add DOI in README and CITATION file

CITATION.cff

@@ -18,6 +18,12 @@ authors:
     family-names: Bock
     orcid: 'https://orcid.org/0000-0001-6091-3088'
     affiliation: CeMM Research Center for Molecular Medicine
+identifiers:
+  - type: doi
+    value: 10.5281/zenodo.8405360
+    description: >-
+      This DOI represents all versions, and will always
+      resolve to the latest one.
 repository-code: 'https://github.com/epigen/mixscape_seurat'
 url: 'https://epigen.github.io/mixscape_seurat/'
 abstract: >-

README.md

@@ -1,9 +1,11 @@
-# scCRISPR-seq Perturbation Analysis Snakemake Workflow powered by Seurat's Mixscape
+[![DOI](https://zenodo.org/badge/481635018.svg)](https://zenodo.org/badge/latestdoi/481635018)
+
+# scCRISPR-seq Perturbation Analysis Snakemake Workflow using Seurat's Mixscape
 A [Snakemake](https://snakemake.readthedocs.io/en/stable/) workflow for performing perturbation analyses of pooled (multimodal) CRISPR screens with sc/snRNA-seq read-out (scCRISPR-seq) powered by the R package [Seurat's](https://satijalab.org/seurat/index.html) method [Mixscape](https://satijalab.org/seurat/articles/mixscape_vignette.html).
 
 This workflow adheres to the module specifications of [MR.PARETO](https://github.com/epigen/mr.pareto), an effort to augment research by modularizing (biomedical) data science. For more details and modules check out the project's repository.
 
-**If you use this workflow in a publication, don't forget to give credits to the authors by citing the URL of this (original) repository (and its DOI, see Zenodo badge above -> coming soon).**
+**If you use this workflow in a publication, please don't forget to give credit to the authors by citing it using this DOI [10.5281/zenodo.8424761](https://doi.org/10.5281/zenodo.8424761).**
 
 ![Workflow Rulegraph](./workflow/dags/rulegraph.svg)
 
@@ -41,13 +43,13 @@ This is a template for the Methods section of a scientific publication and is in
 
 The outlined analyses were performed using the R package Seurat (ver) [ref] unless stated otherwise.
 
-Mixscape. We applied the Mixscape workflow [ref], implemented in Seurat, on each [sample] separately as well as all [samples] simultaneously to identify perturbed cells compared to non-targeting (NT) guide RNA (gRNA) assigned cells. Briefly, cells putatively assigned to a gRNA and respective knockout (KO) target gene in conjunction with NT cells were used to calculate cell-wise perturbation signatures by using Seurat::CalcPerturbSig to subtract the average expression profile of  the [n_neighbors] closest NT cells in [ndims]-dimensional PCA space. Using Seurat::RunMixscape, with a log2(fold change) threshold of [lfc_th] and a minimum of [min_de_genes] differentially expressed genes, cells were classified as perturbed or non-perturbed using posterior probabilities of an expectation-maximization (EM) algorithm for mixtures of univariate normals, assuming each putatively annotated target gene group is a mixture of two Gaussian distributions (perturbed signal and non-perturbed background). 
+**Mixscape**. We applied the Mixscape workflow [ref], implemented in Seurat, on each [sample] separately as well as all [samples] simultaneously to identify perturbed cells compared to non-targeting (NT) guide RNA (gRNA) assigned cells. Briefly, cells putatively assigned to a gRNA and respective knockout (KO) target gene in conjunction with NT cells were used to calculate cell-wise perturbation signatures by using Seurat::CalcPerturbSig to subtract the average expression profile of  the [n_neighbors] closest NT cells in [ndims]-dimensional PCA space. Using Seurat::RunMixscape, with a log2(fold change) threshold of [lfc_th] and a minimum of [min_de_genes] differentially expressed genes, cells were classified as perturbed or non-perturbed using posterior probabilities of an expectation-maximization (EM) algorithm for mixtures of univariate normals, assuming each putatively annotated target gene group is a mixture of two Gaussian distributions (perturbed signal and non-perturbed background). 
 
-Visualizations. Statistics of the Mixscape classification of perturbed cells versus cells with no detectable perturbation on a target gene and gRNA basis using barplots. Perturbation scores of cells split by their Mixscape classification as density plots. Posterior probability values of non-perturbed and perturbed cells as violin plots using the Seurat function VlnPlot. Perturbation scores and posterior probabilities were additionally plotted split by replicates [split_by_col] and experiment conditions [split_by_col]. For the visualization of protein surface expression measured by Antibody Capture technologies the Seurat function VlnPlot for violin plots split by perturbation classification of cells was used.
+**Visualizations**. Statistics of the Mixscape classification of perturbed cells versus cells with no detectable perturbation on a target gene and gRNA basis using barplots. Perturbation scores of cells split by their Mixscape classification as density plots. Posterior probability values of non-perturbed and perturbed cells as violin plots using the Seurat function VlnPlot. Perturbation scores and posterior probabilities were additionally plotted split by replicates [split_by_col] and experiment conditions [split_by_col]. For the visualization of protein surface expression measured by Antibody Capture technologies the Seurat function VlnPlot for violin plots split by perturbation classification of cells was used.
 
-Linear discriminant analysis (LDA). LDA was applied on the perturbation signatures of all perturbed and NT cells using Seurat::MixscapeLDA with number of principal components [npcs] per KO class to find the most discriminative subspace, given the KO/NT classes, to project the data into and visualized in two dimensions using UMAP with Seurat::RunUMAP.
+**Linear discriminant analysis (LDA)**. LDA was applied on the perturbation signatures of all perturbed and NT cells using Seurat::MixscapeLDA with number of principal components [npcs] per KO class to find the most discriminative subspace, given the KO/NT classes, to project the data into and visualized in two dimensions using UMAP with Seurat::RunUMAP.
 
-The analysis and visualizations described here were performed using a publicly available Snakemake [ver] (ref) workflow [ref - cite this workflow here].
+**The analysis and visualizations described here were performed using a publicly available Snakemake [ver] (ref) workflow [10.5281/zenodo.8424761](https://doi.org/10.5281/zenodo.8424761).**
 
 # Features
 The workflow performs all steps of the [Mixscape Vignette](https://satijalab.org/seurat/articles/mixscape_vignette.html) on all samples in the annotation file according to the parametrization in the config file.
@@ -75,7 +77,7 @@ Detailed specifications can be found here [./config/README.md](./config/README.m
 # Links
 - [GitHub Repository](https://github.com/epigen/mixscape_seurat/)
 - [GitHub Page](https://epigen.github.io/mixscape_seurat/)
-- [Zenodo Repository (coming soon)]()
+- [Zenodo Repository](https://doi.org/10.5281/zenodo.8424761)
 - [Snakemake Workflow Catalog Entry](https://snakemake.github.io/snakemake-workflow-catalog?usage=epigen/mixscape_seurat)
 
 # Publications