Two-Stage, In Silico Deconvolution of the Lymphocyte Compartment of the Peripheral Whole Blood Transcriptome in the Context of Acute Kidney Allograft Rejection
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{"title"=>"Two-stage, in Silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection", "type"=>"journal", "authors"=>[{"first_name"=>"Casey P.", "last_name"=>"Shannon", "scopus_author_id"=>"55110393200"}, {"first_name"=>"Robert", "last_name"=>"Balshaw", "scopus_author_id"=>"6602319443"}, {"first_name"=>"Raymond T.", "last_name"=>"Ng", "scopus_author_id"=>"7102153783"}, {"first_name"=>"Janet E.", "last_name"=>"Wilson-McManus", "scopus_author_id"=>"6508362027"}, {"first_name"=>"Paul", "last_name"=>"Keown", "scopus_author_id"=>"7005536176"}, {"first_name"=>"Robert", "last_name"=>"McMaster", "scopus_author_id"=>"7006182682"}, {"first_name"=>"Bruce M.", "last_name"=>"McManus", "scopus_author_id"=>"7102867282"}, {"first_name"=>"David", "last_name"=>"Landsberg", "scopus_author_id"=>"7003273132"}, {"first_name"=>"Nicole M.", "last_name"=>"Isbel", "scopus_author_id"=>"35454123200"}, {"first_name"=>"Greg", "last_name"=>"Knoll", "scopus_author_id"=>"7005077442"}, {"first_name"=>"Scott J.", "last_name"=>"Tebbutt", "scopus_author_id"=>"6701561071"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84899631863", "pui"=>"372981474", "doi"=>"10.1371/journal.pone.0095224", "sgr"=>"84899631863", "pmid"=>"24733377"}, "id"=>"cade2736-8abf-3aab-9425-5e4b939f76da", "abstract"=>"Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay.", "link"=>"http://www.mendeley.com/research/twostage-silico-deconvolution-lymphocyte-compartment-peripheral-whole-blood-transcriptome-context-ac", "reader_count"=>44, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Other"=>6, "Student > Master"=>4, "Student > Bachelor"=>2, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Other"=>6, "Student > Master"=>4, "Student > Bachelor"=>2, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>8, "Mathematics"=>2, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>10, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemistry"=>1, "Computer Science"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"New Zealand"=>1, "United States"=>1, "United Kingdom"=>1, "Israel"=>1, "Germany"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1463589"], "description"=>"<p>Datasets and applications</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays"], "article_id"=>998560, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Datasets_and_applications_/998560", "title"=>"Datasets and applications", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463587"], "description"=>"<p>Gene set enrichment of cell type-specific gene lists – Benita <i>et al.</i> dataset</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "enrichment", "type-specific", "lists", "benita"], "article_id"=>998558, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.t002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_set_enrichment_of_cell_type_specific_gene_lists_Benita_et_al_dataset_/998558", "title"=>"Gene set enrichment of cell type-specific gene lists – Benita <i>et al.</i> dataset", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463584"], "description"=>"<p>The tissue specificity of the cell type-specific gene lists identified in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095224#pone-0095224-g002\" target=\"_blank\">Figure 2</a> is assessed by visualizing their median enrichment across a wide range of tissues (<b>A</b>). Significance of enrichment of each cell type-specific gene list in each tissue is assessed by hypergeometric test (<b>B</b>).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "type-specific", "differentially", "probe-sets", "establishes"], "article_id"=>998555, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_analysis_of_cell_type_specific_differentially_expressed_probe_sets_establishes_their_plausibility_/998555", "title"=>"Enrichment analysis of cell type-specific differentially expressed probe-sets establishes their plausibility.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463583"], "description"=>"<p>The cellular composition of peripheral whole blood is plotted for 48 kidney transplant recipients (24AR, 24NR) at the time of a treatable acute rejection episode. Actual cell type proportions were obtained from total leukocyte differentials (time-matched to the RNA collection for the rejection episode), only available for a subset of the 48 subjects (<b>A</b>; n = 41, 18AR, 23NR), while predicted cell type proportions were inferred from peripheral whole blood microarray data using the basis matrix from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095224#pone-0095224-g001\" target=\"_blank\"><b>Figure 1</b></a> (<b>B</b>; n = 48, 24AR, 24NR). The proportions of all seven cell-types included