Fixed typos

prelims/abstract.Rmd

@@ -1 +1 @@
-High-throughput techniques such as microarrays and RNA-sequencing enable the relatively easy and inexpensive collection of bulk gene expression profiles from any biological condition. Recently, also the transcriptome of single cells can be efficiently captured via novel single-cell RNA-sequencing technologies. Functional analysis of bulk or single-cell gene expression data has been proven to be a powerful approach as they summarize the large and noisy gene expression space into a smaller number of biologically meaningful features such as pathway and transcription factor activities. In the first part of this thesis, I expanded the scope on the pathway analysis tool PROGENy and the transcription factor analysis tool DoRothEA through thorough benchmarking pipelines. First I transferred their regulatory knowledge from human to mouse to enable the functional characterization of gene expression profiles from mice. Moreover, I demonstrated the robustness and applicability of both tools on human single-cell RNA-sequencing data. In the second part of this thesis, I focussed on the analysis of gene expression profiles from mice and humans in the context of acute and chronic liver diseases. Finally, I identified and functionally characterized exclusively and commonly regulated genes of chronic and acute liver damage in mice and a set of genes that were consistently altered in a novel chronic mouse model and patients of chronic liver disease. Especially the latter demonstrates that, although major interspecies differences remain, there is a common and consistent transcriptomic response to chronic liver damage in mice and humans. This set of genes could be further investigated to study the pathophysiology of the liver in in-vitro and in-vivo studies.
+High-throughput techniques such as microarrays and RNA-sequencing enable the relatively easy and inexpensive collection of bulk gene expression profiles from any biological condition. Recently, also the transcriptome of single cells can be efficiently captured via novel single-cell RNA-sequencing technologies. Functional analysis of bulk or single-cell gene expression data has been proven to be a powerful approach as they summarize the large and noisy gene expression space into a smaller number of biologically meaningful features such as pathway and transcription factor activities. In the first part of this thesis, I expanded the scope of the pathway analysis tool PROGENy and the transcription factor analysis tool DoRothEA through thorough benchmarking pipelines. First I transferred their regulatory knowledge from human to mouse to enable the functional characterization of gene expression profiles from mice. Moreover, I demonstrated the robustness and applicability of both tools on human single-cell RNA-sequencing data. In the second part of this thesis, I focussed on the analysis of gene expression profiles from mice and humans in the context of acute and chronic liver diseases. Finally, I identified and functionally characterized exclusively and commonly regulated genes of chronic and acute liver damage in mice and a set of genes that were consistently altered in a novel chronic mouse model and patients of chronic liver disease. Especially the latter demonstrates that, although major interspecies differences remain, there is a common and consistent transcriptomic response to chronic liver damage in mice and humans. This set of genes could be further investigated to study the pathophysiology of the liver in in-vitro and in-vivo studies.

sections/03-scrna-seq-benchmark.Rmd

@@ -226,7 +226,7 @@ My analysis suggested at different points that the performance of TF and pathway
 
 ## Conclusions
 
-my systematic and comprehensive benchmark study suggests that functional analysis tools that rely on manually curated footprint gene sets are effective in inferring TF and pathway activity from scRNA-seq data, partially outperforming tools specifically designed for scRNA-seq analysis. In particular, the performance of DoRothEA and PROGENy was consistently better than all other tools. I showed the limits of both tools with respect to low gene coverage. I also provided recommendations on how to use DoRothEA’s and PROGENy’s gene sets in the best way dependent on the number of cells, reflecting the amount of available information, and sequencing depths. Furthermore, I showed that TF and pathway activities are rich in cell-type-specific information with a reduced amount of noise and provide an intuitive way of interpretation and hypothesis generation. I provide my benchmark data and code to the community for further assessment of methods for functional analysis.
+My systematic and comprehensive benchmark study suggests that functional analysis tools that rely on manually curated footprint gene sets are effective in inferring TF and pathway activity from scRNA-seq data, partially outperforming tools specifically designed for scRNA-seq analysis. In particular, the performance of DoRothEA and PROGENy was consistently better than all other tools. I showed the limits of both tools with respect to low gene coverage. I also provided recommendations on how to use DoRothEA’s and PROGENy’s gene sets in the best way dependent on the number of cells, reflecting the amount of available information, and sequencing depths. Furthermore, I showed that TF and pathway activities are rich in cell-type-specific information with a reduced amount of noise and provide an intuitive way of interpretation and hypothesis generation. I provide my benchmark data and code to the community for further assessment of methods for functional analysis.
 
 ## Availability of data and materials
 The code to perform all presented studies is written in R [@rcoreteam_software_2020; @gentleman_2004; @wickham_2016] and is freely available [on GitHub](https://github.com/saezlab/FootprintMethods_on_scRNAseq). The datasets supporting the conclusions of this article are available at [Zenodo](https://doi.org/10.5281/zenodo.3564179).