Wolfram Cloud Resource Object

Data Resource

Human Protein Atlas

Source Notebook

Data from The Human Protein Atlas

Examples

Basic Examples (2) 

Gene expression (nTPM) in different human tissues:

In[1]:=
Dataset@ResourceData[ ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]]]
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Expression of 50 strongest expressing genes in human tissues (Red color indicates data not available):

In[2]:=
RNAExpressionData = ResourceData[ ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]]]; tissues = RNAExpressionData[[1]]; ensemblIDGeneAssoc = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "Ensembl ID gene name association"]; ensemblIDs = Keys@RNAExpressionData[[2 ;; -1]]; genes = ensemblIDGeneAssoc /@ ensemblIDs; v = Values@RNAExpressionData; totExp = Table[Total@v[[i]] /. {"NA" -> 0}, {i, 2, Length@genes + 1}]; sv = Sort[totExp]; numGenes = 50; pos = Flatten@ Map[Position[totExp, #] &, sv[[Length@genes - numGenes ;; Length@genes]]]; highestExpressionGenes = ensemblIDs[[pos]]; highestExpressionGeneNames = Map[ensemblIDGeneAssoc, highestExpressionGenes]; ftx = MapThread[{#1, #2} &, {Range@Length@highestExpressionGeneNames, highestExpressionGeneNames}]; fty = MapThread[{#1, Rotate[Capitalize@#2, Pi/2]} &, {Range@ Length@tissues, tissues}]; array = v[[pos + 1]]; mA = {Min@array /. "NA" -> 0, Max@array /. "NA" -> 0}; ArrayPlot[array, ColorFunction -> "BlueGreenYellow", FrameTicks -> {ftx, fty}, ImageSize -> 1000, Frame -> True, FrameStyle -> Directive[Black, 12], PlotLegends -> BarLegend[{"BlueGreenYellow", mA}], PlotLabel -> Style["Highest expressing genes in human tissues (nTPM)", Black, Bold, 20]]
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Visualizations (3) 

Subgraph containing 30 randomly chosen genes from the tissue co-expression network with VertexSize scaled by VertexDegree in the graph:

In[19]:=
RNACoExpressionNetwork = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "TissueAtlas Gene co-expression graph"]; getSubGraph[graph_Graph, vertexList_] := Graph@Sort@ DeleteDuplicates@ Flatten[IncidenceList[graph, #] & /@ vertexList]; ResourceFunction[ ResourceObject[<|"Name" -> "VertexSizeScaledGraph", "ShortName" -> "VertexSizeScaledGraph", "UUID" -> "82dfde6e-106f-4322-a329-1216e9f28362", "ResourceType" -> "Function", "Version" -> "1.1.0", "Description" -> "Visualize a graph with scaled vertex size based on custom graph related measures", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$4d8c3d76bc4e42be9489e98149696195`VertexSizeScaledGraph", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/b0eb1e52-6cf3-4b6a-8efb-7950a9c63da5"]|>, ResourceSystemBase -> Automatic]][ getSubGraph[RNACoExpressionNetwork, Echo@RandomChoice[VertexList@RNACoExpressionNetwork, 30]], VertexStyle -> RGBColor[0.6, 0.68, 0], EdgeStyle -> RGBColor[0, 0, 0.61], "MaxVertexSize" -> 200, VertexLabels -> Placed["Name", Tooltip]]
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Expression patterns of randomly chosen pair of connected genes in the "TissueAtlas Gene co-expression network":

In[20]:=
RNAExpressionData = ResourceData[ ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]]]; RNACoExpressionNetwork = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "TissueAtlas Gene co-expression network"]; ensemblIDGeneAssoc = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "Ensembl ID gene name association"]; ensemblIDs = Keys@RNAExpressionData[[2 ;; -1]]; genes = ensemblIDGeneAssoc /@ ensemblIDs; genePairs = VertexList[Graph@RandomChoice@EdgeList@RNACoExpressionNetwork]; array = (Rescale@RNAExpressionData[#] &) /@ ensemblIDs[[Flatten[Position[genes, #] & /@ genePairs]]]; ListLinePlot[array, PlotLegends -> {genePairs[[1]], genePairs[[2]]}, AxesLabel -> {None, "Rescaled expression (nTPM)" }, PlotRange -> All, PlotStyle -> {Red, Directive[Blue, Dashed]}, ImageSize -> Large]
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Correlation between expression levels:

In[28]:=
Correlation @@ array
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Expression levels for a ITGA5, one of the most frequently occurring genes in unfavorable cancer prognosis, in different cancers:

Code
In[29]:=
plotCancerShortNames = {{"GBM"}, {"THCA"}, {"LUAD", "LUSC"}, {"COAD", "READ"}, {"HNSC"}, {"STAD"}, {"LIHC"}, {"PAAD"}, {"KIRC", "KIRP", "KICH"}, {"BLCA"}, {"PRAD"}, {"TGCT"}, {"BRCA"}, {"CESC"}, {"UCEC"}, {"OV"}, {"SKCM"}}; plotCancerLongNames = {"Glioma", "Thyroid cancer", "Lung cancer", "Colorectal cancer", "Head and neck cancer", "Stomach cancer", "Liver cancer", "Pancreatic cancer", "Renal cancer", "Urothelial cancer", "Prostate cancer", "Testis cancer", "Breast cancer", "Cervical cancer", "Endometrial cancer", "Ovarian cancer", "Melanoma"}; plotCancerColors = { RGBColor[1., 0.800000094944996, 0.3019609068561773, 1.], RGBColor[ 0.454901902606013, 0.37647054361669646`, 0.5568627171197533, 1.], RGBColor[ 0.35816992186625884`, 0.48104578647795015`, 0.35294121107541104`, 1.], RGBColor[ 9.589812169208401*^-7, 0.4705881904500226, 0.7176470465066893, 1.], RGBColor[ 9.589812169208401*^-7, 0.4705881904500226, 0.7176470465066893, 1.], RGBColor[ 9.589812169208401*^-7, 0.4705881904500226, 0.7176470465066893, 1.], RGBColor[ 0.7960782989163758, 0.7686276968328074, 0.8745099497269464, 1.], RGBColor[ 0.5372548827993883, 0.7215686797169252, 0.5294118166131165, 1.], RGBColor[1., 0.509803856871976, 0.3725490654639468, 1.], RGBColor[1., 0.509803856871976, 0.3725490654639468, 1.], RGBColor[ 0.5137255717824479, 0.8196078836750866, 0.941176564576014, 1.], RGBColor[ 0.5137255717824479, 0.8196078836750866, 0.941176564576014, 1.], RGBColor[ 0.9832875177291199, 0.7011909727286483, 0.8148310349772592, 1.], RGBColor[ 0.9832875177291199, 0.7011909727286483, 0.8148310349772592, 1.], RGBColor[ 0.9832875177291199, 0.7011909727286483, 0.8148310349772592, 1.], RGBColor[ 0.9832875177291199, 0.7011909727286483, 0.8148310349772592, 1.], RGBColor[ 0.9946701221664577, 0.575625452943115, 0.3876721337241307, 1.]}; cancers = {"BRCA", "BLCA", "CESC", "COAD", "READ", "GBM", "HNSC", "KIRC", "KIRP", "KICH", "LIHC", "LUAD", "LUSC", "OV", "PAAD", "PRAD", "SKCM", "STAD", "TGCT", "THCA", "UCEC"}; pathologyData = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "PathologyAtlas mRNA expression (FPKM) " <> #] & /@ cancers; dataFull = AssociationThread[cancers, pathologyData];
In[30]:=
plotCancerColorsAssoc = AssociationThread[plotCancerLongNames, plotCancerColors]; plotCancerTickLabelsAssoc = AssociationThread[plotCancerLongNames, plotCancerShortNames]; ensGeneAssoc = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "Ensembl ID gene name association"]; geneEnsAssoc = AssociationThread[Values@ensGeneAssoc, Keys@ensGeneAssoc]; ticks = MapThread[{#1, Rotate[#2, -5*Pi/3]} &, {Range@ Length@plotCancerLongNames, plotCancerLongNames}]; cancerGeneExpressionPlot[gene_] := Module[{ensembl, data, plottingData, xVal, yVals, plotData}, ensembl = geneEnsAssoc@gene; data = (#@ensembl) & /@ dataFull; plottingData = Table[xVal = Position[plotCancerLongNames, cancerNameToPlot][[1, 1]]; yVals = Flatten[data[#] & /@ plotCancerTickLabelsAssoc[cancerNameToPlot]]; plotData = {xVal, #} & /@ yVals; {plotData, yVals}, {cancerNameToPlot, plotCancerLongNames}]; Show[ListPlot[plottingData[[All, 1]], PlotStyle -> Black, PlotMarkers -> "\!\(\* GraphicsBox[CircleBox[{0, 0}], ImageSize->{11., Automatic}]\)", FrameTicks -> {{Automatic, None}, {ticks, None}}, PlotRange -> Full, ImageSize -> 700, Frame -> True, FrameLabel -> {None, "mRNA expression (FPKM)"}, FrameStyle -> Directive[Black, 15]], BoxWhiskerChart[plottingData[[All, 2]], ChartStyle -> plotCancerColors]]] cancerGeneExpressionDistributionChart[gene_] := Module[{ensembl, data, plottingData}, ensembl = geneEnsAssoc@gene; data = (#@ensembl) & /@ dataFull; plottingData = Table[xVal = Position[plotCancerLongNames, cancerNameToPlot][[1, 1]]; Flatten[ data[#] & /@ plotCancerTickLabelsAssoc[cancerNameToPlot]], {cancerNameToPlot, plotCancerLongNames}]; DistributionChart[plottingData, ChartStyle -> plotCancerColors, ChartLegends -> plotCancerLongNames, PlotRange -> Automatic, ImageSize -> 700, FrameLabel -> {None, "mRNA expression (FPKM)"}, FrameStyle -> Directive[Black, 15]]]
Plots
In[31]:=
cancerGeneExpressionPlot["ITGA5"]
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In[32]:=
cancerGeneExpressionDistributionChart["ITGA5"]
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Survival probability for ITGA5 in Renal cancer:

