Commit 21279ec9 authored by Puxue Qiao's avatar Puxue Qiao
Browse files

Merge branch 'master' of gitlab.svi.edu.au:biocellgen-public/mig_2019_scrnaseq-workshop

parents 44fe1699 fc3775dc
Pipeline #936 passed with stage
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......@@ -4,7 +4,7 @@ output: html_document
---
```{r setup, echo=FALSE}
knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=TRUE)
knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=FALSE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
```
......
......@@ -3,7 +3,7 @@ output: html_document
---
```{r setup, echo=FALSE}
knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=TRUE)
knitr::opts_chunk$set(out.width='90%', fig.align = 'center', eval=FALSE)
knitr::opts_knit$set(root.dir = normalizePath(".."))
```
......
......@@ -125,7 +125,7 @@ residuals (this may be reasonable for normalized log-counts in many cases; but
it may not be---debate continues in the literature), then we can apply `limma`
to regress out (known) unwanted sources of variation as follows.
```{r limma-lm}
```{r limma-lm, eval=FALSE}
## fit a model just accounting for batch
lm_design_batch <- model.matrix(~0 + batch, data = colData(umi.qc))
fit_lm_batch <- lmFit(logcounts(umi.qc), lm_design_batch)
......@@ -160,7 +160,7 @@ __Exercise 2__
Perform LM correction for each individual separately. Store the final corrected
matrix in the `lm_batch_indi` slot.
```{r limma-lm-indi, echo=TRUE}
```{r limma-lm-indi, echo=TRUE, eval=FALSE}
## define cellular detection rate (cdr), i.e. proportion of genes expressed in each cell
umi.qc$cdr <- umi.qc$total_features_by_counts_endogenous / nrow(umi.qc)
## fit a model just accounting for batch by individual
......@@ -273,10 +273,12 @@ $W$, $\alpha$, $\beta$, and $k$ is infeasible. For a given $k$, instead the
following three approaches to estimate the factors of unwanted variation $W$ are
used:
* _RUVg_ uses negative control genes (e.g. ERCCs), assumed to have constant expression across samples;
* _RUVs_ uses centered (technical) replicate/negative control samples for which the covariates of interest are
constant;
* _RUVr_ uses residuals, e.g., from a first-pass GLM regression of the counts on the covariates of interest.
* _RUVg_ uses negative control genes (e.g. ERCCs), assumed to have constant
expression across samples;
* _RUVs_ uses centered (technical) replicate/negative control samples for which
the covariates of interest are constant;
* _RUVr_ uses residuals, e.g., from a first-pass GLM regression of the counts on
the covariates of interest.
We will concentrate on the first two approaches.
......
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