ClusterMap：用于空间基因表达的多尺度聚类分析 空间转录组分析工具推荐
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参考技术A 在空间背景下量化RNA是了解复杂组织中基因表达和调控的关键。原位转录组方法可以在完整的组织中产生空间分辨率的RNA图谱。然而，目前还缺乏一个统一的计算工具来综合分析原位转录组数据。2021年10月， Nature Communications 发表了 一个无监督和无注释的计算工具：ClusterMap，其在二维和三维空间将RNA精确地聚类到亚细胞结构、细胞体和组织区域中，并在不同的组织类型（包括小鼠大脑、胎盘、肠道和人类心脏器官）中表现稳定。ClusterMap广泛适用于各种原位转录组技术，从高维转录组图谱图像中揭示基因表达模式、细胞生态位和组织结构原理。为了绘制细胞和组织中基因表达的空间异质性，已经开发了大量基于图像的原位转录组学方法（例如STARmap、Fiseq、ISS、MERFISH、seqFISH、osmFISH等），提供了完整组织中亚细胞RNA定位的图谱。然而， 从高维空间转录组数据中直接提取生物模式的低维表示仍然具有挑战性。
ClusterMap是基于两个关键的生物学现象。首先，细胞内RNA分子的密度高于细胞外；其次，不同基因编码的细胞RNA在不同的亚细胞位置、细胞类型和组织区域富集。因此，开发团队推断， 通过对RNA的物理密度和基因身份进行联合聚类，可以直接从原位转录组数据中确定有生物学意义的模式和结构。随后，根据基因身份和空间尺度对空间聚类进行解析，以表示亚细胞定位、细胞分割和区域识别。
ClusterMap从基于原位转录组数据的预处理成像开始，原始荧光图像被转换成具有物理3D位置和基因身份的离散RNA点。开发团队引入了一个高维向量，称为邻域基因组成（NGC），它是通过考虑每个RNA点上圆形窗口的基因表达谱来计算的。每个RNA点的NGC坐标和物理坐标通过计算整合成每个点上的联合物理和NGC（P-NGC）坐标。接下来的目标是在P-NGC坐标系中对RNA进行聚类，以便进行下游分割。开发团队选择了密度峰值聚类算法(DPC)来计算两个变量：P-NGC联合空间中每个点的局部密度ρ和距离δ。然后将这两个变量的乘积γ按照递减的顺序进行排序，以找到真正的具有高数量级γ值的集群。
与此前的方法相比，ClusterMap在模拟数据集和生物数据集中均表现出稳定的高性能。此外，ClusterMap广泛适用于各种实验方法，包括但不限于STARmap、MERFISH、ISS和osmFISH。结果表明ClusterMap从不同组织样本的原位转录组数据中准确地创建了RNA注释的亚细胞和细胞图谱，这些组织样本具有不同的RNA定位、细胞密度、形态和连接。
开发团队在五个模拟聚类基准数据集和一个由STARmap收集的代表性原位转录组数据中测试了ClusterMap的性能。 与以前的方法相比，即使在斑点分布包含不规则的边界、不同的物理密度以及不同形状和大小的情况下ClusterMap在所有六个数据集中均表现出稳定的性能。
ClusterMap在小鼠初级皮质（V1）生成细胞类型和组织区域图： 即使是密集的小鼠V1皮质细胞，细胞分割也清晰可见；与人工注释的分割标签相比，ClusterMap细胞标签的准确率达到80~90%；在小鼠V1皮层数据集中，ClusterMap确定了与之前报告中表达特征和组织定位都相匹配的细胞类型；ClusterMap准确地检测到皮质分层，从而可以对每个皮质层的细胞类型组成进行量化。 综上，ClusterMap可以有效、准确、自动地进行细胞分割、细胞分型和组织区域识别。
ClusterMap在小鼠胎盘中生成细胞类型和组织区域图： 鉴定了多达7224个细胞，通过使用Louvain算法将其聚类为12种细胞类型，其标记基因与scRNA-seq定义的细胞类型一致；ClusterMap根据细胞类型图确定了五个组织区域。
ClusterMap揭示胎盘中的细胞生态位和细胞-细胞邻接图。
开发团队分析了来自四种具有代表性的原位转录组学方法的小鼠大脑数据，验证了ClusterMap对不同实验方法的普遍适用性。
ClusterMap支持3D原位转录组学分析： 在3D心脏类器官样本中，分层聚类将细胞分为三类，其具有不同的分子特征；100μm厚的小鼠V1样本包括所有六个皮质层和胼胝体，其中鉴定出多达24000个细胞，3D聚集成11种细胞类型，其结果显示了与先前发表的结果相似的空间分布。
ClusterMap将显著地扩展科研人员对从亚细胞器到器官的细胞类型图谱的所有尺度细胞组织的知识，并使单个细胞的局部微环境的进一步表征成为可能。
除了空间转录组数据外，ClusterMap还可以被推广应用于其他二维和三维映射的高维离散信号（如蛋白质或活细胞成像数据）。在未来，预计ClusterMap还可以通过结合其他类型的生物特征（如亚细胞器、细胞形状等）进行扩展，以揭示基因表达如何塑造细胞结构和组织形态的基本原理。
ClusterMap工具将在如下链接更新和维护：
https://github.com/wanglab-broad/ClusterMap
https:// github.com/LiuLab-Bioelectronics-Harvard/ClusterMap
首发公号：国家基因库大数据平台
参考文献
He, Y., Tang, X., Huang, J. et al. ClusterMap for multi-scale clustering analysis of spatial gene expression.Nat Commun 12, 5909 (2021). https://doi.org/10.1038/s41467-021-26044-x
实战--酵母基因表达数据的聚类分析
背景：酵母会在一定的时期发生diauxic shift，有一些基因的表达上升，有一些基因表达被抑制，通过聚类算法，将基因表达的变化模式聚成6类。
