela Zucknick, Tero Aittokallio (2022).
+> EnrichIntersect: an R package for custom set enrichment analysis and
+> interactive visualization of intersecting sets. _Bioinformatics Advances_,
+> vbac073. DOI: [10.1093/bioadv/vbac073](https://doi.org/10.1093/bioadv/vbac073).
+
+> Zhi Zhao, Shixiong Wang, Manuela Zucknick, Tero Aittokallio (2022).
+> Tissue-specific identification of multi-omics features for pan-cancer
+> drug response prediction. _iScience_, 25(8): 104767. DOI:
+> [10.1016/j.isci.2022.104767](https://doi.org/10.1016/j.isci.2022.104767).

inst/CITATION

@@ -1,7 +1,5 @@
-citHeader("To cite EnrichIntersect in publications use:")
-
-citEntry(entry = "Article",
-  title        = "EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets",
+bibentry(bibtype = "Article",
+  title        = "{EnrichIntersect}: an {R} package for custom set enrichment analysis and interactive visualization of intersecting sets",
   author       = c(person(given = "Zhi",
                           family = "Zhao",
                           email = "zhi.zhao@medisin.uio.no"),
@@ -16,38 +14,6 @@ citEntry(entry = "Article",
   volume       = "2",
   number       = "1",
   pages        = "vbac073",
-  doi          = "10.1093/bioadv/vbac073",
-  textVersion  =
-  paste("Zhi Zhao, Manuela Zucknick, Tero Aittokallio (2022).",
-        "EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets.",
-        "Bioinformatics Advances, 2(1), vbac073.",
-        "doi: 10.1093/bioadv/vbac073.")
-)
-
-citEntry(entry = "Article",
-  title        = "Tissue-specific identification of multi-omics features for pan-cancer drug response prediction",
-  author       = c(person(given = "Zhi",
-                          family = "Zhao",
-                          email = "zhi.zhao@medisin.uio.no"),
-                   person(given = "Shixiong",
-                          family = "Wang",
-                          email = "shixiong.wang@rr-research.no"),
-                   person(given = "Manuela",
-                          family = "Zucknick",
-                          email = "manuela.zucknick@medisin.uio.no"),
-                   person(given = "Tero",
-                          family = "Aittokallio",
-                          email = "t.a.aittokallio@medisin.uio.no")),
-  journal      = "iScience",
-  year         = "2022",
-  volume       = "25",
-  number       = "8",
-  pages        = "104767",
-  doi          = "10.1016/j.isci.2022.104767",
-  textVersion  =
-  paste("Zhi Zhao and Shixiong Wang and Manuela Zucknick and Tero Aittokallio (2022).",
-        "Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.",
-        "iScience, 25(8), 104767.",
-        "doi: 10.1016/j.isci.2022.104767."),
-  header       = "If mix-lasso is used, please also cite:"
+  doi          = "10.1093/bioadv/vbac073",
+  header       = "To cite EnrichIntersect in publications use:"
 )

inst/doc/EnrichIntersect.R

@@ -1,10 +1,10 @@
 ## ----setup, include=FALSE-----------------------------------------------------
 knitr::opts_chunk$set(echo = TRUE)
 
-## ---- eval=TRUE---------------------------------------------------------------
+## ----eval=TRUE----------------------------------------------------------------
 library("EnrichIntersect")
 
-## ----enrichment_map, results="hide", fig.width=7, fig.height=5----------------
+## ----enrichment_map, results="hide", fig.width=7.2, fig.height=5--------------
 x <- cancers_drug_groups$score
 custom.set <- cancers_drug_groups$custom.set
 set.seed(123)

inst/doc/EnrichIntersect.Rmd

@@ -28,7 +28,7 @@ library("EnrichIntersect")
 
 An example input data object `cancers_drug_groups` is an R `list` provided in our package, which includes a `data.frame` object with 147 cancer drugs as rows and nine cancer types as columns, and another `data.frame` that groups the 147 drugs (second column) into nine user-defined drug classes (first column). The default setup of `enrichment()` uses the classic K-S test statistic to calculate the normalized enrichment score that quantifies the degree to which the features in a feature set are over-represented at the top or bottom of the entire ranked list of features (e.g. a list of drugs), using as default 100 permutations for the empirical null test statistic. In the visualization, the statistically significantly enriched feature sets are marked with red coloured circles given a pre-specified significance level. The pre-specified significance level will be adjusted automatically if argument `padj.method` is one of `c("holm","hochberg","hommel","bonferroni","BH","BY","fdr","none")`. Users can specify the argument `alpha` for calculating a weighted enrichment score, argument `normalize=FALSE` for using the standard enrichment score rather than the normalized score, argument `permute.n` for the number of permutations of the ranked feature list used for estimating the empirical null test statistic, and the argument `pvalue.cutoff` for marking enriched categories at a specific significance level. See the following code for an example.
 
-```{r enrichment_map, results="hide", fig.width=7, fig.height=5}
+```{r enrichment_map, results="hide", fig.width=7.2, fig.height=5}
 x <- cancers_drug_groups$score
 custom.set <- cancers_drug_groups$custom.set
 set.seed(123)

vignettes/EnrichIntersect.Rmd

@@ -28,7 +28,7 @@ library("EnrichIntersect")
 
 An example input data object `cancers_drug_groups` is an R `list` provided in our package, which includes a `data.frame` object with 147 cancer drugs as rows and nine cancer types as columns, and another `data.frame` that groups the 147 drugs (second column) into nine user-defined drug classes (first column). The default setup of `enrichment()` uses the classic K-S test statistic to calculate the normalized enrichment score that quantifies the degree to which the features in a feature set are over-represented at the top or bottom of the entire ranked list of features (e.g. a list of drugs), using as default 100 permutations for the empirical null test statistic. In the visualization, the statistically significantly enriched feature sets are marked with red coloured circles given a pre-specified significance level. The pre-specified significance level will be adjusted automatically if argument `padj.method` is one of `c("holm","hochberg","hommel","bonferroni","BH","BY","fdr","none")`. Users can specify the argument `alpha` for calculating a weighted enrichment score, argument `normalize=FALSE` for using the standard enrichment score rather than the normalized score, argument `permute.n` for the number of permutations of the ranked feature list used for estimating the empirical null test statistic, and the argument `pvalue.cutoff` for marking enriched categories at a specific significance level. See the following code for an example.
 
-```{r enrichment_map, results="hide", fig.width=7, fig.height=5}
+```{r enrichment_map, results="hide", fig.width=7.2, fig.height=5}
 x <- cancers_drug_groups$score
 custom.set <- cancers_drug_groups$custom.set
 set.seed(123)