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Author: WWXkenmo

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@@ -16,5 +16,5 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 RoxygenNote: 7.3.2
 NeedsCompilation: no
-Packaged: 2024-12-15 21:33:02 UTC; weixu.wang
+Packaged: 2024-12-16 01:04:56 UTC; weixu.wang
 Depends: R (>= 3.5.0)

doc/NetID.Rmd

@@ -195,7 +195,7 @@ dyn.out <- RunNetID(sce,
                     velo=FALSE)
 ```
 
-We should note that the `FateDynamic` function provides a preliminary estimation of cell fate probabilities. However, accurate cell fate estimation relies on further refinements and the incorporation of prior information. If users have their own cell fate probability matrix (`fate_prob`) and `pseudotime`, they can directly plug in these values using the following function.
+We note that the `FateDynamic` function provides a preliminary estimation of cell fate probabilities. However, accurate cell fate estimation relies on further refinements and the incorporation of prior information. If users have their own cell fate probability matrix (`fate_prob`) and `pseudotime`, they can directly plug in these values using the following function.
 
 ```{r Plugin cell fate, eval=FALSE, echo=TRUE, warning=FALSE,message=FALSE}
 dyn.out$LineageClass <- LineageClassifer(fate_prob, maxState = 10, cut_off = 0)

inst/doc/NetID.R

@@ -113,6 +113,11 @@ dyn.out <- RunNetID(sce,
                     dynamicInfer = TRUE,
                     velo=FALSE)
 
+## ----Plugin cell fate, eval=FALSE, echo=TRUE, warning=FALSE,message=FALSE-----
+#  dyn.out$LineageClass <- LineageClassifer(fate_prob, maxState = 10, cut_off = 0)
+#  dyn.out$pseudotime <- pseudotime
+#  dyn.out$fate_prob <- fate_prob # cell fate probability matrix
+
 ## ----plot cell fate probability, eval=TRUE, echo=TRUE, warning=FALSE,message=FALSE----
 ## load basis information
 dyn.out$basis <- reducedDim(sce, "PCA")[,c(1,2)]

inst/doc/NetID.Rmd

@@ -195,6 +195,14 @@ dyn.out <- RunNetID(sce,
                     velo=FALSE)
 ```
 
+We note that the `FateDynamic` function provides a preliminary estimation of cell fate probabilities. However, accurate cell fate estimation relies on further refinements and the incorporation of prior information. If users have their own cell fate probability matrix (`fate_prob`) and `pseudotime`, they can directly plug in these values using the following function.
+
+```{r Plugin cell fate, eval=FALSE, echo=TRUE, warning=FALSE,message=FALSE}
+dyn.out$LineageClass <- LineageClassifer(fate_prob, maxState = 10, cut_off = 0)
+dyn.out$pseudotime <- pseudotime
+dyn.out$fate_prob <- fate_prob # cell fate probability matrix
+```
+
 NetID classifies the cells based on the fate probability matrix using a Gaussian Mixture Model. Next, we compute the lineage fate probability fold change to assign each cluster to a specific lineage. To visualize the cell fate probability in a PCA 2D space, the following function can be utilized:
 
 ```{r plot cell fate probability, eval=TRUE, echo=TRUE, warning=FALSE,message=FALSE}

inst/doc/NetID.html

@@ -195,6 +195,14 @@ dyn.out <- RunNetID(sce,
                     velo=FALSE)
 ```
 
+We note that the `FateDynamic` function provides a preliminary estimation of cell fate probabilities. However, accurate cell fate estimation relies on further refinements and the incorporation of prior information. If users have their own cell fate probability matrix (`fate_prob`) and `pseudotime`, they can directly plug in these values using the following function.
+
+```{r Plugin cell fate, eval=FALSE, echo=TRUE, warning=FALSE,message=FALSE}
+dyn.out$LineageClass <- LineageClassifer(fate_prob, maxState = 10, cut_off = 0)
+dyn.out$pseudotime <- pseudotime
+dyn.out$fate_prob <- fate_prob # cell fate probability matrix
+```
+
 NetID classifies the cells based on the fate probability matrix using a Gaussian Mixture Model. Next, we compute the lineage fate probability fold change to assign each cluster to a specific lineage. To visualize the cell fate probability in a PCA 2D space, the following function can be utilized:
 
 ```{r plot cell fate probability, eval=TRUE, echo=TRUE, warning=FALSE,message=FALSE}