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- Kellgren, Therese, 1983-
(författare)
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Hidden patterns that matter : statistical methods for analysis of DNA and RNA data
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Understanding how the genetic variations can affect characteristics and function of organisms can help researchers and medical doctors to detect genetic alterations that cause disease and reveal genes that causes antibiotic resistance. The opportunities and progress associated with such data come however with challenges related to statistical analysis. It is only by using properly designed and employed tools, that we can extract the information about hidden patterns. In this thesis we present three types of such analysis. First, the genetic variant in the gene COL17A1 that causes corneal dystrophy with recurrent erosions is reveled. By studying Next-generation sequencing data, the order of the nucleotides in the DNAsequence was be obtained, which enabled us to detect interesting variants in the genome. Further, we present results of an experimental design study with the aim to make the best selection from a family that is affected by an inherited disease. In second part of the work, we analyzed a novel antibiotic resistance Staphylococcus epidermidis clone that is only found in northern Europe. By investigating its genetic data, we revealed similarities to a world known antibiotic resistance clone. As a result, the antibiotic resistance profile is established from the DNA sequences. Finally, we also focus on the challenges related to the abundance of genetic data from different sources. The increasing number of public gene expression datasets gives us opportunity to increase our understanding by using information from multiple sources simultaneously. Naturally, this requires merging independent datasets together. However, when doing so, the technical and biological variation in the joined data increases. We present a pre-processing method to construct gene co-expression networks from a large diverse gene-expression dataset.
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| 2. |
- Law, Simon R, et al.
(författare)
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Centralization Within Sub-Experiments Enhances the Biological Relevance of Gene Co-expression Networks : A Plant Mitochondrial Case Study
- 2020
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network. Author Summary Gene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a "core" GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study.
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| 3. |
- Li, Xingru, et al.
(författare)
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A Detailed Flow Cytometric Analysis of Immune Activity Profiles in Molecular Subtypes of Colorectal Cancer
- 2020
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Ingår i: Cancers. - : MDPI. - 2072-6694. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- The local anti-tumour immune response has important prognostic value in colorectal cancer (CRC). In the era of immunotherapy, a better understanding of the immune response in molecular subgroups of CRC may lead to significant advances in personalised medicine. On this note, microsatellite instable (MSI) tumours have been characterised by increased immune infiltration, suggesting MSI as a marker for immune inhibitor checkpoint therapy. Here, we used flow cytometry to perform a comprehensive analysis of immune activity profiles in tumour tissues, adjacent non-malignant tissues and blood, from a cohort of 69 CRC patients. We found several signs of immune suppression in tumours compared to adjacent non-malignant tissues, including T cells more often expressing the immune checkpoint molecules programmed cell death protein (PD-1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). We further analysed immune cell infiltration in molecular subgroups of CRC. MSI tumours were indeed found to be associated with increased immune infiltration, including increased fractions of PD-1+ T cells. No correlation was, however, found between MSI and the fraction of CTLA-4+ T cells. Interestingly, within the group of patients with microsatellite stable (MSS) tumours, some also presented with increased immune infiltration, including comparably high portions of PD-1+ T cells, but also CTLA-4+ T cells. Furthermore, no correlation was found between PD-1+ and CTLA-4+ T cells, suggesting that different tumours may, to some extent, be regulated by different immune checkpoints. We further evaluated the distribution of immune activity profiles in the consensus molecular subtypes of CRC. In conclusion, our findings suggest that different immune checkpoint inhibitors may be beneficial for selected CRC patients irrespective of MSI status. Improved predictive tools are required to identify these patients.
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