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Merge branch 'develop' into fix/heatmap
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timoast authored Jul 16, 2021
2 parents eeed8a0 + fb0394a commit 1baa39e
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1 change: 1 addition & 0 deletions NEWS.md
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- Bug fix in SingleCellExperiment conversion ([#4633](https://github.com/satijalab/seurat/issues/4633))
- Bug fix in `FindVariableFeatures()` when using `selection.method = "mvp"` and `binning.method = "equal_frequency"` ([#4712](https://github.com/satijalab/seurat/issues/4712))
- Bug fix in `DoHeatmap()` to remove random characters from plot legend([#4660](https://github.com/satijalab/seurat/issues/4660))
- Fix cell renaming in `RunCCA()`

## Seurat 4.0.3 (2020-06-10)`
## Added
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3 changes: 3 additions & 0 deletions R/dimensional_reduction.R
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Expand Up @@ -635,6 +635,9 @@ RunCCA.Seurat <- function(
merge.data = TRUE,
...
)
rownames(x = cca.results$ccv) <- Cells(x = combined.object)
colnames(x = data1) <- Cells(x = combined.object)[1:ncol(x = data1)]
colnames(x = data2) <- Cells(x = combined.object)[(ncol(x = data1) + 1):length(x = Cells(x = combined.object))]
combined.object[['cca']] <- CreateDimReducObject(
embeddings = cca.results$ccv[colnames(combined.object), ],
assay = assay1,
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4 changes: 2 additions & 2 deletions vignettes/hashing_vignette.Rmd
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Expand Up @@ -117,7 +117,7 @@ pbmc.hashtag <- NormalizeData(pbmc.hashtag, assay = 'HTO', normalization.method

Here we use the Seurat function `HTODemux()` to assign single cells back to their sample origins.

```{r hashtag_demux}
```{r hashtag_demux, results = FALSE}
# If you have a very large dataset we suggest using k_function = "clara". This is a k-medoid clustering function for large applications
# You can also play with additional parameters (see documentation for HTODemux()) to adjust the threshold for classification
# Here we are using the default settings
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## Demultiplex data

```{r demux}
```{r demux, results = FALSE}
hto12 <- HTODemux(hto12, assay = "HTO", positive.quantile = 0.99)
```

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14 changes: 14 additions & 0 deletions vignettes/vignettes.yaml
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reference: https://doi.org/10.1186/s12859-020-3538-2
citation: Ekiz et. al., BMC Bioinformatics 2020
source: https://github.com/atakanekiz/CIPR-Package

- name: miQC
title: Running miQC on Seurat objects
link: http://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/miQC.html
reference: https://www.biorxiv.org/content/10.1101/2021.03.03.433798v1
citation: Hippen et. al., bioRxiv 2021
source: https://github.com/greenelab/miQC

- name: tricycle
title: Running estimate_cycle_position from tricycle on Seurat Objects
link: http://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/tricycle.html
reference: https://doi.org/10.1101/2021.04.06.438463
citation: Zheng et. al., bioRxiv 2021
source: https://www.bioconductor.org/packages/release/bioc/html/tricycle.html

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