@@ -1,7 +1,7 @@

 Package: SGCP

 Type: Package

 Title: SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks

-Version: 0.99.2

+Version: 0.99.3

 Authors@R: c(person("Niloofar", "AghaieAbiane", email = "niloofar.abiane@gmail.com" ,role = c("aut", "cre")),

 			 person("Ioannis", "Koutis", email = " ikoutis@njit.edu",role = c("aut")))

 Description: SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks.

@@ -395,7 +395,7 @@ We highly recommend users to perform `SGCP` on the three potential metod

 `SGCP` allows user to visualize the result. `SGCP_ezPLOT` takes `sgcp` result from 

 `ezSGCP` function along with `expData` and `geneID`. It returns two files; excel 

-and pdf depending on the initial call. It also let users keep the plotting object 

+and pdf depending on the initial call. User also can keep the plotting object 

 by setting keep = TRUE. Here, we set `silhouette_in` to TRUE to see the silhouette index plot. We need to have `magic` package installed.

 ```{r, message=FALSE, warning=FALSE}

@@ -404,8 +404,7 @@ library(magick)

 ```{r, message=FALSE, warning=FALSE}

 message("PCA of expression data without label")

-sgcp_plts <- SGCP_ezPLOT(sgcp = sgcp, expreData = expData, 

-                        silhouette_index = TRUE, keep = TRUE)

+SGCP_ezPLOT(sgcp = sgcp, expreData = expData, silhouette_index = TRUE, keep = FALSE)

 ```

 Note that in order to store files in xlsx format, excel must be installed on your system. 

@@ -448,13 +447,13 @@ resAdja[0:10, 0:5]

 ```

-`resAdja` is the adjacency matrix of the gene co-expression network. We can visualize the heatmap of the adjacency matrix using$~$` SGCP_plot_heatMap` function as follow.

+`resAdja` is the adjacency matrix of the gene co-expression network. We can visualize the heatmap of the adjacency matrix using$~$` SGCP_plot_heatMap` function as follow.To see the heatmap, uncomment the following line of codes.

 ```{r, message=TRUE, warning=FALSE}

-message("Plotting adjacency heatmap")

-pl <- SGCP_plot_heatMap(m = resAdja, tit = "Adjacency Heatmap", 

-                    xname = "genes", yname = "genes")

-print(pl)

+#message("Plotting adjacency heatmap")

+#pl <- SGCP_plot_heatMap(m = resAdja, tit = "Adjacency Heatmap", 

+#                    xname = "genes", yname = "genes")

+#print(pl)

 ```

@@ -561,25 +560,25 @@ If you run the code, you will see the following will be printout for you.

 We saw that three methods ("relativeGap", "secondOrderGap", "additiveGap") resulted in three distinct potential number of clusters. `SGCP` picked "secondOrderGap" after gene ontology validation which is 2. `cv` field is "cvGO", which indicates that k is found based on gene ontology validation. In `original` field, you can have the n_egvec first columns ( eigenvectors) and eigenvalues of the transformation matrix. This might be useful for further investigation.

-Now we can see the plot of PCA on the expression and transformed data with and without the labels as follow.

+Now we can see the plot of PCA on the expression and transformed data with and without the labels using `SGCP_plot_pca`. For practice, uncomment the following and see the resulting PCAs.

 ```{r, message=TRUE, warning=FALSE}

 message("Plotting PCA of expression data with label")

-pl <- SGCP_plot_pca(m = expData, clusLabs = NULL, tit = "PCA plot without label", ps = .5)

-print(pl)

+# pl <- SGCP_plot_pca(m = expData, clusLabs = NULL, tit = "PCA plot without label", ps = .5)

+# print(pl)

 ```

 ```{r, message=TRUE, warning=FALSE}

 message("Plptting PCA of transformed data without label")

-pl <- SGCP_plot_pca(m = resClus$Y, clusLabs = NULL, tit = "PCA plot without label", ps = .5)

-print(pl)

+# pl <- SGCP_plot_pca(m = resClus$Y, clusLabs = NULL, tit = "PCA plot without label", ps = .5)

+# print(pl)

 ```

 ```{r, message=TRUE, warning=FALSE}

 message("Plotting PCA of transformed data with label")

-pl <- SGCP_plot_pca(m = resClus$Y, clusLabs = resClus$clusterLabels, tit = "PCA plot with label", ps = .5)

-print(pl)

+# pl <- SGCP_plot_pca(m = resClus$Y, clusLabs = resClus$clusterLabels, tit = "PCA plot with label", ps = .5)

+# print(pl)

 ```

@@ -750,24 +749,24 @@ print(table(resSemiSupervised$FinalLabeling$FinalLabel))

 ```

 In this step, k-nearest neighbor model is selected. It hyper-parameter is selected based on cross validation on accuracy metric. Table above shows the gene semi label and final labeling.

-Now lets see the PCA plot with the final labeling.

+Now you can see the PCA plot with the final labeling using `SGCP_plot_pca` function. For practice uncomment the following to see the resulting PCAs.

 ```{r, message=TRUE, warning=FALSE}

-message("Plotting PCA of transformed data with label")

-pl <- SGCP_plot_pca(m = expData, 

-                clusLabs = resSemiSupervised$FinalLabeling$FinalLabel, 

-                tit = "PCA plot with label", ps = .5)

+# message("Plotting PCA of transformed data with label")

+# pl <- SGCP_plot_pca(m = expData, 

+#                clusLabs = resSemiSupervised$FinalLabeling$FinalLabel, 

+#                tit = "PCA plot with label", ps = .5)

 print(pl)

 ```

 ```{r, message=TRUE, warning=FALSE}

-message("Plotting PCA of transformed data with label")

-pl <- SGCP_plot_pca(m = resClus$Y, 

-                clusLabs = resSemiSupervised$FinalLabeling$FinalLabel, 

-                tit = "PCA plot with label", ps = .5)

-print(pl)

+# message("Plotting PCA of transformed data with label")

+# pl <- SGCP_plot_pca(m = resClus$Y, 

+#                clusLabs = resSemiSupervised$FinalLabeling$FinalLabel, 

+#                tit = "PCA plot with label", ps = .5)

+#print(pl)

 ```