Update README.Rmd

README.Rmd

@@ -100,7 +100,7 @@ DegTx <- getDegTx(rse_tx, type = "cell_component")
 pcTx <- getPCs(DegTx, "tpm")
 ```
 
-Next we use the `k_qsvs()` function to calculate how many PCs we will need to account for the variation. A model matrix accounting for relevant variables should be used. Common variables such as Age, Sex, Race and Religion are often included in the model. Again we are using our `RangedSummarizedExperiment` `DegTx` as the `rse_tx` option. Next we specify the `mod` with our `model.matrix()`. `model.matrix()` creates a design (or model) matrix, e.g., by expanding factors to a set of dummy variables (depending on the contrasts) and expanding interactions similarly. For more information on creating a design matrix for your experiment see the documentation [here](http://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html). Again we use the `assayname` option to specify the we are using the `tpm` assay, where TPM stands for _transcripts per million_.
+Next we use the `k_qsvs()` function to calculate how many PCs we will need to account for the variation. A model matrix accounting for relevant variables should be used. Common variables such as Age, Sex, Race and Region are often included in the model. Again we are using our `RangedSummarizedExperiment` `DegTx` as the `rse_tx` option. Next we specify the `mod` with our `model.matrix()`. `model.matrix()` creates a design (or model) matrix, e.g., by expanding factors to a set of dummy variables (depending on the contrasts) and expanding interactions similarly. For more information on creating a design matrix for your experiment see the documentation [here](http://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html). Again we use the `assayname` option to specify the we are using the `tpm` assay, where TPM stands for _transcripts per million_.
 
 ```{r select_k}
 ## Using a simple statistical model we determine the number of PCs needed (k)