v1.0.2

Author: martinjzhang

docs/faq.rst

@@ -54,6 +54,20 @@ Computational complexity?
 scDRS scales linearly with the number of cells and number of control gene sets for both computation and memory use. It takes around 3 hours and 60GB for a single-cell data set with a million cells). Please see details in Methods in Zhang & Hou et al. Nat Genet 2022. 
 
 
+Which GWAS and scRNA-seq data should I use?
+======================================================
+
+To ensure a reasonable number of scDRS discoveries, we recommend using GWAS data with a heritability z-score greater than 5 or a sample size greater than 100K. We also recommend using scRNA-seq data with a diverse set of cells potentially relevant to disease, although a smaller number of cells should not affect the scDRS power.
+
+
+scDRS detected very few significant cells (FDR<0.2)?
+======================================================
+
+scDRS may be underpowered for certain GWAS/scRNA-seq data sets. In these cases, the ensuing scDRS group analyses may still have sufficient power, because scDRS group analyses aggregate results of individual cells and hence usually have higher power than the scDRS individual cell-level analyses. To assess if scDRS has sufficient power, we suggest visually inspecting the scDRS normalized disease score on the UMAP plot. If some parts of the UMAP plot are enriched with clearly higher scDRS disease scores, then scDRS should have detected interesting biological signals in your data. 
+
+scDRS may be underpowered for certain GWAS/scRNA-seq data sets. In these cases, the ensuing scDRS group analyses may still have sufficient power, because scDRS group analyses aggregate results of individual cells and hence usually have higher power than the scDRS individual cell-level analyses. To assess if scDRS has sufficient power, we suggest performing [`scdrs perform-downstream --group-analysis`](https://martinjzhang.github.io/scDRS/reference_cli.html#perform-downstream) to assess significance at an aggregated level. In addition, it is helpful to visually inspect the scDRS normalized disease score on the UMAP plot. If some parts of the UMAP plot are enriched with clearly higher scDRS disease scores, then scDRS should have detected interesting biological signals in your data.
+
+
 Use scDRS for other types of single-cell data?
 ====================================================
 

docs/index.rst

@@ -3,7 +3,7 @@ scDRS
 
 scDRS (single-cell disease-relevance score) is a method for associating individual cells in scRNA-seq data with disease GWASs, built on top of `AnnData <https://anndata.readthedocs.io/en/latest/>`_ and `Scanpy <https://scanpy.readthedocs.io/en/stable/>`_.
 
-Check out our manuscript `Zhang*, Hou*, et al. "Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data <https://www.biorxiv.org/content/10.1101/2021.09.24.461597v2>`_.
+Check out our manuscript `Zhang*, Hou*, et al. "Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data <https://www.nature.com/articles/s41588-022-01167-z>`_.
 
 Explore results for 74 diseases/traits and the TMS FACS data on `cellxgene <https://scdrs-tms-facs.herokuapp.com/>`_.
 
@@ -15,7 +15,7 @@ Installation
 
    git clone https://github.com/martinjzhang/scDRS.git
    cd scDRS
-   git checkout -b pub v1.0.1
+   git checkout -b v102 v1.0.2
    pip install -e .
    
 Quick test:
@@ -28,7 +28,7 @@ Install via `PyPI <https://pypi.org/project/scdrs/1.0.1/#description>`_
 
 .. code-block:: bash
 
-    pip install scdrs==1.0.1
+    pip install scdrs==1.0.2
     
 Quick test for PyPI installation: open Python (>=3.5) and run the code in the Usage section below.
 

docs/versions.rst

@@ -1,6 +1,22 @@
 Versions
 ========
 
+v1.0.2: publication (091222)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+   git clone https://github.com/martinjzhang/scDRS.git
+   cd scDRS 
+   git checkout -b v102 v1.0.2
+   pip install -e .
+   
+Quick test:
+
+.. code-block:: bash
+
+   python -m pytest tests/test_CLI.py -p no:warnings
+
 v1.0.1: publication (072222)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

scdrs/version.py

@@ -2,5 +2,5 @@
 # 1) we don't load dependencies by storing it in __init__.py
 # 2) we can import it in setup.py for the same reason
 # 3) we can import it into your module module
-__version__ = '1.0.1'
+__version__ = '1.0.2'
 __version_info__ = tuple([ int(num) for num in __version__.split('.')])