in the basis matrix are predicted, but only neutrophils and lymphocyte sub-types are shown. Significant differences between groups are labeled (Wilcoxon rank-sum test; p≤0.05 *, p≤0.01 **). Cell type-specific differential expression is assessed using csSAM for 48 kidney transplant recipients (24AR, 24NR) using either actual cell type proportions alone (<b>C</b>), or predicted cell type proportions (inferred from peripheral whole blood microarray data) alone (<b>D</b>). Cell type-specific differential expression was assessed for all seven cell-types included in the basis matrix, but results are shown only for neutrophils, B cells, CD4+, CD8+ T cells and NK cells (no signal in monocytes, eosinophils). The number of probe-sets called significantly differentially expressed at various false discovery rate (FDR) values is plotted for the one-tailed up and one-tailed down hypotheses (red and blue lines, respectively). A cutoff FDR = 0.30 was selected for discovery purposes (dashed line).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "lymphocyte", "cellular", "compartment", "provides", "insights", "acute", "kidney", "allograft"], "article_id"=>998554, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.g002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deconvolution_of_the_lymphocyte_cellular_compartment_provides_additional_insights_into_the_biology_of_acute_kidney_allograft_rejection_/998554", "title"=>"Deconvolution of the lymphocyte cellular compartment provides additional insights into the biology of acute kidney allograft rejection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463591", "https://ndownloader.figshare.com/files/1463592", "https://ndownloader.figshare.com/files/1463593", "https://ndownloader.figshare.com/files/1463594", "https://ndownloader.figshare.com/files/1463595", "https://ndownloader.figshare.com/files/1463596"], "description"=>"<div><p>Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, <i>in silico</i> deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay.</p></div>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "deconvolution", "lymphocyte", "compartment", "peripheral", "transcriptome", "acute", "kidney", "allograft", "rejection"], "article_id"=>998562, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0095224.s001", "https://dx.doi.org/10.1371/journal.pone.0095224.s002", "https://dx.doi.org/10.1371/journal.pone.0095224.s003", "https://dx.doi.org/10.1371/journal.pone.0095224.s004", "https://dx.doi.org/10.1371/journal.pone.0095224.s005", "https://dx.doi.org/10.1371/journal.pone.0095224.s006"], "stats"=>{"downloads"=>1, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Two_Stage_In_Silico_Deconvolution_of_the_Lymphocyte_Compartment_of_the_Peripheral_Whole_Blood_Transcriptome_in_the_Context_of_Acute_Kidney_Allograft_Rejection/998562", "title"=>"Two-Stage, <i>In Silico</i> Deconvolution of the Lymphocyte Compartment of the Peripheral Whole Blood Transcriptome in the Context of Acute Kidney Allograft Rejection", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463586"], "description"=>"<p>The composition of the peripheral whole blood samples was inferred from mixed expression data for all 48 subjects (24 AR, 24 NR) at the time of a treatable acute rejection episode, as well as at baseline (23 AR, 20 NR) and after rejection had resolved (20 AR, 19 NR) when expression data was available (<b>A</b>). The mean (and bootstrapped confidence intervals) of the proportions of neutrophils, B cells, CD4+, CD8+ T cells and NK cells are plotted for each group, at each time point. Significant differences between groups are labeled (Wilcoxon rank-sum test; p≤0.05 *, p≤0.01 **). Cell type specific differential expression analysis (csSAM) was performed using the sample composition information inferred above (<b>B</b>). Cell type-specific differential expression was assessed for all seven cell-types included in the basis matrix, but results are shown only for neutrophils, B cells, CD4+, CD8+ T cells and NK cells (no signal in monocytes, eosinophils). Cell types not detectable in more than 75% of subjects at a given time point were omitted from the model. For each time point, the number of probe-sets called significantly differentially expressed at various false discovery rate (FDR) values is plotted for the one-tailed up and one-tailed down hypotheses (red and blue lines, respectively). A cutoff FDR ≤0.30 was selected for discovery purposes (dashed line).