Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis
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{"title"=>"Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Wei", "last_name"=>"Zhang", "scopus_author_id"=>"56760577700"}, {"first_name"=>"Jae Woong", "last_name"=>"Chang", "scopus_author_id"=>"7601550211"}, {"first_name"=>"Lilong", "last_name"=>"Lin", "scopus_author_id"=>"57037436200"}, {"first_name"=>"Kay", "last_name"=>"Minn", "scopus_author_id"=>"6701859567"}, {"first_name"=>"Baolin", "last_name"=>"Wu", "scopus_author_id"=>"14009097700"}, {"first_name"=>"Jeremy", "last_name"=>"Chien", "scopus_author_id"=>"7202435241"}, {"first_name"=>"Jeongsik", "last_name"=>"Yong", "scopus_author_id"=>"7102704688"}, {"first_name"=>"Hui", "last_name"=>"Zheng", "scopus_author_id"=>"7403441193"}, {"first_name"=>"Rui", "last_name"=>"Kuang", "scopus_author_id"=>"7004190781"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"607623909", "arxiv"=>"1403.5029", "pmid"=>"26699225", "doi"=>"10.1371/journal.pcbi.1004465", "scopus"=>"2-s2.0-84953206419", "issn"=>"15537358", "sgr"=>"84953206419"}, "id"=>"d284bce1-25c9-3535-b474-d9bf66b82d18", "abstract"=>"High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification.", "link"=>"http://www.mendeley.com/research/networkbased-isoform-quantification-rnaseq-data-cancer-transcriptome-analysis", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>4, "Student > Bachelor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>4, "Student > Bachelor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>17, "Medicine and Dentistry"=>3, "Computer Science"=>9, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Japan"=>1, "Italy"=>1, "United Kingdom"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2614490"], "description"=>"<p>In this toy example, transcript <i>T</i><sub><i>ik</i></sub> has four neighbor transcripts {<i>T</i><sub><i>g</i><sub>1</sub><i>a</i></sub>, <i>T</i><sub><i>g</i><sub>2</sub><i>b</i></sub>, <i>T</i><sub><i>g</i><sub>3</sub><i>c</i></sub>, <i>T</i><sub><i>g</i><sub>4</sub><i>d</i></sub>}, which are transcripts from <i>g</i><sub>1</sub>, <i>g</i><sub>2</sub>, <i>g</i><sub>3</sub> and <i>g</i><sub>4</sub>, respectively. The neighborhood expression <i>ϕ</i><sub><i>ik</i></sub> of <i>T</i><sub><i>ik</i></sub> is then calculated as the average of its neighbor transcripts’ expressions and further normalized by transcript length, represented as the vector product between <b><i>π</i></b> and <b><i>S</i></b><sub>*,(<i>i</i>,<i>k</i>)</sub> normalized by the number of neighbors ∑<b><i>S</i></b><sub>*,(<i>i</i>,<i>k</i>)</sub> and the transcript length <i>l</i><sub><i>ik</i></sub> in the figure.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628768, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g002", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Transcript_interaction_neighborhood_/1628768", "title"=>"Transcript interaction neighborhood.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614492"], "description"=>"<p>The correlation coefficients between transcript expressions across all patient samples are first calculated in each dataset for each pair of transcripts by Cufflinks. The correlation coefficients are then sorted from largest to smallest and grouped into bins of size 1000 each. The x-axis is the index of the bins with lower index indicating larger correlation coefficients. The y-axis is the number of the pairs among the 1000 pairs of transcripts in each bin that coincide with protein domain-domain interaction between the transcript pair. The red line is the smooth plot by fitting local linear regression method with weighted linear least squares (LOWESS) to the curves. <i>p</i>-value is reported by chi-square test. (A) Co-expressions are calculated based on the small gene list. (B) Co-expressions are calculated based on the large gene list. In both (A) and (B), the left column shows the plots based on the connected transcript pairs in the transcript network and the right column shows the plots based on the transcript pairs with distance up to 2 in the network.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628770, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g003", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_transcript_co_expression_and_protein_domain_domain_interaction_in_TCGA_datasets_/1628770", "title"=>"Correlation between transcript co-expression and protein domain-domain interaction in TCGA datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-06 09:32:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614493"], "description"=>"<p>In (A) and (B) x-axis are labeled by the compared methods and different λ parameters of Net-RSTQ. The bar plots show the results of running Net-RSTQ with 100 randomized networks. In (C) and (D), x-axis are the percentage of edges that are removed from the networks. The plots show the results of running Net-RSTQ with the incomplete networks. (A) and (C) report the results of 109 transcripts of the isoforms in the same gene with different domain-domain interactions. (B) and (D) report the results of 712 isoforms in genes with multiple isoforms.