Code
In[33]:=
Plots
In[34]:=
mRNAExpressionPlot["ITGA5", "Renal cancer"]
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In[35]:=
expressionProbabilityPlot["ITGA5", "Renal cancer"]
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In[36]:=
survivalProbabilityPlot["ITGA5", "Renal cancer"]
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Cell cluster plot for different tissues:

In[37]:=
CellClusterPlot[organ_] := Module[{assoc, clusters, L, color, clusterList}, assoc = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "SingleCellAtlas UMAP coordinates in tissue " <> organ]; clusters = Keys@assoc; clusterList = StringJoin["Cluster ", ToString@#] & /@ clusters; L = Length@clusters; color = ColorData["Rainbow"][#] & /@ (Range[L]/L); Legended[ ListPlot[Table[Style[assoc[[i, All, -1]], color[[i]]], {i, L}], Axes -> False, FrameLabel -> {"UMAP_x", "UMAP_y"}, FrameStyle -> Directive[Black, Thickness[0.0025], 15], AspectRatio -> 1, PlotLabel -> Style[Capitalize@organ, Black, 18], Frame -> True, PlotStyle -> PointSize[Small]], SwatchLegend[color, clusterList, LegendMarkers -> "Bubble"]]]
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CellClusterPlot["brain"]
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Cluster plot for all tissues:

In[39]:=
CellClusterPlotBasic[organ_] := Module[{assoc, clusters, L, color, clusterList}, assoc = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "SingleCellAtlas UMAP coordinates in tissue " <> organ]; clusters = Keys@assoc; clusterList = StringJoin["Cluster ", ToString@#] & /@ clusters; L = Length@clusters; color = ColorData["Rainbow"][#] & /@ (Range[L]/L); ListPlot[Table[Style[assoc[[i, All, -1]], color[[i]]], {i, L}], Axes -> False, FrameLabel -> {"UMAP_x", "UMAP_y"}, FrameStyle -> Directive[Black, Thickness[0.0025], 15], AspectRatio -> 1, PlotLabel -> Style[Capitalize@organ, Black, 18], Frame -> True, PlotStyle -> PointSize[Small]]]
In[40]:=
tissueList = {"adipose_tissue", "bone_marrow", "brain", "breast", "bronchus", "colon", "endometrium", "esophagus", "eye", "fallopian_tube", "heart_muscle", "kidney", "liver", "lung", "lymph_node", "ovary", "pancreas", "pbmc", "placenta", "prostate", "rectum", "salivary_gland", "skeletal_muscle", "skin", "small_intestine", "spleen", "stomach", "testis", "thymus", "tongue", "vascular"};
In[41]:=
plots = CellClusterPlotBasic[#] & /@ tissueList;
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GraphicsGrid[Partition[plots, UpTo@5], ImageSize -> 1500]
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Analysis (2) 

Power law dependence of VertexDegree in the human gene co-expression networks showing its scale-free nature:

