updating README

README.md

@@ -1,21 +1,21 @@
 # Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
-###### Jan Lause, Philipp Berens, Dmitry Kobak
+###### Jan Lause, Philipp Berens & Dmitry Kobak
 
 
 ### How to use this repository
 
-This repository contains the code to reproduce the analysis presented in our preprint on UMI data normalization (https://www.biorxiv.org/content/10.1101/2020.12.01.405886v1).
+This repository contains the code to reproduce the analysis presented in our paper on UMI data normalization (Lause, Berens & Kobak (2020), https://www.biorxiv.org/content/10.1101/2020.12.01.405886v1).
 
 To start, follow these steps:
 
  - install the required software listed below
  - clone this repository to your system
- - go to `tools.py`, and adapt the three import paths as needed
+ - go to `tools.py` and adapt the three import paths as needed
  - follow the dataset download instructions below
 
 Then, you can step through our analysis by following the sequence of the notebooks. There are four independet analyses:
 
- - Reproduction and investigation of the NB regression model by Hafemeister & Satija (Notebookes `01` & `02`, producing Figure 1 from our paper)
+ - Reproduction of the NB regression model by Hafemeister & Satija (2019) and investigation of alternative models (Notebookes `01` & `02`, producing Figure 1 from our paper)
  - Estimation of technical overdispersion from negative control datasets (Notebooks `01` & `03`, producing Figure S1)
  - Benchmarking normalization by Analytical Pearson residuals vs. GLM-PCA vs. standard methods:
      - on the PBMC dataset (Notebooks `01`, `04`, `05`, producing Figures 2, S3, S4, S6, S7, S8, and additional figures)
@@ -25,6 +25,8 @@ Each of the analyses will first preprocess and filter the datasets. Next, comput
 
 We recommend to run the code on a powerful machine with at least 150 GB RAM.
 
+For questions or feedback, feel free to use the issue system or email us.
+
 ### Pre-requisites
 
 - Python (version used in the paper: `3.6.9`)