ORF Name R1.Ratio R2.Ratio R3.Ratio R4.Ratio R5.Ratio R6.Ratio R7.Ratio 1 YDR025W RPS18A 0.136061549576028 -0.111031312388744 -0.189033824390017 -0.782408564927373 -0.75702324650746 -0.855989697308481 -2.30451104180995 2 YDR031w -0.286881147788162 -0.0840642647884746 0.184424571137428 0.136061549576028 0.535331732996556 2.32192809488736 1.25153876699596 3 YDR060w -0.0426443374084937 -0.0976107966264223 -0.0143552929770701 -0.799087306074004 -0.839959587489532 -2.24792751344359 -2.38681094647222 4 YDR064W YS15 -0.0565835283663676 -0.124328135002202 -0.070389327891398 -0.545968369105293 -0.55581615506164 -2.10433665981474 -2.36737106564853 5 YDR070c 0.0144995696951152 0.0740005814437768 0.0588936890535686 0.0144995696951152 0.251538766995964 2.39592867633114 1.68965987938785 6 YDR144C MKC7 0.136061549576028 -0.0426443374084937 -0.298658315564515 -0.871843648509318 -1.14404636961671 -2.42223300068305 -2.72246602447109 7 YDR171W HSP42 0.0740005814437768 0.136061549576028 0.434402824145775 0.836501267717121 0.943416471633632 3.64385618977472 3.32192809488736 8 YDR178W SDH4 -0.189033824390017 -0.028569152196771 0.599462070416271 0.888968687611256 0.836501267717121 2.55639334852439 2.64385618977472 9 YDR258C HSP78 -0.250961573533219 0.136061549576028 -0.22650852980868 0.862496476250065 0.62148837674627 2.55639334852439 2.39592867633114 10 YBL015W ACH1 -0.111031312388744 0.0588936890535686 0.0144995696951152 0.104697378666693 0.3040061868901 2.73696559416621 3.32192809488736 11 YDR272W GLO2 -0.028569152196771 -0.0976107966264223 0.358453970912476 0.434402824145775 0.377069649079823 2.55639334852439 1.25153876699596 12 YDR272W 0.0740005814437768 0.3040061868901 0.494109070270043 0.473931188332412 0.377069649079823 2.32192809488736 1.47393118833241 13 YDR342C HXT7 0.184424571137428 0.434402824145775 0.943416471633632 1.51457317282976 1.64385618977472 2.32192809488736 1.59946207041627 14 YDR343C HXT6 0.15200309344505 0.761213140412884 1.28630418515664 1.94341647163363 1.51457317282976 2.18442457113743 2.64385618977472 15 YBL027W RPL19A 0.0439433475875972 0.0144995696951152 0.395928676331139 -0.16349873228288 -0.27500704749987 -1.11103131238874 -2.44360665147561 16 YDR382W RPLA4 0.104697378666693 0.15200309344505 0.104697378666693 0.0588936890535686 0.0439433475875972 -0.604071323668861 -2.36457243229586 17 YDR398W -0.0143552929770701 -0.0840642647884746 -0.124328135002202 -1.03562390973072 -1.1243281350022 -2.02147972741045 -2.799087306074 18 YDR417C 0.321928094887362 0.120294233717712 0.0892673380970873 -0.344828496997441 -0.250961573533219 -1.64616265715789 -2.54596836910529 19 YDR418W RPL15A 0.200912693925996 0.168122758808327 0.286304185156641 -0.20163386116965 -0.20163386116965 -1.75274859140713 -2.99638874644762 20 YDR516C -0.137503523749935 0.2688167584278 0.415037499278844 0.785875194647153 0.713118852211838 2.32192809488736 1.02914634565952 21 YBL043W -0.516015147003665 