docs: add competing methods

docs/Introduction.rst

@@ -21,4 +21,22 @@ scAR uses a latent variable model to represent the biological and technical comp
 
 What types of data that scAR can process?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-We validated scAR on scRNAseq to remove ambient mRNA, scCRISPR-seq to assign sgRNAs, cell multiplexing to identify the true tags and CITE-seq to clean noisy protein counts (ADT). It recovers a great number (33% ~ 50%) of cells in scCRISPR-seq and cell multiplexing experiments and significantly improves data quality in scRNAseq and CITE-seq. In theory, any droplet-based single-cell omics technology should have the ambient contamination issue, especially for the complex experiments or samples. scAR can be a reasonable solution in these cases.
+We validated scAR on scRNAseq to remove ambient mRNA, scCRISPR-seq to assign sgRNAs, cell multiplexing to identify the true tags and CITE-seq to clean noisy protein counts (ADT). It recovers a great number (33% ~ 50%) of cells in scCRISPR-seq and cell multiplexing experiments and significantly improves data quality in scRNAseq and CITE-seq. In theory, any droplet-based single-cell omics technology should have the ambient contamination issue, especially for the complex experiments or samples. scAR can be a reasonable solution in these cases.
+
+What are the alternative apporaches?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+There are several methods to model the data noise in single-cell omics. In general, they can be categorized into two classes. One is dealing with background noise and the other is modeling the stachastic noise. Some of them are listed below.
+
++-------------------------------------------+-------------------------------------------+
+| Background noise                          | Stachastic noise                          |
++========+===============+==================+========+===============+==================+
+| CellBender [Fleming2019]_                 | scVI [Lopez2018]_                         |
++-------------------------------------------+-------------------------------------------+
+| SoupX [Young2020]_                        | DCA [Eraslan2019]_                        |
++-------------------------------------------+-------------------------------------------+
+| DecontX [Yang2020]_                       |                                           |
++-------------------------------------------+-------------------------------------------+
+| totalVI (protein counts) [Gayoso2021]_    |                                           |
++-------------------------------------------+-------------------------------------------+
+| DSB  (protein counts) [Mulè2022]_         |                                           |
++-------------------------------------------+-------------------------------------------+

docs/Reference.rst

@@ -1,18 +1,46 @@
 Reference
 ===============
 
+.. [Dixit2016] Dixit *et al.* (2016),
+   `Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens <http://dx.doi.org/10.1016/j.cell.2016.11.038>`__,
+   Cell.
+
+.. [Eraslan2019] Eraslan *et al.* (2019),
+    `Single-cell RNA-seq denoising using a deep count autoencoder <http://dx.doi.org/10.1038/s41467-018-07931-2>`__,
+    Nature Communications.
+
+.. [Fleming2019] Fleming *et al.* (2019),
+    `CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets <https://doi.org/10.1101/791699>`__,
+    bioRxiv.
+
+.. [Gayoso2021] Gayoso *et al.* (2021),
+    `Joint probabilistic modeling of single-cell multi-omic data with totalVI <http://dx.doi.org/10.1038/s41592-020-01050-x>`__,
+    Nature Methods.
+
+.. [Lopez2018] Lopez *et al.* (2018),
+    `Deep generative modeling for single-cell transcriptomics <http://www.nature.com/articles/s41592-018-0229-2>`__,
+    Nature Methods.
+
 .. [Lun2019] Lun *et al.* (2019),
    `EmptyDrops: Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data <https://doi.org/10.1186/s13059-019-1662-y>`__,
    Genome Biology.
 
-.. [Dixit2016] Dixit *et al.* (2016),
-   `Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens <http://dx.doi.org/10.1016/j.cell.2016.11.038>`__,
-   Cell.
+.. [Ly2020] Ly *et al.* (2020),
+    `The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test <https://doi.org/10.1007/s42113-019-00070-x>`__,
+    Computational Brain & Behavior.
+
+.. [Mulè2022] Mulè *et al.* (2022),
+    `Normalizing and denoising protein expression data from droplet-based single cell profiling <https://doi.org/10.1038/s41467-022-29356-8>`__,
+    Nature Communications.
 
 .. [Sheng2022] Sheng *et al.* (2022),
    `Probabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omics <https://www.biorxiv.org/content/early/2022/03/24/2022.01.14.476312>`__,
    bioRxiv.
 
-.. [Ly2020] Ly *et al.* (2020),
-    `The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test <https://doi.org/10.1007/s42113-019-00070-x>`__,
-    Computational Brain & Behavior.
+.. [Yang2020] Yang *et al.* (2020),
+    `Decontamination of ambient RNA in single-cell RNA-seq with DecontX <https://doi.org/10.1186/s13059-020-1950-6>`__,
+    Genome Biology.
+
+.. [Young2020] Young *et al.* (2020),
+    `SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data <https://doi.org/10.1093/gigascience/giaa151>`__,
+    GigaScience.