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628771, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g004", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_estimated_transcript_expressions_and_ground_truth_in_simulation_/1628771", "title"=>"Correlation between estimated transcript expressions and ground truth in simulation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614494"], "description"=>"<p>(A) The scatter plots compare the reported relative proportion of each pair of the isoforms of each gene between the computational methods (Net-RSTQ, base EM, Cufflinks, and RSEM) and qRT-PCR experiments. The proportions of the two compared isoforms in a pair are normalized to adding to 1. The x-axis and y-axis are the relative proportion of one of the two isoform (the other is 1 minus the proportion) reported by qRT-PCR and the computational methods, respectively. The scatter points aligning closer to the diagonal line indicate better estimations by a computational method matching to the qRT-PCR results. The unshaded gradient around the diagonal line shows the regions with scatter differences less than 0.1, 0.15, 0.2 and 0.25, within which the estimations are more similar to the qRT-PCR results. (B)-(D) The scatter plots on each individual dataset. (E) The table shows the percentage of predictions by each method within the unshaded regions and the overall Root Mean Square Error of the predictions by each method compared to the qRT-PCR results.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628772, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g005", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_by_comparison_with_qRT_PCR_results_/1628772", "title"=>"Validation by comparison with qRT-PCR results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614495"], "description"=>"<p>Comparison of the classification results by the randomized networks and the true network. The λ parameter was fixed to be 0.1 in all the experiments. The blue star and the red star represent the results with the real network and without network (base EM), respectively. The boxplot shows the results with the randomized networks.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628773, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g006", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_analysis_with_randomized_networks_/1628773", "title"=>"Statistical analysis with randomized networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614496"], "description"=>"<p>The plots show the CPU time (Intel Xeon E5-1620 with 3.70GHZ) for running the Net-RSTQ algorithm one three networks, the small transcript network, the large transcript network, and an artificial huge network of 10000 transcripts.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628774, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g007", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Running_time_/1628774", "title"=>"Running time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614497", "https://ndownloader.figshare.com/files/2614498", "https://ndownloader.figshare.com/files/2614499", "https://ndownloader.figshare.com/files/2614500", "https://ndownloader.figshare.com/files/2614501", "https://ndownloader.figshare.com/files/2614502", "https://ndownloader.figshare.com/files/2614503", "https://ndownloader.figshare.com/files/2614504", "https://ndownloader.figshare.com/files/2614505", "https://ndownloader.figshare.com/files/2614507", "https://ndownloader.figshare.com/files/2614509", "https://ndownloader.figshare.com/files/2614510", "https://ndownloader.figshare.com/files/2614512", "https://ndownloader.figshare.com/files/2614513", "https://ndownloader.figshare.com/files/2614514"], "description"=>"<div><p>High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at <a href=\"http://compbio.cs.umn.edu/Net-RSTQ/\" target=\"_blank\">http://compbio.cs.umn.edu/Net-RSTQ/</a>.</p></div>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628775, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004465.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004465.s015"], "stats"=>{"downloads"=>16, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_Based_Isoform_Quantification_with_RNA_Seq_Data_for_Cancer_Transcriptome_Analysis_/1628775", "title"=>"Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-01-05 14:52:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2614488"], "description"=>"<p>(A) The subnetwork shows the domain-domain interactions among transcripts from four human genes, CD79B, CD79A, LCK and SYK. In the network, the nodes represent isoform transcripts, which are further grouped and annotated by their gene name; and the edges represent domain-domain interactions between two transcripts. Each edge is also annotated by the interacting domains in the two transcripts. (B) RefSeq transcript annotations of CD79A and CD79B are shown with Pfam domain marked in color. The Pfam domains were detected with Pfam-Scan software. Note that no interaction is included between transcripts NM_001039933 and NM_000626 of gene CD79B without assuming self-interactions for modeling simplicity. For better visualization, only the interactions coincide with PPI are shown in the figure.</p>", "links"=>[], "tags"=>["breast cancer cell line", "em", "transcript abundances", "interaction", "TCGA", "transcript abundance estimation", "cancer cell line", "Cancer Genome Atlas", "isoform transcript quantifications", "data", "gene", "patient sample classification"], "article_id"=>1628766, "categories"=>["Biological Sciences"], "users"=>["Wei Zhang", "Jae-Woong Chang", "Lilong Lin", "Kay Minn", "Baolin Wu", "Jeremy Chien", "Jeongsik Yong", "Hui Zheng", "Rui Kuang"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004465.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_isoform_transcript_network_based_on_protein_domain_domain_interactions_/1628766", "title"=>"An isoform transcript network based on protein domain-domain interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-05 14:52:26"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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