@@ -9,6 +9,7 @@
 pars*.rds
 deng.csv
 scimpute_count.txt
+MAGIC_count.csv
 totalCounts_by_cell.rds
 clust.rds
 tung/reads.rds
 
@@ -118,14 +118,10 @@ knitr::include_graphics("figures/sc3.png")
 * Based on __tSNE__ maps
 * Utilises _k_-means
 
-#### [SEURAT](https://github.com/satijalab/seurat)
-
-In the newest versions of SEURAT (v. 1.3-1.4) the clustering is based on a _community detection_ approach similar to one previously proposed for analyzing CyTOF data [@Levine2015-fk]. __tSNE__ is only used exclusively for visualization. In the next chapter we will be using the latest version of `SEURAT`.
-
-__Note__ 
+#### [SNN-Cliq](http://bioinfo.uncc.edu/SNNCliq/)
 
-In the original version `SEURAT` [@Macosko2015-ix] first utilised PCA on a set of cells, then a number of statistically significant PCs were defined. Those PCs were further projected to a 2D space using tSNE. The remaining cells were projected on the same tSNE map. Density clustering algorithm ([DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)) was then used to identify cell clusters in the 2D space.
+`SNN-Cliq` [@Xu2015-vf] is a graph-based method. First the method identifies the k-nearest-neighbours of each cell according to the _distance_ measure. This is used to calculate the number of Shared Nearest Neighbours (SNN) between each pair of cells. A graph is built by placing an edge between two cells If they have at least one SNN. Clusters are defined as groups of cells with many edges between them using a "clique" method. SNN-Cliq requires several parameters to be defined manually.
 
-#### [SNN-Cliq](http://bioinfo.uncc.edu/SNNCliq/)
+#### [Seurat](https://github.com/satijalab/seurat)
 
-SNN-Cliq [@Xu2015-vf] is a graph-based method. First the method identifies the k-nearest-neighbours of each cell according to the _distance_ measure. This is used to calculate the number of Shared Nearest Neighbours (SNN) between each pair of cells. A graph is built by placing an edge between two cells If they have at least one SNN. Clusters are defined as groups of cells with many edges between them using a "clique" method. SNN-Cliq requires several parameters to be defined manually.
+`Seurat` clustering is based on a _community detection_ approach similar to `SNN-Cliq` and to one previously proposed for analyzing CyTOF data [@Levine2015-fk]. Since `Seurat` has become more like an all-in-one tool for scRNA-seq data analysis we dedicate a separate chapter to discuss it in more details (chapter \@ref(seurat)).
@@ -16,7 +16,7 @@ library(SC3)
 library(scater)
 library(SingleCellExperiment)
 library(pheatmap)
-library(irr)
+library(mclust)
 set.seed(1234567)
 ```
 
 
@@ -36,7 +36,7 @@ the total amount of data to be processed.
 For scRNASeq data, we will be focusing on unsupervised methods of feature
 selection which don't require any a priori information, such as cell-type
 labels or biological group, since they are not available, or may be unreliable,
-for many experiments. In contrast, differential expression (Chapter \ref())
+for many experiments. In contrast, differential expression (chapter \@ref(dechapter))
 can be considered a form of supervised feature selection since it uses the
 known biological label of each sample to identify features (i.e. genes) which
 are expressed at different levels across groups.
@@ -53,7 +53,7 @@ which removes cells with few detected genes, removes
 undetected genes, and converts raw counts to CPM.
 
 ```{r}
-deng_list <- M3Drop::M3DropCleanData(
+deng_list <- M3DropCleanData(
     counts(deng),
     labels = cellLabels,
     min_detected_genes = 100,
@@ -112,7 +112,7 @@ CI. Pink dots are the genes with significant biological variability
 after multiple-testing correction.
 
 ```{r, fig.width = 7, fig.height = 6}
-Brennecke_HVG <- M3Drop::BrenneckeGetVariableGenes(
+Brennecke_HVG <- BrenneckeGetVariableGenes(
     expr_matrix,
     fdr = 0.01,
     minBiolDisp = 0.5
@@ -156,7 +156,7 @@ We use M3Drop to identify significant outliers to the right of the MM
 curve. We also apply 1% FDR multiple testing correction:
 
 ```{r, fig.width = 7, fig.height = 6}
-M3Drop_genes <- M3Drop::M3DropFeatureSelection(
+M3Drop_genes <- M3DropFeatureSelection(
     expr_matrix,
     mt_method = "fdr",
     mt_threshold = 0.01
@@ -233,7 +233,7 @@ We can check whether the identified features really do represent genes different
 cell-types in this dataset.
 
 ```{r, fig.width = 7, fig.height = 10}
-M3Drop::M3DropExpressionHeatmap(
+M3DropExpressionHeatmap(
     M3Drop_genes,
     expr_matrix,
     cell_labels = celltype_labs
 
@@ -83,9 +83,9 @@ which is based on a mixture of normal distributions. Then it builds a minimum sp
 
 First we will try to use all genes to order the cells.
 ```{r tscan-all-genes}
-deng <- readRDS("deng/deng-reads.rds")
-cellLabels <- colData(deng)$cell_type2
-deng <- logcounts(deng)
+deng_counts <- readRDS("deng/deng-reads.rds")
+cellLabels <- colData(deng_counts)$cell_type2
+deng <- logcounts(deng_counts)
 colnames(deng) <- cellLabels
 procdeng <- TSCAN::preprocess(deng)
 colnames(procdeng) <- 1:ncol(deng)
@@ -131,7 +131,7 @@ Unfortunately, Monocle does not work when all the genes are used, so
 we must carry out feature selection. First, we use M3Drop:
 ```{r m3d-select-genes}
 m3dGenes <- as.character(
-    M3Drop::M3DropFeatureSelection(deng)$Gene
+    M3DropFeatureSelection(counts(deng_counts))$Gene
 )
 d <- deng[which(rownames(deng) %in% m3dGenes), ]
 d <- d[!duplicated(rownames(d)), ]
 
@@ -81,10 +81,17 @@ Based on the PCA and the clustering results, do you think that imputation is a g
 
 ### MAGIC
 
-```{r, eval = FALSE}
-res <- readRDS("deng/deng_MAGIC.rds")
+```{r}
+system("MAGIC.py -d deng.csv -o MAGIC_count.csv --cell-axis columns -l 1 --pca-non-random csv")
+```
+
+```{r}
+res <- read.csv("MAGIC_count.csv", header = TRUE)
+rownames(res) <- res[,1]
+res <- res[,-1]
+res <- t(res)
 res <- SingleCellExperiment(
-    assays = list(logcounts = as.matrix(res)), 
+    assays = list(logcounts = res), 
     colData = colData(deng)
 )
 plotPCA(
 
@@ -2,7 +2,7 @@
 knit: bookdown::preview_chapter
 ---
 
-## Differential Expression (DE) analysis
+## Differential Expression (DE) analysis {#dechapter}
 
 ```{r, echo=FALSE}
 library(knitr)