From 418da2e9411249f9636e8bd932eda675928b1257 Mon Sep 17 00:00:00 2001
From: MikeDMorgan <michael.morgan@abdn.ac.uk>
Date: Wed, 8 Nov 2023 15:31:56 +0000
Subject: [PATCH] update version

---
 DESCRIPTION             |  3 ++-
 man/plotDAbeeswarm.Rd   |  3 ++-
 man/plotNhoodGraph.Rd   |  2 +-
 man/plotNhoodGraphDA.Rd | 14 +-------------
 man/plotNhoodMA.Rd      |  4 +++-
 5 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d3a1a2a..ebb85d8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,9 +1,10 @@
 Package: miloR
 Type: Package
 Title: Differential neighbourhood abundance testing on a graph
-Version: 1.9.1
+Version: 1.9.99
 Authors@R:
     c(person("Mike", "Morgan", role=c("aut", "cre"), email="michael.morgan@abdn.ac.uk"),
+    comment=c(ORCID="0000-0003-0757-0711"),
     person("Emma", "Dann", role=c("aut", "ctb"), email="ed6@sanger.ac.uk"))
 Description: Milo performs single-cell differential abundance testing. Cell states are modelled 
     as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a 
diff --git a/man/plotDAbeeswarm.Rd b/man/plotDAbeeswarm.Rd
index b3b0ce5..0e6bcc3 100644
--- a/man/plotDAbeeswarm.Rd
+++ b/man/plotDAbeeswarm.Rd
@@ -23,7 +23,8 @@ If \code{da.res[,group.by]} is a character or a numeric, the function will coerc
 a \code{ggplot} object
 }
 \description{
-Visualize DA results as a beeswarm plot
+This function constructs a beeswarm plot using the ggplot engine to visualise the distribution of
+log fold changes across neighbourhood annotations.
 }
 \details{
 The group.by variable will be coerced to a factor. If you want the variables in group.by to be
diff --git a/man/plotNhoodGraph.Rd b/man/plotNhoodGraph.Rd
index 1df29a8..6d151df 100644
--- a/man/plotNhoodGraph.Rd
+++ b/man/plotNhoodGraph.Rd
@@ -23,7 +23,7 @@ plotNhoodGraph(
 \item{colour_by}{this can be a data.frame of milo results or a character corresponding to a column in colData}
 
 \item{subset.nhoods}{A logical, integer or character vector indicating a subset of nhoods to show in plot
-(default: NULL, no subsetting)}
+(default: NULL, no subsetting). This is necessary if \code{testNhoods} was run using \code{subset.nhoods=...}.}
 
 \item{size_range}{a numeric vector indicating the range of node sizes to use for plotting (to avoid overplotting
 in the graph)}
diff --git a/man/plotNhoodGraphDA.Rd b/man/plotNhoodGraphDA.Rd
index 0301c65..90b187b 100644
--- a/man/plotNhoodGraphDA.Rd
+++ b/man/plotNhoodGraphDA.Rd
@@ -2,43 +2,31 @@
 % Please edit documentation in R/plotNhoods.R
 \name{plotNhoodGraphDA}
 \alias{plotNhoodGraphDA}
-\alias{plotNhoodGroups}
 \title{Plot Milo results on graph of neighbourhood}
 \usage{
 plotNhoodGraphDA(x, milo_res, alpha = 0.05, res_column = "logFC", ...)
-
-plotNhoodGroups(x, milo_res, show_groups = NULL, ...)
 }
 \arguments{
 \item{x}{A \code{\linkS4class{Milo}} object}
 
-\item{milo_res}{a data.frame of milo results containing the \code{nhoodGroup} column}
+\item{milo_res}{a data.frame of milo results}
 
 \item{alpha}{significance level for Spatial FDR (default: 0.05)}
 
 \item{res_column}{which column of \code{milo_res} object to use for color (default: logFC)}
 
 \item{...}{arguments to pass to \code{plotNhoodGraph}}
-
-\item{show_groups}{a character vector indicating which groups to plot
-all other neighbourhoods will be gray}
 }
 \value{
-a \code{ggplot} object
-
 a \code{ggplot} object
 }
 \description{
 Visualize log-FC estimated with differential nhood abundance testing
 on embedding of original single-cell dataset.
-
-Visualize grouping of neighbourhoods obtained with \code{groupNhoods}
 }
 \examples{
 NULL
 
-NULL
-
 }
 \author{
 Emma Dann
diff --git a/man/plotNhoodMA.Rd b/man/plotNhoodMA.Rd
index 0be3ae8..800ca6f 100644
--- a/man/plotNhoodMA.Rd
+++ b/man/plotNhoodMA.Rd
@@ -18,7 +18,9 @@ hypothesis. \code{default=0}.}
 a \code{ggplot} object
 }
 \description{
-Visualize DA results as an MAplot
+Make an MAplot to visualise the relationship between DA log fold changes and neighbourhood abundance. This
+is a useful way to diagnose issues with the DA testing, such as large compositional biases and/or issues
+relating to large imbalances in numbers of cells between condition labels/levels.
 }
 \details{
 MA plots provide a useful means to evaluate the distribution of log fold changes after differential