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• {"link"=>{"url"=>"http://www.citeulike.org/group/10570/article/13374745"}, "post_time"=>"2014-11-06 10:30:07", "tag"=>["informatics", "staffpaper"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pcbi.1003824"}, "group"=>"Journal picks", "group_id"=>"10570", "article_id"=>"13374745"}
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Mendeley | Further Information

{"title"=>"Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets", "type"=>"journal", "authors"=>[{"first_name"=>"Jean Baptiste", "last_name"=>"Pettit", "scopus_author_id"=>"55756822600"}, {"first_name"=>"Raju", "last_name"=>"Tomer", "scopus_author_id"=>"55584325200"}, {"first_name"=>"Kaia", "last_name"=>"Achim", "scopus_author_id"=>"55245942600"}, {"first_name"=>"Sylvia", "last_name"=>"Richardson", "scopus_author_id"=>"7202589117"}, {"first_name"=>"Lamiae", "last_name"=>"Azizi", "scopus_author_id"=>"55206503900"}, {"first_name"=>"John", "last_name"=>"Marioni", "scopus_author_id"=>"13103737300"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"25254363", "doi"=>"10.1371/journal.pcbi.1003824", "sgr"=>"84907587700", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "scopus"=>"2-s2.0-84907587700", "issn"=>"15537358", "pui"=>"600082117"}, "id"=>"8f883691-72a0-3f68-88e3-e69c92281e9a", "abstract"=>"Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.", "link"=>"http://www.mendeley.com/research/identifying-cell-types-spatially-referenced-singlecell-expression-datasets", "reader_count"=>79, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>29, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>29, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>3, "Agricultural and Biological Sciences"=>42, "Medicine and Dentistry"=>3, "Neuroscience"=>2, "Physics and Astronomy"=>1, "Chemistry"=>2, "Computer Science"=>13}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>2}, "Chemistry"=>{"Chemistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>42}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "Korea (South)"=>1, "United States"=>4, "Japan"=>1, "Denmark"=>1, "United Kingdom"=>4}, "group_count"=>8}

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• http://doi.org/10.1146/annurev-statistics-041715-033506
• http://doi.org/10.1038/nrg3833
• http://doi.org/10.1038/nbt.3209
• http://doi.org/10.1016/j.tibtech.2017.11.003
• http://doi.org/10.7554/eLife.26349
• http://doi.org/10.1186/s40064-016-3141-1
• http://doi.org/10.3389/fgene.2016.00163
• http://doi.org/10.1016/j.molmet.2017.04.012
• http://doi.org/10.1038/nmeth.4636
• http://doi.org/10.1016/j.gde.2016.07.005
• http://doi.org/10.1073/pnas.1603534113
• http://doi.org/10.1186/s12864-015-2303-9
• http://doi.org/10.1371/journal.pcbi.1004575
• http://doi.org/10.1007/s00429-016-1338-2

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• 4658017

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• {"id"=>"515216109419888640", "text"=>"the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-25T19:07:47Z", "user"=>"mikelove", "user_name"=>"Mike Love", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000343505629/109cb831fd170f14148815075b7579ae_normal.jpeg"}
• {"id"=>"515216147726102528", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-25T19:07:56Z", "user"=>"WonderMixTape", "user_name"=>"Shannon McWeeney", "user_profile_image"=>"http://pbs.twimg.com/profile_images/463066959660064769/7rmVSUFB_normal.jpeg"}
• {"id"=>"515236090555686912", "text"=>"RT @mikelove: the Spatially Referenced SingleCell Expression work from John Marioni's lab @PLOSCompBiol http://t.co/yAtoMWdoCo +1 github", "created_at"=>"2014-09-25T20:27:11Z", "user"=>"lopantano", "user_name"=>"Lorena Pantano", "user_profile_image"=>"http://pbs.twimg.com/profile_images/1436552743/Photo_on_2011-07-01_at_13.49_normal.jpg"}
• {"id"=>"515246051100745729", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-25T21:06:46Z", "user"=>"tangming2005", "user_name"=>"Ming Tang", "user_profile_image"=>"http://pbs.twimg.com/profile_images/442060405225967617/jOtd1ueH_normal.jpeg"}
• {"id"=>"515269728084848640", "text"=>"Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/xErhNu7blD", "created_at"=>"2014-09-25T22:40:51Z", "user"=>"DePaceLab", "user_name"=>"DePace Lab", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2150753326/Lab_logo_small_normal.jpg"}
• {"id"=>"515297683183902721", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-26T00:31:56Z", "user"=>"NiranjanTW", "user_name"=>"Niranjan Nagarajan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/492209283371520000/XGi7L_94_normal.jpeg"}
• {"id"=>"515302844908699649", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-26T00:52:26Z", "user"=>"jjmerkin", "user_name"=>"jason merkin", "user_profile_image"=>"http://abs.twimg.com/sticky/default_profile_images/default_profile_1_normal.png"}
• {"id"=>"515444659330379777", "text"=>"J. Marioni's paper from #gi2014 talk is out: IDing Cell Types w/ Spatially Referenced Single-Cell Expression Datasets http://t.co/MKET22CXSM", "created_at"=>"2014-09-26T10:15:57Z", "user"=>"benjaminlmoore", "user_name"=>"Ben Moore", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000335959952/dc9e863342be97d93b897e1e5f332b43_normal.jpeg"}
• {"id"=>"515453153756852224", "text"=>"Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/7xnekqfZcY", "created_at"=>"2014-09-26T10:49:43Z", "user"=>"JekelyLab", "user_name"=>"Jékely Lab", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3604489206/1619935bb91ff947e3e035183afc8f6b_normal.png"}
