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WWXkenmo authored Dec 16, 2024
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Expand Up @@ -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)
2 changes: 1 addition & 1 deletion doc/NetID.Rmd
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Expand Up @@ -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)
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5 changes: 5 additions & 0 deletions inst/doc/NetID.R
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Expand Up @@ -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)]
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8 changes: 8 additions & 0 deletions inst/doc/NetID.Rmd
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Expand Up @@ -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}
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90 changes: 40 additions & 50 deletions inst/doc/NetID.html

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8 changes: 8 additions & 0 deletions vignettes/NetID.Rmd
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Expand Up @@ -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}
Expand Down

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