Merge pull request AlexsLemonade#464 from jashapiro/jashapiro/458-ref…

…-updates

build/pandoc/defaults/common.yaml

@@ -9,5 +9,5 @@ filters:
 - citeproc
 wrap: preserve
 metadata:
-  csl: https://www.zotero.org/styles/cell-numeric-superscript
+  csl:  https://www.zotero.org/styles/cell-genomics
   link-citations: true

content/03.results.md

@@ -14,7 +14,7 @@ The contributor formally requested to include their analytical code and results
 All PRs underwent peer review to ensure scientific accuracy, maintainability, and readability of code and documentation (**Figure {@fig:Fig1}C-D**).
 
 Beyond peer review, we implemented additional checks to ensure consistent results for all collaborators over time (**Figure {@fig:Fig1}D**).
-To provide a consistent software development environment, we created a monolithic image with all OpenPBTA dependencies using Docker® [@https://dl.acm.org/doi/10.5555/2600239.2600241] and the Rocker project [@https://doi.org/10.48550/arXiv.1710.03675].
+To provide a consistent software development environment, we created a monolithic image with all OpenPBTA dependencies using Docker® [@https://dl.acm.org/doi/10.5555/2600239.2600241] and the Rocker project [@arxiv:1710.03675].
 We used the continuous integration (CI) service CircleCI® to run analytical code in PRs on a test dataset before formal code review, allowing us to detect code bugs or sensitivity to data release changes.
 
 We followed a similar process in our Manubot-powered [@doi:10.1371/journal.pcbi.1007128] repository for proposed manuscript additions (**Figure {@fig:Fig1}C**); peer reviewers ensured clarity and scientific accuracy, and Manubot performed spell-checking.
@@ -203,7 +203,7 @@ We suggest this classifier could be generally applied to infer _TP53_ function i
 We used gene expression data to predict telomerase activity using EXpression-based Telomerase ENzymatic activity Detection (`EXTEND`) [@doi:10.1038/s41467-020-20474-9] as a surrogate measure of malignant potential [@doi:10.1038/s41467-020-20474-9; @doi:10.1093/carcin/bgp268], where higher `EXTEND` scores indicate higher telomerase activity.
 Aggressive tumors such as DMGs, other HGGs, and MB had high `EXTEND` scores (**Figure {@fig:Fig4}D**), and low-grade lesions such as schwannomas, GNGs, DNETs, and other LGGs had among the lowest scores (**Table S3**), supporting previous reports that aggressive tumor phenotypes have higher telomerase activity [@doi:10.1007/s13277-016-5045-7; @doi:10.1038/labinvest.3700710; @doi:10.1007/s12032-016-0736-x; @doi:10.1111/j.1750-3639.2010.00372.x].
 While `EXTEND` scores were not significantly higher in tumors with _TERT_ promoter (TERTp) mutations (N = 6; Wilcoxon p-value = 0.1196), scores were significantly correlated with _TERC_ (R = 0.619, p < 0.01) and _TERT_ (R = 0.491, p < 0.01) log2 FPKM expression values (**Figure {@fig:S5}B-C**).
-Since catalytically-active telomerase requires full-length _TERT_, _TERC_, and certain accessory proteins [@url:https://pubmed.ncbi.nlm.nih.gov/9751630], we expect that `EXTEND` scores may not be exclusively correlated with _TERT_ alterations and expression.
+Since catalytically-active telomerase requires full-length _TERT_, _TERC_, and certain accessory proteins [@pubmed:9751630], we expect that `EXTEND` scores may not be exclusively correlated with _TERT_ alterations and expression.
 
 #### Hypermutant tumors share mutational signatures and have dysregulated **_TP53_**
 

content/05.limitations.md

@@ -3,7 +3,7 @@
 Notably, PBTA brain tumor samples were collected over decades, and RNA samples were prepared using two distinct library preparations (stranded or poly-A, **Figure {@fig:S7}A**) by multiple sequencing centers.
 While we noted a strong library preparation batch effect (**Figure {@fig:S7}B**) and a possible sequencing center batch effect (**Figure {@fig:S7}C**), cancer groups are highly unbalanced across library preparations (**Figure {@fig:S7}A**).
 We did not perform batch correction because removing batch effects across unbalanced groups may induce false differences among groups [@doi:10.1093/biostatistics/kxv027; @doi:10.1016/j.tibtech.2017.02.012].
-Instead, we circumvent batch effects by grouping only stranded RNA-Seq expression data, which comprises the vast majority of the PBTA cohort, for transcriptomic analyses presented in **Figure {@fig:4}** and **Figure {@fig:5}** .
+Instead, we circumvent batch effects by grouping only stranded RNA-Seq expression data, which comprises the vast majority of the PBTA cohort, for transcriptomic analyses presented in **Figure {@fig:Fig4}** and **Figure {@fig:Fig5}** .
 As batch correction strategy depends highly on research goals [@doi:10.1016/j.tibtech.2017.02.012], we provide library preparation-specific expression matrices in the OpenPBTA data release for others to adapt to their needs.
 A second potential limitation is that performing analyses with all samples, rather than samples with high tumor purity, might result in loss of information, such as subclonal variants or low-level oncogenic pathway expression.
 To this end, we re-performed transcriptomic analyses using only samples with high tumor purity (see **Methods** for details), and indeed, results were broadly consistent with those derived from the full cohort (**Figure {@fig:S7}D-I**).