DGCA: A comprehensive R package for Differential Gene Correlation Analysis

McKenzie, Andrew; Katsyv, Igor; Song, Won Min; Wang, Minghui; Zhang, Bin

doi:10.1186/s12918-016-0349-1

Cited by 199 publications

(200 citation statements)

References 79 publications

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“…Cell identity is defined not only by expression of specific genes, but also interactions between them. To assess which interactions define Treg and Tmem identity in NLTs or Treg identity between NLTs, we used the DGCA package (McKenzie et al 2016) , which relies on the Fisher z-score transformation of the Spearman correlation values to perform a differential correlation test on gene pairs. For these tests, only genes identified as NLT markers and expressed in more than 5 cells in both conditions tested were used.…”

Section: Differential Co-expression Analysismentioning

confidence: 99%

Single cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation

Miragaia

Gomes

Chomka

et al. 2017

Preprint

152

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SummaryNon-lymphoid tissues (NLTs) harbour a pool of adaptive immune cells, the development and phenotype of which remains largely unexplored. Here, we used single-cell RNA-seq to characterise CD4 + regulatory (Treg) and memory (Tmem) T cells in mouse skin and colon, the respective draining lymph nodes and spleen. From this data, we modelled a continuous lymphoid-to-NLT trajectory for Treg, and reconstructed the mechanisms of cell migration and NLT adaption. This revealed a shared transcriptional programme of NLT priming in both skin and colon-associated lymph nodes, followed by tissue-specific adaptation. Predicted migration kinetics were validated using a melanoma-induction model, emphasizing the relevance of key regulators and receptors, including Batf, Rora, Ccr8, Samsn1 . Finally, we profiled human blood and NLT Treg and Tmem cells, identifying cross-mammalian conserved tissue signatures. In summary, we have identified molecular signals mediating 1 . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/217489 doi: bioRxiv preprint first posted online Nov. 22, 2017; NLT Treg recruitment and tissue adaptation through the combined use of computational prediction and in vivo validation.

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Section: Differential Co-expression Analysismentioning

confidence: 99%

Single cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation

Miragaia

Gomes

Chomka

et al. 2017

Preprint

152

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“…Using the R package DGCA [48], Pearson correlation coefficients (r) and their corresponding ‫‬ values were calculated for each pair of genes across multiple samples, which were subsequently classified as having a significant ‫(‬ < 0.05) positive correlation (+), a significant negative correlation (-), or not significantly different from zero (0). Fisher's Z-test [49] was used to identify significant correlation changes between the homoeologous and the diploid (expected) r values.…”

Section: Gene Expression Correlation Analysismentioning

confidence: 99%

Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids

Grover

Arick

et al. 2019

Preprint

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Biographical noteGuanjing Hu (https://orcid.org/0000-0001-8552-7394) is a researcher in evolutionary genomics, functional genomics, and whole-genome duplication (polyploidization).Corrinne E. Grover (https://orcid.org/0000-0003-3878-5459) is a researcher in evolutionary genomics, molecular Evolution, and plant Systematics and Evolution. 0000-0003-3878-5459 Mark A. Arick II (https://orcid.org/0000-0002-7207-3052) is a researcher in genomics, bioinformatics, and computational sciences.Meiling Liu (https://orcid.org/0000-0001-7953-1506) is a researcher in statistics and bioinformatics.Daniel G. Peterson (https://orcid.org/0000-0002-0274-5968) is a researcher in genomics, biocomputing, and biotechnology. Jonathan F. Wendel (https://orcid.org/0000-0003-2258-5081) is a researcher in evolutionary genomics, molecular Evolution, and plant Systematics and Evolution. ABSTRACTPolyploidy is a widespread phenomenon throughout eukaryotes. Due to the coexistence of duplicated genomes, polyploids offer unique challenges for estimating gene expression levels, which is essential for understanding the massive and various forms of transcriptomic responses accompanying polyploidy. Although previous studies have explored the bioinformatics of polyploid transcriptomic profiling, the causes and consequences of inaccurate quantification of transcripts from duplicated gene copies have not been addressed. Using transcriptomic data from the cotton genus (Gossypium) as an example, we present an analytical workflow to evaluate a variety of bioinformatic method choices at different stages of RNA-seq analysis, from homoeolog expression quantification to downstream analysis used to infer key phenomena of polyploid expression evolution. In general, GSNAP-PolyCat outperforms other quantification pipelines tested, and its derived expression dataset best represents the expected homoeolog expression and co-expression divergence. The performance of co-expression network analysis was less affected by homoeolog quantification than by network construction methods, where weighted networks outperformed binary networks. By examining the extent and consequences of homoeolog read ambiguity, we illuminate the potential artifacts that may affect our understanding of duplicate gene expression, including an over-estimation of homoeolog coregulation and the incorrect inference of subgenome asymmetry in network topology. Taken together, our work points to a set of reasonable practices that we hope are broadly applicable to the evolutionary exploration of polyploids. RNA-seq read mapping and homoeolog-specific read partitioningThe following five pipelines were each independently applied to the diploid and AD polyploid datasets.GSNAP-PolyCat. This pipeline utilizes the SNP-tolerant capabilities of GSNAP [v2016-08-16] [29] to map polyploid reads to a single diploid progenitor genome (here, G. raimondii; [30]). The SNP-tolerant feature of GSNAP permits equivocal mapping of both A-and D-diploid derived reads based on a priori SNP information. Here, we used a previousl...

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“…In order to highlight newly appearing or disappearing correlations between NATs and their corresponding PCTs in the tumor, differential correlation analysis between all pairs of PCTs and NATs was performed with the DGCA software (v. 1.0.1) 19 . Complete results can be found in Supplementary Table, Table 3 showing that genes that are well known in the breast cancer field are displaying deregulated correlation of expression with their antisense transcripts.…”

Section: Positive Correlation Of Expression Between Nat and Their Cormentioning

confidence: 99%

Transcriptome wide analysis of natural antisense transcripts shows their potential role in breast cancer

Wenric

ElGuendi

Caberg³

et al. 2017

Preprint

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Non-coding RNAs (ncRNA) represent at least 1/5 of the mammalian transcript amount, and about 90% of the genome length is actively transcribed. Many ncRNAs have been demonstrated to play a role in cancer. Among them, natural antisense transcripts (NAT) are RNA sequences which are complementary and overlapping to those of protein-coding transcripts (PCT). NATs were punctually described as regulating gene expression, and are expected to act more frequently in cis than other ncRNAs that commonly function in trans. In this work, 22 breast cancers expressing estrogen receptors and their paired healthy tissues were analyzed by strand-specific RNA sequencing. To highlight the potential role of NATs in gene regulations occurring in breast cancer, three different gene extraction methods were used: differential expression analysis of NATs between tumor and healthy tissues, differential correlation analysis of paired NAT/PCT between tumor and healthy tissues, and NAT/PCT read count ratio variation between tumor and healthy tissues. Each of these methods yielded lists of NAT/PCT pairs that were demonstrated to be enriched in survival-associated genes on an independent cohort (TCGA). This work allows to highlight NAT lists that display a strong potential to affect the expression of genes involved in the breast cancer pathology.

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DGCA: A comprehensive R package for Differential Gene Correlation Analysis

Cited by 199 publications

References 79 publications

Single cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation

Single cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation

Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids

Transcriptome wide analysis of natural antisense transcripts shows their potential role in breast cancer

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