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do not cache chunk outputs as this triggers git lfs error
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lgeistlinger committed May 23, 2024
1 parent 2fd57cb commit 6293ab0
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Showing 5 changed files with 5 additions and 5 deletions.
2 changes: 1 addition & 1 deletion episodes/cell_type_annotation.Rmd
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Expand Up @@ -23,7 +23,7 @@ editor_options:
## Setup

```{r chunk-opts, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(BiocStyle)
```

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2 changes: 1 addition & 1 deletion episodes/eda_qc.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ exercises: 15 # Minutes of exercises in the lesson
```{r chunk-opts, include=FALSE}
rm(list = ls())
gc()
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(BiocStyle)
```

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2 changes: 1 addition & 1 deletion episodes/intro-sce.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ exercises: 10 # Minutes of exercises in the lesson
## Setup

```{r chunk-opts, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(BiocStyle)
```

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2 changes: 1 addition & 1 deletion episodes/large_data.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ exercises: 2 # Minutes of exercises in the lesson
::::::::::::::::::::::::::::::::::::::::::::::::

```{r chunk-opts, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(BiocStyle)
```

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2 changes: 1 addition & 1 deletion episodes/multi-sample.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ Note that this is a paired design in which for each biological replicate (pool 3
We start by loading the data and doing a quick exploratory analysis, essentially applying the normalization and visualization techniques that we have seen in the previous lectures to all samples.

```{r chunk-opts, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(BiocStyle)
```

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