From 5a2135018ff80913a83dfc5bf4e3ac2a7158d704 Mon Sep 17 00:00:00 2001 From: Geo Pertea Date: Mon, 17 Jun 2024 12:26:42 -0400 Subject: [PATCH] Update index.html --- index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.html b/index.html index caf2c48..20bd982 100644 --- a/index.html +++ b/index.html @@ -139,7 +139,7 @@

Example## Now we can compute the Principal Components (PCs) of the degraded ## transcripts 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. Again we use the assayname option to specify the we are using the tpm assay, where TPM stands for transcripts per million.

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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. Again we use the assayname option to specify the we are using the tpm assay, where TPM stands for transcripts per million.

 ## Using a simple statistical model we determine the number of PCs needed (k)
 mod <- model.matrix(~ Dx + Age + Sex + Race + Region,