• {"id"=>"515454313817464832", "text"=>"RT @JekelyLab: Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/7xnekqfZcY", "created_at"=>"2014-09-26T10:54:19Z", "user"=>"OpposingThumb", "user_name"=>"Opposing Thumb", "user_profile_image"=>"http://pbs.twimg.com/profile_images/511507457877630976/cBXRMGah_normal.jpeg"}
• {"id"=>"515464395112345600", "text"=>"RT @JekelyLab: Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/7xnekqfZcY", "created_at"=>"2014-09-26T11:34:23Z", "user"=>"MHThorsager", "user_name"=>"Mette H-Thorsager", "user_profile_image"=>"http://pbs.twimg.com/profile_images/493448585199906816/jaKMoQTD_normal.jpeg"}
• {"id"=>"515541287554924544", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-26T16:39:55Z", "user"=>"CrowdedHead", "user_name"=>"CrowdedHead", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000498162656/cf0dceaa9125b896fb18165a3d137cc5_normal.jpeg"}
• {"id"=>"515548869137301504", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-26T17:10:03Z", "user"=>"CGATist", "user_name"=>"Chris P Ponting", "user_profile_image"=>"http://pbs.twimg.com/profile_images/465110405686116353/0qMROtgI_normal.jpeg"}
• {"id"=>"515565659858931712", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-26T18:16:46Z", "user"=>"darrenlogan", "user_name"=>"Darren Logan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2070327375/image_normal.jpg"}
• {"id"=>"515664697983782912", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-27T00:50:19Z", "user"=>"TGrantBelgard", "user_name"=>"T Grant Belgard", "user_profile_image"=>"http://pbs.twimg.com/profile_images/484126630223155200/lP4CfS7-_normal.jpeg"}
• {"id"=>"515855607132594176", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-09-27T13:28:55Z", "user"=>"silParacchini", "user_name"=>"Silvia Paracchini", "user_profile_image"=>"http://pbs.twimg.com/profile_images/1419510064/DSC_8726_normal.JPG"}
• {"id"=>"517317251788046336", "text"=>"#PLOSCompBio: Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/03OJwzhUQH #openaccess #datasets", "created_at"=>"2014-10-01T14:16:58Z", "user"=>"MRC_BSU", "user_name"=>"MRCBiostatisticsUnit", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000637157743/b1015303d55ffbbfd1ba8232beb58d1e_normal.jpeg"}
• {"id"=>"517318825680072705", "text"=>"RT @MRC_BSU: #PLOSCompBio: Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets http://t.co/03OJwzhUQH #openacc…", "created_at"=>"2014-10-01T14:23:14Z", "user"=>"CrowdedHead", "user_name"=>"CrowdedHead", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000498162656/cf0dceaa9125b896fb18165a3d137cc5_normal.jpeg"}
• {"id"=>"517936436977205248", "text"=>"RT @mikelove: the Spatially Referenced Single-Cell Expression work from John Marioni's lab in @PLOSCompBiol http://t.co/2WVOQDchA3", "created_at"=>"2014-10-03T07:17:24Z", "user"=>"Dani_petit14", "user_name"=>"daniela", "user_profile_image"=>"http://abs.twimg.com/sticky/default_profile_images/default_profile_0_normal.png"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1692139"], "description"=>"<p>This figure shows the evolution for of the mean value of across all the clusters. The red dots represent the biological data clustering (i.e the reference in our simulations scheme). The green dots represent the results obtained after clustering simulated data, which shows an underestimation of . To confirm that this underestimation come from the simulation scheme and not the clustering method, we used the simulated data as the reference to generate a \"second generation\" of simulated data, suppressing the simulation scheme bias (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g007\" target=\"_blank\">Figure 7</a>). The results of this re-simulation are shown by the blue dots, which exhibit no underestimation of . Finally the brown dots represent the mean value of on the same simulated data but spatially randomized, as expected the are now estimated to .</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183262, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g006", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validating_the_estimation_of_beta_/1183262", "title"=>"Validating the estimation of beta.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692144"], "description"=>"<p>Panel A: Adult and larval eyes in separate clusters with their top 3 most representative genes. Panel B: Mushroom bodies and their most representative genes. This visualization has been captured using the software bioWeb3D <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Pettit1\" target=\"_blank\">[53]</a>.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183267, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g009", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Eyes_and_mushroom_bodies_in_the_brain_of_Platynereis_as_clustered_by_the_HMRF_method_/1183267", "title"=>"Eyes and mushroom bodies in the brain of Platynereis as clustered by the HMRF method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692153"], "description"=>"<div><p>Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid <i>Platynereis dumereilii</i>. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.