0.0740005814437768 0 -0.214124805352847 -0.53605290024021 1.94341647163363 2.32192809488736 22 YDR529C QCR7 0.168122758808327 0.0291463456595165 0.377069649079823 0.862496476250065 0.810966175609983 2.18442457113743 2.55639334852439 23 YDR533C -0.070389327891398 0.0588936890535686 0.577766999316952 0.415037499278844 0.0144995696951152 2.73696559416621 2.39592867633114 24 YBL045C COR1 0.0740005814437768 0 0.68965987938785 0.514573172829758 0.15200309344505 1.68965987938785 2.47393118833241 25 YEL011w GLC3 -0.286881147788162 0.120294233717712 0.494109070270043 0.836501267717121 0.494109070270043 2.64385618977472 2.83650126771712 26 YEL012w UBC8 -0.0840642647884746 -0.298658315564515 0.0740005814437768 0.0740005814437768 -0.356143810225275 1.32192809488736 2.32192809488736 27 YEL024w RIP1 0.0892673380970873 0.2688167584278 0.556393348524385 0.888968687611256 0.943416471633632 1.94341647163363 2.32192809488736 28 YBL048W -0.250961573533219 0.104697378666693 -0.150559676575381 -0.22650852980868 -0.356143810225275 2.55639334852439 2.18442457113743 29 YBL049W -0.584962500721156 0.168122758808327 0.184424571137428 -0.150559676575381 -0.823749360308273 2.47393118833241 2.64385618977472 30 YEL054c RPL15A 0.200912693925996 0.0892673380970873 0.321928094887362 -0.321928094887362 -0.475084882948783 -1.64154602908752 -2.46466826700344 31 YER024w -0.27500704749987 -0.150559676575381 0.168122758808327 -0.356143810225275 -0.495695162624069 1.12029423371771 3.18442457113743 32 YER053c -0.189033824390017 0.358453970912476 1.02914634565952 1.51457317282976 1.64385618977472 2.18442457113743 2.64385618977472 33 YER065c ICL1 0.120294233717712 0.168122758808327 0.535331732996556 0.0892673380970873 -0.150559676575381 0.535331732996556 3.64385618977472 34 YER067w -0.070389327891398 0.15200309344505 0.761213140412884 1.02914634565952 0.377069649079823 3.18442457113743 2.55639334852439 35 YBL064C 0.377069649079823 0.0439433475875972 0.736965594166206 0.888968687611256 0.761213140412884 3.47393118833241 2.83650126771712 36 YER131w RPS26B 0.358453970912476 0.136061549576028 0.473931188332412 0.0439433475875972 -0.0143552929770701 -1.36737106564853 -2.48800077083407 37 YER150w -0.0143552929770701 0.184424571137428 1.15200309344505 1.51457317282976 1.59946207041627 3.47393118833241 3.32192809488736 38 YAL003W TEF5 0.234465253637023 0 -0.0426443374084937 -0.641546029087524 -0.933572638261024 -1.53605290024021 -2.33342373372519 39 YFL014W HSP12 -0.150559676575381 0.377069649079823 0.62148837674627 1.59946207041627 2.25153876699596 3.64385618977472 3.47393118833241 40 YFL054C -0.678071905112638 -0.36737106564853 -0.137503523749935 -0.27500704749987 -0.176322772640463 2.47393118833241 0.971430847803229 41 YBL078C -0.400537929583729 -0.16349873228288 0.136061549576028 0.234465253637023 0.321928094887362 2.32192809488736 2.12029423371771 42 YFR015C GSY1 -0.22650852980868 0.0439433475875972 0.415037499278844 1.55639334852439 1.02914634565952 2.73696559416621 2.73696559416621 43 YFR033C QCR6 0.0588936890535686 -0.111031312388744 0.0291463456595165 0.321928094887362 0.62148837674627 2.39592867633114 1.88896868761126 44 YFR053C HXK1 0.184424571137428 0.321928094887362 0.395928676331139 1 1.47393118833241 2.55639334852439 0.377069649079823 45 YGL031C RPL30A 0.0291463456595165 0 0.0439433475875972 -0.137503523749935 0 -0.669026765509631 -2.31324585178756 46 YGL037C 0.200912693925996 0.377069649079823 0.286304185156641 1.15200309344505 1.47393118833241 2.83650126771712 1.83650126771712 47 YGL076C RPL6A 0.0740005814437768 -0.411426245726465 -0.028569152196771 -0.790772037862 -0.839959587489532 -1.6959938131099 -2.78868571061353 48 YGL078C -0.0143552929770701 -0.250961573533219 0.321928094887362 -0.831877241191673 -0.831877241191673 -1.46988597627446 -2.4594316186373 49 YGL102C 0.0291463456595165 -0.0976107966264223 0.168122758808327 0 -0.028569152196771 -1.39506279951758 -2.55090066464752 50 YBL100C 0.136061549576028 0.217591435072627 0.415037499278844 0.577766999316952 0.888968687611256 1.28630418515664 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YGR103W 0.15200309344505 -0.0565835283663676 0.321928094887362 -0.903038270112912 -1.10433665981474 -1.64154602908752 -2.56315813040281 59 YGR148C RPL30B 0.200912693925996 0.104697378666693 0.0588936890535686 -0.286881147788162 -0.27500704749987 -1.81966818349646 -2.61588707391606 60 YGR160W 0.321928094887362 0.104697378666693 -0.250961573533219 -1.48542682717024 -1.48026512205446 -2.50842865253186 -3.21412480535285 61 YGR214W NAB1A 0.2688167584278 0.15200309344505 0.454031630894707 -0.36737106564853 -0.286881147788162 -1.66902676550963 -2.45154083301783 62 YGR236C -0.124328135002202 -0.189033824390017 -0.298658315564515 -0.495695162624069 -0.641546029087524 3.47393118833241 2.83650126771712 63 YGR243W -0.344828496997441 -0.27500704749987 -0.37851162325373 -0.20163386116965 -0.411426245726465 3.18442457113743 2.47393118833241 64 YGR244C 0.0439433475875972 0.2688167584278 0.761213140412884 0.943416471633632 1.47393118833241 2.39592867633114 2.05889368905357 65 YGR248W -0.298658315564515 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算法1 Lloyd Algorithm k_mean clustering * Centers to Clusters: After centers have been selected, assign each data point to the cluster corresponding to its nearest center; ties are broken arbitrarily. * Clusters to Centers: After data points have been assigned to clusters, assign each cluster’s center of gravity to be the cluster’s new center.