Tissue Atlas
In[43]:=
RNACoExpressionNetwork = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "TissueAtlas Gene co-expression graph"]; power = 1/2; power2 = 1; Legended[Show[ListLogLogPlot[ResourceFunction[ ResourceObject[<|"Name" -> "VertexDegreeRelativeFrequencies", "ShortName" -> "VertexDegreeRelativeFrequencies", "UUID" -> "a76d6242-2a31-4326-bb6a-df4e446f50a6", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Relative frequencies of VertexDegree in a Graph", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$8f984335e1b840a0b3c49438547b6dc4`VertexDegreeRelativeFrequencies", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/beb446d6-a18b-4eae-b8fa-c7d76afd70c6"]|>, ResourceSystemBase -> Automatic]][ RNACoExpressionNetwork], ImageSize -> 700, PlotStyle -> RGBColor[0.54, 0.53, 0.7000000000000001], Frame -> True, FrameStyle -> Directive[Black, 14], PlotLabel -> Style["Tissue gene co-expression network", Black, 15], FrameLabel -> {{"Probability", None}, {"VertexDegree", Automatic}}], ListLogLogPlot[(0.03/#^power) & /@ (Range@1000), PlotStyle -> RGBColor[1, 0.51, 0]], ListLogLogPlot[(0.2/#^power2) & /@ (Range@2000), PlotStyle -> RGBColor[0.43, 0.74, 0]]], SwatchLegend[{RGBColor[0.54, 0.53, 0.7000000000000001], RGBColor[ 1, 0.51, 0], RGBColor[0.43, 0.74, 0]}, {"data", "~1/x^0.5", "~1/x"}, LegendMarkers -> "Bubble"]]
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Brain Atlas
In[44]:=
brainGeneCoExpressionNetwork = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "BrainAtlas Gene co-expression graph"]; \[Alpha] = 3/4; Legended[Show[ListLogLogPlot[ResourceFunction[ ResourceObject[<|"Name" -> "VertexDegreeRelativeFrequencies", "ShortName" -> "VertexDegreeRelativeFrequencies", "UUID" -> "a76d6242-2a31-4326-bb6a-df4e446f50a6", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Relative frequencies of VertexDegree in a Graph", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$8f984335e1b840a0b3c49438547b6dc4`VertexDegreeRelativeFrequencies", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/beb446d6-a18b-4eae-b8fa-c7d76afd70c6"]|>, ResourceSystemBase -> Automatic]][ brainGeneCoExpressionNetwork], ImageSize -> 700, PlotStyle -> RGBColor[0.54, 0.53, 0.7000000000000001], Frame -> True, FrameStyle -> Directive[Black, 14], FrameLabel -> {{"Probability", None}, {"VertexDegree", Automatic}}, PlotLabel -> Style["Brain gene co-expression network", Black, 15]], ListLogLogPlot[(0.05/#^\[Alpha]) & /@ (Range@2000), PlotStyle -> RGBColor[1, 0.51, 0]]], SwatchLegend[{RGBColor[0.54, 0.53, 0.7000000000000001], RGBColor[ 1, 0.51, 0]}, {"data", "~1/x^0.75"}, LegendMarkers -> "Bubble"]]
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Single cell atlas
In[46]:=
scGeneCoExpressionNetwork = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "SingleCellAtlas Gene co-expression graph"]; \[Alpha] = 3/4; Legended[Show[ListLogLogPlot[ResourceFunction[ ResourceObject[<|"Name" -> "VertexDegreeRelativeFrequencies", "ShortName" -> "VertexDegreeRelativeFrequencies", "UUID" -> "a76d6242-2a31-4326-bb6a-df4e446f50a6", "ResourceType" -> "Function", "Version" -> "1.0.0", "Description" -> "Relative frequencies of VertexDegree in a Graph", "RepositoryLocation" -> URL[ "https://www.wolframcloud.com/obj/resourcesystem/api/1.0"], "SymbolName" -> "FunctionRepository`$8f984335e1b840a0b3c49438547b6dc4`VertexDegreeRelativeFrequencies", "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/beb446d6-a18b-4eae-b8fa-c7d76afd70c6"]|>, ResourceSystemBase -> Automatic]][ scGeneCoExpressionNetwork], ImageSize -> 700, PlotStyle -> RGBColor[0.54, 0.53, 0.7000000000000001], Frame -> True, FrameStyle -> Directive[Black, 14], FrameLabel -> {{"Probability", None}, {"VertexDegree", Automatic}}, PlotLabel -> Style["Single cell atlas gene co-expression network", Black, 15]], ListLogLogPlot[(0.05/#^\[Alpha]) & /@ (Range@2000), PlotStyle -> RGBColor[1, 0.51, 0]]], SwatchLegend[{RGBColor[0.54, 0.53, 0.7000000000000001], RGBColor[ 1, 0.51, 0]}, {"data", "~1/x^0.75"}, LegendMarkers -> "Bubble"]]
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Visualize the values of VertexDegree and BetweennessCentrality for common genes in the three networks:

In[48]:=
commonGenes = Intersection[VertexList@ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "TissueAtlas Gene co-expression graph"], VertexList@ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "BrainAtlas Gene co-expression graph"], VertexList@ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "SingleCellAtlas Gene co-expression graph"]]
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In[49]:=
tissueData = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "TissueAtlas Gene co-expression network Graph measures"]; brainData = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "BrainAtlas Gene co-expression network Graph measures"]; scData = ResourceData[ResourceObject[ CloudObject[ "https://www.wolframcloud.com/obj/souticks/DeployedResources/Data/Human-Protein-Atlas"]], "SingleCellAtlas Gene co-expression network Graph measures"]; maxV = Max@ MapThread[#1* #2* #3 &, {Map[tissueData[#, "VertexDegree"] &, commonGenes], Map[brainData[#, "VertexDegree"] &, commonGenes], Map[scData[#, "VertexDegree"] &, commonGenes]}]; plotVDData = MapThread[ Tooltip[Style[{#1, #2, #3}, ColorData["Rainbow"][Log[#1* #2* #3]/Log@maxV]], #4] &, {Map[ tissueData[#, "VertexDegree"] &, commonGenes], Map[brainData[#, "VertexDegree"] &, commonGenes], Map[scData[#, "VertexDegree"] &, commonGenes], commonGenes}];
In[50]:=
ListPointPlot3D[plotVDData, ScalingFunctions -> {"Log", "Log", "Log"}, PlotStyle -> PointSize[Medium], AxesStyle -> Directive[Black, 15], AxesLabel -> {"Tissue Atlas", "Brain Atlas", "Single Cell Atlas"}, ImageSize -> Large, PlotLabel -> Style["VertexDegree for three\nco-expression networks", Black, 15]]
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In[51]:=
maxV = Max@ MapThread[#1* #2* #3 &, {Map[ tissueData[#, "BetweennessCentrality"] &, commonGenes], Map[brainData[#, "BetweennessCentrality"] &, commonGenes], Map[scData[#, "BetweennessCentrality"] &, commonGenes]}]; plotBCData = MapThread[ Tooltip[Style[{#1, #2, #3}, ColorData["Rainbow"][ Log[If[#1* #2* #3 == 0, 0.000001, #1* #2* #3]]/ Log@maxV]], #4] &, {Map[ tissueData[#, "BetweennessCentrality"] &, commonGenes], Map[brainData[#, "BetweennessCentrality"] &, commonGenes], Map[scData[#, "BetweennessCentrality"] &, commonGenes], commonGenes}]; ListPointPlot3D[plotBCData, ScalingFunctions -> {"Log", "Log", "Log"}, PlotStyle -> PointSize[Medium], AxesStyle -> Directive[Black, 15], AxesLabel -> {"Tissue Atlas", "Brain Atlas", "Single Cell Atlas"}, ImageSize -> Large, PlotLabel -> Style["BetweennessCentrality for three\nco-expression networks", Black, 15]]
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Keywords

  • protein
  • genes
  • networks
  • RNA
  • Tanscriptomics
  • Proteomics
  • tissue
  • pathology
  • disease
  • cancer

Source Metadata

  • Citation: {Uhlén M et al., Tissue-based map of the human proteome. Science (2015) PubMed: 25613900 DOI: 10.1126/science.1260419, Thul PJ et al., A subcellular map of the human proteome. Science. (2017) PubMed: 28495876 DOI: 10.1126/science.aal3321 , Sjöstedt E et al., An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. (2020) PubMed: 32139519 DOI: 10.1126/science.aay5947, Karlsson M et al., A single-cell type transcriptomics map of human tissues. Sci Adv. (2021) PubMed: 34321199 DOI: 10.1126/sciadv.abh2169, Uhlen M et al., A pathology atlas of the human cancer transcriptome. Science. (2017) PubMed: 28818916 DOI: 10.1126/science.aan2507 , Uhlen M et al., A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science. (2019) PubMed: 31857451 DOI: 10.1126/science.aax9198, Sjöstedt E et al., An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. (2020) PubMed: 32139519 DOI: 10.1126/science.aay5947 , Uhlén M et al., The human secretome. Sci Signal. (2019) PubMed: 31772123 DOI: 10.1126/scisignal.aaz0274, Becht, Etienne, et al. "Dimensionality reduction for visualizing single-cell data using UMAP." Nature biotechnology 37.1 (2019): 38-44.}

See Also

  • Alpha Missense

Publisher Information

  • Contributed By: Soutick Saha