updating image links as several are broken

Author: aabarug

02-01-ExperimentalPlanning.Rmd

@@ -158,7 +158,7 @@ knitr::include_graphics("https://lizard.bio/hs-fs/hubfs/scs_blog1_table-1.png?wi
 (ref:foo5) Different RNAseq isolation methods. Image source: [RNA Sequencing and Analysis (2015)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863231/)
 
 ```{r, echo=FALSE, out.width="100%", fig.cap=" (ref:foo5)"}
-knitr::include_graphics("https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4863231/bin/nihms768779f1.jpg")
+knitr::include_graphics("https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef22/4863231/0cc69253abdf/nihms768779f1.jpg")
 ```
 
 
@@ -191,7 +191,7 @@ Not every gene needs to be assayed in an RNAseq experiment. Targeted RNAseq allo
 (ref:foo7) Alternative splicing and RNA technologies. Image source: [Deshpande D, et al, RNA-seq data science: From raw data to effective interpretation. Frontier Genetics. 2023](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043755/)
 
 ```{r, echo=FALSE, fig.align="center", fig.cap="(ref:foo7)", out.width="95%", out.height="70%"}
-knitr::include_graphics("https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043755/bin/fgene-14-997383-g002.jpg")
+knitr::include_graphics("images/experimental_design/fig_fgene-14-997383-g002.jpg")
 ```
 
 
@@ -204,7 +204,7 @@ Long read sequencing typically refers to technology that produce reads with leng
 (ref:foo8) Sequencing Depth and Coverage. Image source: [sequencing depth vs coverage](https://3billion.io/blog/sequencing-depth-vs-coverage)
 
 ```{r, echo=FALSE, fig.align="center", fig.cap="(ref:foo8)", out.width="90%"}
-knitr::include_graphics("https://3billion.io/img/news/depth-and-coverage_2023-08-25-051528_evsk.webp")
+knitr::include_graphics("images/experimental_design/fig_depth_coverage.jpeg")
 ```
 
 
@@ -230,10 +230,10 @@ However, higher sequencing depth is necessary for detecting lowly expressed diff
 
 ## RNAseq Uses
 
-(ref:foo10) Image source: [RNA-seq](https://www.helixio.fr/page/rna-seq-1)
+(ref:foo10) Image source: [RNA-seq](https://helixio.com/page/rna-seq-1)
 
 ```{r, echo=FALSE, out.width="100%", fig.cap='(ref:foo10)'}
-knitr::include_graphics("https://www.helixio.fr/uploads/kcfinder/images/PAGE%20WORKFLOW6-1.jpg")
+knitr::include_graphics("images/experimental_design/fig_rnaseq_uses_helixio.jpg")
 ```
 
 
@@ -254,7 +254,7 @@ The most common type of analysis performed with RNAseq data is differential gene
 
 (ref:foo11) Image source: [Gene-level differential expression analysis with DESeq2](https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/04a_design_formulas.html)
 
-```{r, echo=FALSE, out.width="100%", fig.align="center", fig.cap =""}
+```{r, echo=FALSE, out.width="100%", fig.align="center", fig.cap ="(ref:foo11)"}
 knitr::include_graphics("images/experimental_design/fig_dge.png")
 ```
 
@@ -294,10 +294,10 @@ knitr::include_graphics("images/experimental_design/fig_dif_splice_usage.png")
 Additionally, we can look at whether transcript levels are changing between two conditions, regardless of the gene of origin. This is known as differential transcript expression. 
 
 
-(ref:foo14) Image source: [Froussios K, et al, Relative Abundance of Transcripts (RATs): Identifying differential isoform abundance from RNA-seq. F1000Research. 2019](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043755/)
+(ref:foo14) Image source: [Froussios K, et al, Relative Abundance of Transcripts (RATs): Identifying differential isoform abundance from RNA-seq. F1000Research. 2019](https://pmc.ncbi.nlm.nih.gov/articles/PMC6426083/)
 
 ```{r, echo=FALSE, fig.align="center", fig.cap="The types of differential expression analysis. (ref:foo14)", out.width="100%"}
-knitr::include_graphics("https://f1000research.s3.amazonaws.com/manuscripts/19594/95bdb2a3-7459-4d64-9569-ad69d8910d35_figure1.gif")
+knitr::include_graphics("images/experimental_design/fig_f1000research-8-19594-g0000.jpg")
 ```
 
 
@@ -328,6 +328,7 @@ knitr::include_graphics("https://www.pacb.com/wp-content/uploads/img_isoform_dis
 
 
 
+
 ### Transcriptome Assembly
 
 When a reference genome is either unavailable or not desired, it is possible to take RNAseq reads and assemble them into a transcriptome of the assayed genes. The assembly can be done in 2 ways, there are reference based methods and de novo methods. Reference based methods use the reference of either the organism or a closely related species. 
@@ -339,11 +340,12 @@ Long read sequencing is better suited to building high quality transcriptomes th
 (ref:foo17) Image source: [A simple guide to de novo transcriptome assembly and annotation (2022)](https://academic.oup.com/bib/article/23/2/bbab563/6514404)
 
 ```{r, echo=FALSE, out.width="100%", fig.cap='(ref:foo17)'}
-knitr::include_graphics("https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/23/2/10.1093_bib_bbab563/1/m_bbab563f3.jpeg?Expires=1729202148&Signature=FMhHgcKT3vS5SP1phAnOU3R9ayIzXS6DDCdTPOK7WQdpOPRSxFEqbIla6ISWV7pKDaINiPZBEdcpmjCn75qGFCIGTLasuKU~DD5Po69XlEH8Os~9UgmXbPMhGddL7cXxdw3skQ~dial4rI4A8mu388ZJlRPDbmr6EURBKAdm-lGpG~xxLJzN9hUmqB2FDNtOt6bY6TGvVRNOlGXTz24HveHp~feY4nhtpb9BpUEKBZgpChqMpQEw8mQAkmx-BcXBon8pynzZ7ud6cBjKMZ7W3T1Cip0OGSrJE2Bn~af~uY-Lv68PJ3319cnUelEs05Mnsr34ntJtnllPjVRUkDo2Bw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA")
+knitr::include_graphics("images/experimental_design/fig_assembly_m_bbab563f3.jpeg")
 ```
 
 
 
+
 ### Gene Fusion Detection
 
 RNAseq can have clinical applications. One such application is the detection of fusion genes - these can arise due to chromosomal rearrangements combining the coding regions of two genes. These genes can produce aberrant proteins and lead to cancer development if the fused genes are oncogenes or tumor suppresor genes. Therefore, detection of fusion genes can be an important diagnostic tool in clinical settings as well as for cancer research.