程序利用Lloyd算法，随机选取起始的Centers，并进行2000次迭代，选取最佳结果。
python源码
1 from os.path import dirname 2 import numpy as np 3 import math 4 import random 5 import matplotlib.pyplot as plt 6 7 def square_EuclideanDistance(vId,wId,Centers): 8 s = 0 9 for i in range(m): 10 s += (points[vId][i]-Centers[wId][i])**2 11 return s 12 13 def Centers_to_Clusters(Centers): 14 global Cluster 15 Cluster = [[] for i in range(k)] 16 Distortion = 0 17 for i in range(len(points)): 18 minVal = INF 19 minId = -1 20 for c in range(k): 21 tmp = square_EuclideanDistance(i, c,Centers) 22 if tmp < minVal: 23 minVal = tmp 24 minId = c 25 Cluster[minId].append(i) 26 Distortion += minVal 27 return Distortion/points_num 28 29 def Clusters_to_Centers(): 30 #print(Cluster) 31 Centers = [] 32 for i in range(k): 33 coordinate = [0 for i in range(m)] 34 for pId in Cluster[i]: 35 for j in range(m): 36 coordinate[j] += points[pId][j] 37 if len(Cluster[i])==0: 38 coordinate = [0.5 for item in coordinate] 39 else: 40 coordinate = [item/len(Cluster[i]) for item in coordinate] 41 Centers.append(coordinate) 42 #print(Centers) 43 return Centers 44 45 def LloydClustering(): 46 global Cluster 47 Centers = random.sample(points,k) 48 minDistortion = INF 49 Distortion = Centers_to_Clusters(Centers) 50 i = 0 51 while(Distortion < minDistortion): 52 i += 1 53 #print(‘*%d iteration*...‘%i) 54 minDistortion = Distortion 55 Centers = Clusters_to_Centers() 56 Distortion = Centers_to_Clusters(Centers) 57 return [Centers,Distortion] 58 59 if __name__ == ‘__main__‘: 60 INF = 999999 61 dataset = open(dirname(__file__)+‘230genes_log_expression.txt‘).read().strip().split(‘ ‘) 62 dataset = [line.split() for line in dataset] 63 name = [item[1] for item in dataset[1:]] 64 k = 6 # 分成 6 类 65 m = 7 # 数据的维度 66 #print(m) 67 # print(m,k) 68 points = [] 69 for line in dataset[1:]: 70 if(len(line)==10): 71 points.append(list(map(float,line[3:]))) 72 elif(len(line)==9): 73 points.append(list(map(float,line[2:]))) 74 75 points_num = len(points) 76 out_Distortion = INF 77 out_Centers = [] 78 out_i = -1 79 for i in range(2000): 80 [Centers,Distortion] = LloydClustering() #Distortion 精确度 81 if Distortion<out_Distortion: 82 out_Distortion = Distortion 83 out_Centers = Centers 84 out_i = i 85 86 print(out_i,out_Distortion) 87 88 for c in out_Centers: 89 for i in range(len(c)): 90 if i != 0: 91 print(‘ ‘,end=‘‘) 92 print(‘%.3f‘%c[i],end=‘‘) 93 print(‘‘) 94 95 x = [i for i in range(1,8)] 96 97 plt.rcParams[‘figure.figsize‘]=(12,8) 98 for i in range(len(points)): 99 plt.plot(x,points[i],color=‘#C0C0C0‘,linewidth=1.5,linestyle=‘-‘,marker=‘.‘) 100 101 for i in range(k): 102 plt.plot(x,Centers[i],linewidth=1.5,linestyle=‘-‘,marker=‘.‘) 103 plt.show()
运行结果：
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