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Co-authored-by: Jaclyn Taroni <[email protected]>

analyses/cell-type-wilms-tumor-06/notebook_template/06_cnv_infercnv_exploration.Rmd

@@ -380,7 +380,7 @@ The risk is that the main cell population (in our case the fetal nephron compart
 
 ### Summary CNV score
 
-We want to calculate a single CNV score and asesss if/how it can be use to define cells with CNV versus stable/normal cells. 
+We want to calculate a single CNV score and assess if/how it can be used to define cells with CNV versus stable/normal cells. 
 We defined the score as described in the [biostar discussion](https://www.biostars.org/p/9573777/).
 
 We would expect:

analyses/cell-type-wilms-tumor-06/results/README.md

@@ -82,7 +82,7 @@ Based on the above slots, it would be straightforward to extract info of interes
 
 In addition, for the condition `reference = "both"`, we ran `infercnv` with `HMM = TRUE`.
 [HMM CNV prediction methods](https://github.com/broadinstitute/infercnv/wiki/inferCNV-HMM-based-CNV-Prediction-Methods) will allow us to explore the CNV results better, with an easy [merge](https://github.com/broadinstitute/infercnv/wiki/Extracting-features) of `infercnv` result with the `Seurat` object.
-However, HMM CNV prediction methods uses a lot of resources, including time (~2h/sample/condition), and often casues the R session to crash.
+However, HMM CNV prediction methods uses a lot of resources, including time (~2h/sample/condition), and often causes the R session to crash.
 This is why we only ran the HMM model for one `reference` condition. After selection of the best reference to use, we will run it for all samples.