further updates on documentation and reduced content in README to make README slimmer and reduce redundancy. updated version to 0.2

Author: davidsebfischer

README.md

@@ -1,12 +1,8 @@
-
 # Fast and scalable differential expression analysis on single-cell RNA-seq data
 diffxpy covers a wide range of differential expression analysis scenarios encountered in single-cell RNA-seq scenarios. The core ideas of diffxpy is to speed up the model fitting time, which is the run-time bottle-neck of differential expression analysis on models that require maximum likelihood estimators and that do not have closed form solutions thereof. This model fitting is performed in a separate package, batchglm. diffxpy exposes wrapper funtions to the user which perform the model fitting and the differential expression test. Advanced users can chose the between various hypothesis tests and can vary the model fitting hyperparameters. Fitting can be performed both on CPUs and on GPUs and can be parallelized.
 
 # Installation
-1. Install [batchglm](https://github.com/theislab/batchglm).
-2. Clone the GitHub repository of diffxpy.
-3. cd into the clone.
-4. pip install -e .
+Please refer to the documentation
 
 # Worklows and API
 Diffxpy supports workflows related to differential expression analysis. The functionalities of the workflows are structured in the API. To access these workflows, import diffxpy as follows `import diffxpy.api as de`. Currently, the following analytic strategies are implemented:
@@ -15,12 +11,10 @@ Diffxpy supports workflows related to differential expression analysis. The func
 2. gene set enrichment analysis based on differential expression calls in `de.enrich.*`
 
 The aforementioned workflows can be concatenated in pipelines and results can be shared via diffxpy data structures as explained in the individual functions and in the examples.
+Refer to the documentation for details of the individual submodules.
 
-# Multiple tests per gene
-diffxpy provides infrastructure to perform multiple tests per gene as pairwise comparisons across more than two groups (`de.test.pairwise`, e.g. clusters of cells against each other), tests of each group against the rest (`de.test.versus_test`, e.g. clusters of cells against the rest) and by mapping a given differential test across each partition of a data set (`de.test.partition`, e.g. performing differential tests for treatment effects by a second experimental covariate or by cluster of cells).
-
-# Examples
-We provide usage example cases (vignettes) in the `examples/` directory. The diffxpy API distinguishes two cases: Single-tests and Mult-tests. Single-test wrappers perform a single tests per gene, such as a difference between two groups via a Wald test, a log-likelihood ratio test, a t-test or a Wilcoxon rank sum test or a log-likelihood ratio test for a more complex question. A multi-test wrapper performs multiple tests per gene: pairwise or 1-versus-rest tests. Both are a series of two-sample tests for each gene which can for example be used to explore the differences between louvain groups on the level of each gene. Pairwise tests perform one test for the difference between each pair of groups of cells for each gene (which can be significantly sped up if the test mode 'z-test' is used). 1-versus-rest tests test for the difference between each group and the other groups together for each gene. Examples for additional workflows like gene set enrichment analysis are also provided.
+# Tutorials
+We provide usage example cases (vignettes) in the `tutorials/` directory. These examples are described in the documentation.
 
 # Building the documentation
 The documentation is maintained in the `docs/` directory.

diffxpy/__init__.py

@@ -1 +1 @@
-__version__ = "0.1"
+__version__ = "0.2"

docs/api/index.rst

@@ -11,10 +11,15 @@ Differential expression tests: test
 -----------------------------------
 
 Run differential expression tests.
+diffxpy distinguishes between single tests and multi tests:
+Single tests perform a single hypothesis test for each gene whereas multi tests perform multiple tests per gene.
 
 Single tests per gene
 ~~~~~~~~~~~~~~~~~~~~~
 
+Single tests per gene are the standard differential expression scenario in which one p-value is computed per gene.
+diffxpy provies infrastructure for likelihood ratio tests, Wald tests, t-tests and Wilcoxon tests.
+
 .. autosummary::
    :toctree: .
 
@@ -27,6 +32,12 @@ Single tests per gene
 Multiple tests per gene
 ~~~~~~~~~~~~~~~~~~~~~~~
 
+diffxpy provides infrastructure to perform multiple tests per gene as:
+
+- pairwise: pairwise comparisons across more than two groups (`de.test.pairwise`, e.g. clusters of cells against each other)
+- versus_res:t tests of each group against the rest (`de.test.versus_test`, e.g. clusters of cells against the rest)
+- partition: mapping a given differential test across each partition of a data set (`de.test.partition`, e.g. performing differential tests for treatment effects by a second experimental covariate or by cluster of cells).
+
 .. autosummary::
    :toctree: .
 
@@ -38,6 +49,11 @@ Multiple tests per gene
 Gene set enrichment: enrich
 ---------------------------
 
+diffxpy provides infrastructure for gene set enrichment analysis downstream of differential expression analysis.
+Specifically, reference gene set annotation data sets can be loaded or created and can be compared to diffxpy objects 
+or results from other differential expression tests.
+
+
 Reference gene sets
 ~~~~~~~~~~~~~~~~~~~
 

setup.py

@@ -5,7 +5,7 @@
 setup(
     name='diffxpy',
     author=author,
-    author_email='florian.hoelzlwimmer@helmholtz-muenchen.de',
+    author_email='david.fischer@helmholtz-muenchen.de',
     packages=find_packages(),
     install_requires=[
         'numpy',