@@ -80,7 +80,7 @@ Imports:

     Seurat (>= 3.1.3),

     shiny,

     shinyjs,

-    SingleR (>= 1.99.2),

+    SingleR,

     SoupX,

     sva,

     reshape2,

@@ -338,11 +338,20 @@ on this to run the cell type labeling process.</li>

 annotation will be inserted to the background object. They will all have

 the same prefix as

 <code>"SingleR_{reference abbr}_{annotation level}_"</code>, then

+<code>"score"</code>, <code>"labels"</code>, <code>"delta.next"</code>

+and <code>"pruned.labels"</code>, respectively. <code>"labels"</code>

+contains the predicted label, basing only on the maximum entry in

+scores. <code>"delta.next"</code> contains difference between the best

+and next-best score. <code>"pruned.labels"</code> contains predictions

+where “low-quality” labels are replaced with <code>NA</code>s.</p>

+<blockquote>

+<p>With older versions of SingleR, the output variables ends with

 <code>"score"</code>, <code>"first.labels"</code>, <code>"labels"</code>

 and <code>"pruned.labels"</code>, respectively.

 <code>"first.labels"</code> refers to the labeling initially indicated

 by the scores, <code>"labels"</code> is fine-tuned, and

 <code>"pruned.labels"</code> is the pruned result.</p>

+</blockquote>

 <p><strong>Visualization</strong></p>

 <p>SCTK does not have a quick visualization at this point. But users can

 go to the <a href="visualization.html">CellViewer</a> tab to create a

@@ -384,6 +393,14 @@ contains the predicted label, basing only on the maximum entry in

 scores. <code>"delta.next"</code> contains difference between the best

 and next-best score. <code>"pruned.labels"</code> contains predictions

 where “low-quality” labels are replaced with <code>NA</code>s.</p>

+<blockquote>

+<p>With older versions of SingleR, the output variables ends with

+<code>"score"</code>, <code>"first.labels"</code>, <code>"labels"</code>

+and <code>"pruned.labels"</code>, respectively.

+<code>"first.labels"</code> refers to the labeling initially indicated

+by the scores, <code>"labels"</code> is fine-tuned, and

+<code>"pruned.labels"</code> is the pruned result.</p>

+</blockquote>

 <p>Besides, users can also use their own labeled reference datasets

 wrapped in a <a href="https://rdrr.io/bioc/SingleCellExperiment/man/SingleCellExperiment.html" class="external-link">SingleCellExperiment</a>

 object. Refer to argument <code>useSCERef</code> and

Binary files a/docs/articles/cell_type_labeling_files/figure-html/visualization-1.png and b/docs/articles/cell_type_labeling_files/figure-html/visualization-1.png differ

@@ -62,7 +62,9 @@ There are 6 basic widgets that users need to work with before reaching to the re

 6. **Label** - The button to trigger the algorithm. Click on this to run the cell type labeling process. 

-The result will not be pop out directly, but three levels of cell annotation will be inserted to the background object. They will all have the same prefix as `"SingleR_{reference abbr}_{annotation level}_"`, then `"score"`, `"first.labels"`, `"labels"` and `"pruned.labels"`, respectively. `"first.labels"` refers to the labeling initially indicated by the scores, `"labels"` is fine-tuned, and `"pruned.labels"` is the pruned result.  

+The result will not be pop out directly, but three levels of cell annotation will be inserted to the background object. They will all have the same prefix as `"SingleR_{reference abbr}_{annotation level}_"`, then `"score"`, `"labels"`, `"delta.next"` and `"pruned.labels"`, respectively. `"labels"` contains the predicted label, basing only on the maximum entry in scores. `"delta.next"` contains difference between the best and next-best score. `"pruned.labels"` contains predictions where "low-quality" labels are replaced with `NA`s. 

+

+> With older versions of SingleR, the output variables ends with `"score"`, `"first.labels"`, `"labels"` and `"pruned.labels"`, respectively. `"first.labels"` refers to the labeling initially indicated by the scores, `"labels"` is fine-tuned, and `"pruned.labels"` is the pruned result.  

 **Visualization**

@@ -96,6 +98,8 @@ sce <- runSingleR(inSCE = sce, useAssay = "logcounts", useBltinRef = "hpca", lev

 Four results from SingleR will be stored in `colData()` slot with the same prefix as `"SingleR_{reference abbr}_{annotation level}_"`, then `"score"`, `"labels"`, `"delta.next"` and `"pruned.labels"`, respectively. `"labels"` contains the predicted label, basing only on the maximum entry in scores. `"delta.next"` contains difference between the best and next-best score. `"pruned.labels"` contains predictions where "low-quality" labels are replaced with `NA`s. 

+> With older versions of SingleR, the output variables ends with `"score"`, `"first.labels"`, `"labels"` and `"pruned.labels"`, respectively. `"first.labels"` refers to the labeling initially indicated by the scores, `"labels"` is fine-tuned, and `"pruned.labels"` is the pruned result.  

+

 Besides, users can also use their own labeled reference datasets wrapped in a [SingleCellExperiment](https://rdrr.io/bioc/SingleCellExperiment/man/SingleCellExperiment.html) object. Refer to argument `useSCERef` and `labelColName`. Additionally, the labeling can also be done on cluster label if users have already performed clustering on their dataset and have the result stored in `colData()` slot. Refer to argument `labelByCluster`.  

 **Visualization**