</p></div>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183276, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identifying_Cell_Types_from_Spatially_Referenced_Single_Cell_Expression_Datasets_/1183276", "title"=>"Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692150"], "description"=>"<p>The Adult eyes are well isolated (red). The larval eyes (yellow) as well. The muscles and the region of potential developing neurons are picked up as well. However if all the regions are recognizable, they are extremely poorly defined spatially with a lot of noise and little spatial coherency. This noise leads to biologically incoherent regions to be clustered with known cell types, particularly for the potential pool of developing neurones as well as the developing muscles.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183273, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g012", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clusters_obtained_with_the_independent_mixture_model_/1183273", "title"=>"Clusters obtained with the independent mixture model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692135"], "description"=>"<p>The values of are used to generate a dataset of clusters with the same gene expression profile as the reference. Each simulated voxel is then assigned to its corresponding spatial localization so that the simulated data keeps the spatial component of the biological data.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183258, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_scheme_used_to_generate_gene_expression_data_with_a_spatial_component_and_known_parameters_/1183258", "title"=>"Simulation scheme used to generate gene expression data with a spatial component and known parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692118"], "description"=>"<p>The whole larvae is hybridized with two dyed probes targetting specific mRNAs, one corresponding to a reference gene and the other a gene of interest. Using confocal microscopy, the whole larvae is visualized slice by slice and the dyed regions are reported with laser light reflecting back to the detector. Every image is then divided into 1 cell large squares which allows the reconstruction of the 3D map of expression for the two genes in the full brain. The process was repeated 86 times for key genes in Platynereis development <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Tomer1\" target=\"_blank\">[3]</a>.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183241, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g002", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Wholemount_in_situ_hybridization_expression_data_for_86_genes_in_the_full_brain_of_Platynereis_/1183241", "title"=>"Wholemount in-situ hybridization expression data for 86 genes in the full brain of Platynereis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692147"], "description"=>"<p>The yellow and red clusters are the eyes as seen on <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g009\" target=\"_blank\">figure 9</a>. The green cluster represents the developing muscles on the basal side of the larvae, as the location and the most specific genes strongly suggest. The pink cluster is a putative tissue that makes an interesting link between the eyes and the muscles. The most representative gene of this tissue is Phox2, a homeodomain protein required for the generation of visceral motor-neurons in <i>Drosophila</i><a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Briscoe1\" target=\"_blank\">[49]</a></p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183270, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g011", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_putative_tissue_of_developing_neurons_between_the_eyes_and_the_larvae_s_developing_muscles_/1183270", "title"=>"A putative tissue of developing neurons between the eyes and the larvae's developing muscles.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692112"], "description"=>"<p>Panel A shows an example of noisy data for the expression of two genes as well as the resulting binarised table of expression for four cells in the assay. Panel B shows the reference represented by 3 spatially coherent cell types inside an empty area. Panel C shows the influence of the spatial smoothness/coherency parameter on the clustering results.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183235, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_influence_of_spatial_coherence_when_clustering_noisy_data_/1183235", "title"=>"Schematic representation of the influence of spatial coherence when clustering noisy data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692152"], "description"=>"<p>Results are shown for (x axis) with the full brain, and the two left and right half separately. The y axis shows the BIC value in % of the highest BIC value for each dataset.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183275, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g014", "stats"=>{"downloads"=>5, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BIC_results_on_biological_data_/1183275", "title"=>"BIC results on biological data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692137"], "description"=>"<p>Panel A compares the performance of the MRF method with a randomly initialization with an independent mixture model also with a random initialization, the MRF method initialized with the hClust classification and hClust alone on data simulated with a spatial component. Panel B shows the Jaccard coefficient for the MRF method and independent mixture model both with a random initialization; in this case both methods are applied to simulated data that lacks a spatial component.