Added note output object compatibility

README.Rmd

@@ -71,6 +71,10 @@ Here is a concise list of software changes compared to the previous version. See
 * The option to flexibly change prioritization weights has been replaced by the option to select biological scenario’s. This reduces the number of parameters for the end users and limits unwanted tunability of end results.  
 * The standard interpretable bubble plot visualization has been extended and provides now information about cell-type specificity, fraction of expression, and curation effort of the ligand-receptor pairs according to Omnipath. As a result of this, this plot now summarizes all the criteria used for prioritization and gives an indication about the curation effort of the ligand-receptor prior knowledge. This can help users now to get better insights in their results and define better candidate interactions for follow-up validation. 
 
+### Notes
+
+* Some visualization options of multinichenetr version >= 2.0.0. are only compatible with MultiNicheNet output object generated by version multinichenetr version >= 2.0.0. Please use multinichenetr version >= 2.0.0. for buth running and interpreting the analysis. 
+
 ### Call for feedback
 
 multinichenetr is in ongoing development. We always appreciate it if you let us know how we can improve the software/documentation/algorithm/output visualizations further. The best way to do this is through the issues page: https://github.com/saeyslab/multinichenetr/issues
@@ -134,6 +138,7 @@ To help users in interpreting parameter values and output figures, we provide th
 
 * The input data needed for MultiNicheNet should be raw counts, and metadata of cells giving information about the sample, condition and cell type. In all vignettes, we assume that the data has been preprocessed adequately (proper cell filtering, doublet removal, ambient RNA correction,...).
 * We strongly recommend having at least 4 samples in each of the groups/conditions you want to compare. With less samples, the benefits of performing a pseudobulk-based DE analysis are less clear and non-multi-sample tools for differential cell-cell communication might be better alternatives. If you want to perform differential cell-cell communication with a MultiNicheNet-like prioritization framework, you can have a look at this vignette: [Differential cell-cell Communication for datasets with limited samples: "sample-agnostic/cell-level" MultiNicheNet](vignettes/basic_analysis_steps_MISC_SACL.knit.md). Just realize that the analysis is based on a limited number of samples, and it will be hard to draw strong conclusions. This may often be the best you can get out of your data, but it is not a practice we would recommend.
+* Visualization functions of multinichenetr v.2.0.0 require output objects created by multinichenetr v.2.0.0
 
 # References
 

README.md

@@ -152,6 +152,14 @@ these aspects.
     results and define better candidate interactions for follow-up
     validation.
 
+### Notes
+
+-   Some visualization options of multinichenetr version >= 2.0.0.
+    are only compatible with MultiNicheNet output object generated by
+    version multinichenetr version >= 2.0.0. Please use
+    multinichenetr version >= 2.0.0. for buth running and
+    interpreting the analysis.
+
 ### Call for feedback
 
 multinichenetr is in ongoing development. We always appreciate it if you
@@ -191,9 +199,8 @@ of downstream visualizations that can be created.
 ## Tutorials
 
 We recommend users to start with the following vignette, which
-demonstrates the different steps in the analysis without too many
-details yet. This is the recommended vignette to learn the basics of
-MultiNicheNet.
+demonstrates the different steps in the analysis and exploration of the
+output. This is the recommended vignette to learn MultiNicheNet.
 
 -   [**MultiNicheNet - comprehensive tutorial** - Condition A vs
     Condition B vs Condition
@@ -206,10 +213,11 @@ following vignettes demonstrate how to analyze cell-cell communication
 differences in other settings. These vignettes are the best vignettes to
 learn how to apply MultiNicheNet to different datastes for addressing
 different questions. To reduce the length of these vignettes, the
-sections on downstream analysis has been reduced strongly. So we
-strongly recommend to read these vignettes to learn how to perform the
-analysis in other settings, but still perform all additional analyses
-and checks as demonstrated in the comprehensive tutorial vignette .
+sections on downstream analysis has been reduced strongly and a wrapper
+function is sometimes used to perform the core analysis. So we strongly
+recommend to read these vignettes to learn how to perform the analysis
+in different settings, but still perform all additional analyses and
+checks as demonstrated in the comprehensive tutorial vignette.
 
 -   [Condition A vs Condition B - without repeated
     subjects](vignettes/pairwise_analysis_MISC.knit.md) | [*R Markdown
@@ -302,6 +310,8 @@ provide the following two files:
     samples, and it will be hard to draw strong conclusions. This may
     often be the best you can get out of your data, but it is not a
     practice we would recommend.
+-   Visualization functions of multinichenetr v.2.0.0 require output
+    objects created by multinichenetr v.2.0.0
 
 # References