diff --git a/Markdowns/01_Introduction_to_RNAseq_Methods.Rmd b/Markdowns/01_Introduction_to_RNAseq_Methods.Rmd
index 2ba9a74..992d5d9 100644
--- a/Markdowns/01_Introduction_to_RNAseq_Methods.Rmd
+++ b/Markdowns/01_Introduction_to_RNAseq_Methods.Rmd
@@ -1,6 +1,6 @@
---
title: "Introduction to RNAseq Methods"
-date: "March 2023"
+date: "October 2024"
output:
ioslides_presentation:
css: css/stylesheet.css
@@ -93,7 +93,7 @@ output:
-**Experimental Design**
+**Experimental Design**
@@ -112,61 +112,23 @@ output:
-
+
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
-
-
-
-
-
-
-
-
-
-
-## Designing the right experiment
-
-
-
-
-
-
-
-
-
-### A good experiment should:
-
-* Have clear objectives
-
-* Have sufficient power
-
-* Be amenable to statisical analysis
-
-* Be reproducible
-
-* More on experimental design later
-
-
-## Designing the right experiment
-
-### Practical considerations for RNAseq
-
+## Practical considerations for RNAseq
+
* Coverage: how many reads?
-
+
* Read length & structure: Long or short reads? Paired or Single end?
-
-* Controlling for batch effects
-
+
* Library preparation method: Poly-A, Ribominus, other?
-
-## Designing the right experiment - How many reads do we need?
-
-
+
+## How many reads do we need?
+
The coverage is defined as:
@@ -176,191 +138,24 @@ $\frac{Read\,Length\;\times\;Number\,of\,Reads}{Length\,of\,Target\,Sequence}$
-Each replicate is from an indepent biological individual
-
-* *In Vivo*:
-
- * Patients
- * Mice
-
-* *In Vitro*:
-
- * Different cell lines
- * Different passages
+* Small RNA Analysis – short single read, e.g. SE50 - will need trimming.
+
-
-
-
-
-
-
-
-
-## Designing the right experiment - Batch effects
-
-
-
-## Designing the right experiment - Batch effects
-
-* Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
-
-* Batch effects are problematic if they are confounded with the experimental variable.
-
-* Batch effects that are randomly distributed across experimental variables can be controlled for.
-
-## Designing the right experiment - Batch effects
-
-* Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
-
-* Batch effects are problematic if they are confounded with the experimental variable.
-
-* Batch effects that are randomly distributed across experimental variables can be controlled for.
-
-* Randomise all technical steps in data generation in order to avoid batch effects.
-
-
-
-## Designing the right experiment - Batch effects
-
-* Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
-
-* Batch effects are problematic if they are confounded with the experimental variable.
-
-* Batch effects that are randomly distributed across experimental variables can be controlled for.
-
-* Randomise all technical steps in data generation in order to avoid batch effects.
-
-
-
-## Designing the right experiment - Batch effects
-
-* Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
-
-* Batch effects are problematic if they are confounded with the experimental variable.
-
-* Batch effects that are randomly distributed across experimental variables can be controlled for.
-
-* Randomise all technical steps in data generation in order to avoid batch effects.
-
-
-
-## Designing the right experiment - Batch effects
-
-* Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
-
-* Batch effects are problematic if they are confounded with the experimental variable.
-
-* Batch effects that are randomly distributed across experimental variables can be controlled for.
-
-* Randomise all technical steps in data generation in order to avoid batch effects
-
-* **Record everything**: Age, sex, litter, cell passage ..
-
-
-## RNAseq Workflow
-
-
-
-
-
-
-**Experimental Design**
-
-
-
-**Library Preparation**
-
-
-
-**Sequencing**
-
-
-
-**Bioinformatics Analysis**
-
-
-
-
-
-
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
-
+
-
- Ribosomal RNA
+
- Ribosomal RNA
-
- Poly-A transcripts
+
- Poly-A transcripts
-
- Other RNAs e.g. tRNA, miRNA etc.
+
- Other RNAs e.g. tRNA, miRNA etc.
@@ -406,7 +201,7 @@ Replicates are from the same individual but processed separately
-
+
Poly-A transcripts e.g.:
@@ -424,7 +219,7 @@ Poly-A transcripts e.g.:
-
+
Poly-A transcripts + Other mRNAs e.g.:
@@ -435,43 +230,9 @@ Poly-A transcripts + Other mRNAs e.g.:
-## RNAseq Workflow
-
-
-
-
-
-
-**Experimental Design**
-
-
-
-**Library Preparation**
-
-
-
-**Sequencing**
-
-
-
-**Bioinformatics Analysis**
-
-
-
-
-
-
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
-
-
-
-
-
-
-If a probe doesn't shine as bright as it should, the sequencer is less confident
-in calling that base.
-
-
-
-
-
-
-If there are lots of probes the same colour in the same region the sequencer
-finds it harder to identify the individual reads.
-
-
-
-
-
-
-
-**Experimental Design**
-
-
-
-**Library Preparation**
-
-
-
-**Sequencing**
-
-
-
-**Bioinformatics Analysis**
-
+
+
-
-
-
+
+
-
-
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
-
## Case Study
-
+
## Differential Gene Expression Analysis Workflow {#less_space_after_title}
-
+
diff --git a/Markdowns/01_Introduction_to_RNAseq_Methods.html b/Markdowns/01_Introduction_to_RNAseq_Methods.html
index 1c7f7ed..e44e3af 100644
--- a/Markdowns/01_Introduction_to_RNAseq_Methods.html
+++ b/Markdowns/01_Introduction_to_RNAseq_Methods.html
@@ -3061,20 +3061,23 @@
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