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183260, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g005", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Jaccard_coefficient_between_8220_true_8221_and_resulting_clusters_on_the_simulated_data_with_different_methods_and_initializations_/1183260", "title"=>"Jaccard coefficient between “true” and resulting clusters on the simulated data with different methods and initializations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692151"], "description"=>"<p>For <i>rOpsin</i>, the density exhibits two clear peaks making the choice of a binarizing threshold easy. By contrast, for <i>PRDM8</i> there is no such clear threshold, making an automated binarization method hard to implement.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183274, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g013", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Densities_of_log_luminescence_values_for_two_genes_rOpsin_PRDM8_over_the_voxels_/1183274", "title"=>"Densities of log luminescence values for two genes (rOpsin, PRDM8) over the voxels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692132"], "description"=>"<p>Panel A shows the raw fluorescent microscopy capture of the gene Ascl's expression for one layer in the brain of Platynereis. Panel B shows the light intensity measured along the red line in panel A. Panel C shows the expected light intensity profile without light contamination. Because of the small scale of study, voxels surrounded by other voxels expressing a particular gene will have a higher intensity values because of nearby light contamination. Panel D shows errors introduced by the voxel cell model. Path <i>a</i> shows how regions with highly expressed genes can introduce errors through light contamination. Path <i>b</i> shows how some voxels may appear artificially void of expression because of the uneven distribution of transcripts inside the cytoplasm especially for large cells.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183255, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Light_contamination_in_in_situ_hybridization_luminescence_data_/1183255", "title"=>"Light contamination in in-situ hybridization luminescence data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692146"], "description"=>"<p>Z-projection of the expression of rOpsin (red) in both the adult eyes and the larval eyes, rOpsin3 (green) specifically in the larval eyes and co-expression areas in some areas of the larval eyes in the full brain of <i>Platynereis</i> at 48hpf. This image been obtained directly from the data obtained in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Tomer1\" target=\"_blank\">[3]</a></p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183269, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g010", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_situ_hybridization_image_for_rOpsin_and_rOpsin3_in_the_full_brain_at_48hpf_Apical_view_/1183269", "title"=>"In-situ hybridization image for rOpsin and rOpsin3 in the full brain at 48hpf (Apical view).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692142"], "description"=>"<p>The BIC is plotted on the y-axis for different values of K on the x-axis. The red and the grey points correspond to the BIC estimated when the underlying data have 17 and 7 clusters, respectively. The minimum BIC value is 18 and 7, respectively, suggesting that the MRF approach in conjunction with the BIC well estimates the optimal number of clusters.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183265, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g008", "stats"=>{"downloads"=>4, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimating_the_BIC_from_the_simulated_data_/1183265", "title"=>"Estimating the BIC from the simulated data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}
• {"files"=>["https://ndownloader.figshare.com/files/1692140"], "description"=>"<p>For an example cluster , gene may only be expressed in half of the voxels. This will yield . However, in the biological data, the voxels expressing gene may be spatially coherent (i.e., located close to one another), leading to a reduced area of expression discontinuity (the green line). By contrast, in the simulated data the expression of such a gene will lose its spatial coherency, leading to an increased area of expression discontinuity. The number of voxels having a neighbour with some differences in the gene expression pattern is directly linked to the value of through the energy function (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#s4\" target=\"_blank\">Methods</a>). This explains the underestimation of observed in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g006\" target=\"_blank\">Figure 6</a>.</p>", "links"=>[], "tags"=>["HMRF", "study gene expression levels", "Markov Random Field", "putatively novel cell types", "cell types", "cell gene expression data", "study expression patterns", "marine annelid Platynereis dumereilii"], "article_id"=>1183263, "categories"=>["Uncategorised"], "users"=>["Jean-Baptiste Pettit", "Raju Tomer", "Kaia Achim", "Sylvia Richardson", "Lamiae Azizi", "John Marioni"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003824.g007", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decrease_in_spatial_coherency_due_to_the_simulation_scheme_/1183263", "title"=>"Decrease in spatial coherency due to the simulation scheme.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-25 04:10:30"}

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Relative Metric

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F1000Prime