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "lymphocyte", "compartment", "peripheral", "elucidates", "patterns", "differential", "type-specific", "ar", "nr", "subjects", "treatable", "acute", "kidney", "allograft"], "article_id"=>998557, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.g005", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deconvolution_of_the_lymphocyte_compartment_of_peripheral_whole_blood_elucidates_patterns_of_differential_composition_and_differential_cell_type_specific_gene_expression_between_AR_and_NR_subjects_before_during_and_after_an_episode_of_treatable_acute_kid/998557", "title"=>"Deconvolution of the lymphocyte compartment of peripheral whole blood elucidates patterns of differential composition, and differential cell type-specific gene expression, between AR and NR subjects before, during and after an episode of treatable acute kidney allograft rejection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463585"], "description"=>"<p>The peripheral whole blood expression of top-ranked, cell type-specific differentially expressed genes in B, CD4+ T, and NK cells are plotted for two independent sets of kidney transplant subjects (<b>A</b>; 24AR, 24NR; microarray; <b>B</b>; 13AR, 31NR; nCounter). A ratio score computed so as to maximize signal between AR and NR subjects, and overcome the convolution issue (see Methods), is also shown. Significant differences between groups are labeled (Wilcoxon rank-sum test; p≤0.05 *, p≤0.01 **).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "type-specific"], "article_id"=>998556, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validating_a_cell_type_specific_hypothesis_in_an_independent_cohort_/998556", "title"=>"Validating a cell type-specific hypothesis in an independent cohort.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463582"], "description"=>"<p>The performance of reverse deconvolution using the optimal basis matrix is assessed by visualizing measured and predicted cell type proportions for neutrophils, lymphocytes and monocytes in <b>training</b> (pediatric kidney [n = 24] and heart [n = 26] allograft recipients) and test (kidney allograft recipients [n = 41]) sets of subjects. Predicted lymphocytes proportions are the sum of the predicted proportions for B cells, CD4+, CD8+ T cells and NK cells. Measured and predicted proportions are plotted and the adjusted coefficient of determination (adj. R<sup>2</sup>) and root mean squared error (RMSE) reported in both the <b>training</b> (n = 50; <b>A</b>) and test sets (n = 41; <b>B</b>).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "neutrophil", "lymphocyte", "proportions", "peripheral", "genome", "subset", "informative"], "article_id"=>998553, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.g001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_neutrophil_and_lymphocyte_proportions_of_peripheral_whole_blood_can_be_predicted_from_whole_genome_expression_data_using_a_minimal_subset_of_informative_probe_sets_/998553", "title"=>"The neutrophil and lymphocyte proportions of peripheral whole blood can be predicted from whole genome expression data using a minimal subset of informative probe-sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463590"], "description"=>"<p>Gene set enrichment of cell type-specific gene lists – MsigDB C2 KEGG Canonical Pathways</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "enrichment", "type-specific", "lists", "msigdb", "c2", "kegg", "canonical"], "article_id"=>998561, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.t004", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_set_enrichment_of_cell_type_specific_gene_lists_8211_MsigDB_C2_KEGG_Canonical_Pathways_/998561", "title"=>"Gene set enrichment of cell type-specific gene lists – MsigDB C2 KEGG Canonical Pathways", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-14 03:35:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1463588"], "description"=>"<p>Gene set enrichment of cell type-specific gene lists – MSigDB C7 Immunologic Signatures</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Transcriptome analysis", "Genome expression analysis", "genetics", "gene expression", "Gene regulation", "Gene identification and analysis", "genomics", "Molecular genetics", "Computing methods", "Computer inferencing", "Surgical and invasive medical procedures", "transplantation", "Bioassays and physiological analysis", "microarrays", "enrichment", "type-specific", "lists", "msigdb", "c7", "immunologic"], "article_id"=>998559, "categories"=>["Biological Sciences"], "users"=>["Casey P. Shannon", "Robert Balshaw", "Raymond T. Ng", "Janet E. Wilson-McManus", "Paul Keown", "Robert McMaster", "Bruce M. McManus", "David Landsberg", "Nicole M. Isbel", "Greg Knoll", "Scott J. Tebbutt"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0095224.t003", "stats"=>{"downloads"=>8, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_set_enrichment_of_cell_type_specific_gene_lists_8211_MSigDB_C7_Immunologic_Signatures_/998559", "title"=>"Gene set enrichment of cell type-specific gene lists – MSigDB C7 Immunologic Signatures", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-14 03:35:31"}

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