上一期介绍了 Cardelino 的使用，这期介绍RobustClone，我们知道单细胞数据的SNV和CNV检出率都是非常低的，那么怎么有效的获得可使用的突变位点是重中之重，那么该软件包RobustClone利用PCA模式对单细胞数据进行降维，以此来获得更加准确的突变位点达到对单细胞克隆演化的推断。
**动机：**单细胞测序数据为肿瘤内的异质性提供了前所未有的见解。利用单细胞测序可以更好地表征克隆基因型，并重建肿瘤细胞/克隆的系统发育关系。然而，单细胞测序数据往往容易出错，使其计算分析具有挑战性。
**结果：**为了从容易出错的单细胞测序数据推断肿瘤中的克隆进化，开发了一个高效的计算框架，称为 RobustClone。该算法基于扩展稳健主成分分析和低秩矩阵分解方法，恢复了亚克隆的真实基因型，并重建了亚克隆进化树。RobustClone 是一种无模型的方法，可以应用于单细胞单核苷酸变异 (scSNV) 和单细胞拷贝数变异 (scCNV) 数据。它是高效的和可扩展的大规模数据集。我们对模拟数据集进行了一套系统的评估，证明在大规模数据中，RobustClone 在准确性和效率方面都优于最先进的方法。我们进一步在两个 scSNV 和两个 scCNV 数据集上验证了RobustClone 的有效性，证明其能够在不同场景下准确地恢复基因型矩阵并推断出亚克隆进化树。特别是，在 10X Genomics scCNV 大规模乳腺癌数据集上，RobustClone 揭示了亚克隆进化的空间进展模式。
- 关于安装问题
该软件包是用 MATLAB 和 R 语言写的，所以使用之前需要准备安装 MATLAB。
- 关于输入文件
该软件包输入的数据为SNV或CNV数据。对于SNV数据，可以有两种方式：
- 如果是二进制数据，包括三种元素即 0，1，3
-
0 表示非突变位点；
-
1 表示突变位点；
-
3 表示缺失位点。
- 如果是三元数据，包括四种元素即 0，1，2，3
-
0 表示非突变位点；
-
1 表示突变杂合位点；
-
2 表示突变纯合位点；
-
3 表示缺失位点。
该软件包给出来的例子，如下：
SNV1 SNV2 SNV3 SNV4 SNV5 SNV6 SNV7 SNV8 SNV9 SNV10 SNV11 SNV12 SNV13 SNV14 SNV15 SNV16 SNV17 SNV18 SNV19 SNV20 SNV21 SNV22 SNV23 SNV24 SNV25 SNV26 SNV27 SNV28 SNV29 SNV30 SNV31 SNV32 SNV33 SNV34 SNV35 SNV36 SNV37 SNV38 SNV39 SNV40 SNV41 SNV42 SNV43 SNV44 SNV45 SNV46 SNV47 SNV48 SNV49 SNV50 SNV51 SNV52 SNV53 SNV54 SNV55 SNV56 SNV57 SNV58 SNV59 SNV60 SNV61 SNV62 SNV63 SNV64 SNV65 SNV66 SNV67 SNV68 SNV69 SNV70 SNV71 SNV72 SNV73 SNV74 SNV75 SNV76 SNV77 SNV78 SNV79 SNV80 SNV81 SNV82 SNV83 SNV84 SNV85 SNV86 SNV87 SNV88 SNV89 SNV90 SNV91 SNV92 SNV93 SNV94 SNV95 SNV96 SNV97 SNV98 SNV99 SNV100 SNV101 SNV102 SNV103 SNV104 SNV105 SNV106 SNV107 SNV108 SNV109 SNV110 SNV111 SNV112 SNV113 SNV114 SNV115 SNV116 SNV117 SNV118 SNV119 SNV120 SNV121 SNV122 SNV123 SNV124 SNV125 SNV126 SNV127 SNV128 SNV129 SNV130 SNV131 SNV132 SNV133 SNV134 SNV135 SNV136 SNV137 SNV138 SNV139 SNV140 SNV141 SNV142 SNV143 SNV144 SNV145 SNV146 SNV147 SNV148 SNV149 SNV150 SNV151 SNV152 SNV153 SNV154 SNV155 SNV156 SNV157 SNV158 SNV159 SNV160 SNV161 SNV162 SNV163 SNV164 SNV165 SNV166 SNV167 SNV168 SNV169 SNV170 SNV171 SNV172 SNV173 SNV174 SNV175 SNV176 SNV177 SNV178 SNV179 SNV180 SNV181 SNV182 SNV183 SNV184 SNV185 SNV186 SNV187 SNV188 SNV189 SNV190 SNV191 SNV192 SNV193 SNV194 SNV195 SNV196 SNV197 SNV198 SNV199 SNV200 SNV201 SNV202 SNV203 SNV204 SNV205 SNV206 SNV207 SNV208 SNV209 SNV210 SNV211 SNV212 SNV213 SNV214 SNV215 SNV216 SNV217 SNV218 SNV219 SNV220 SNV221 SNV222 SNV223 SNV224 SNV225 SNV226 SNV227 SNV228 SNV229 SNV230 SNV231 SNV232 SNV233 SNV234 SNV235 SNV236 SNV237 SNV238 SNV239 SNV240 SNV241 SNV242 SNV243 SNV244 SNV245 SNV246 SNV247 SNV248 SNV249 SNV250 SNV251 SNV252 SNV253 SNV254 SNV255 SNV256 SNV257 SNV258 SNV259 SNV260 SNV261 SNV262 SNV263 SNV264 SNV265 SNV266 SNV267 SNV268 SNV269 SNV270 SNV271 SNV272 SNV273 SNV274 SNV275 SNV276 SNV277 SNV278 SNV279 SNV280 SNV281 SNV282 SNV283 SNV284 SNV285 SNV286 SNV287 SNV288 SNV289 SNV290 SNV291 SNV292 SNV293 SNV294 SNV295 SNV296 SNV297 SNV298 SNV299 SNV300 SNV301 SNV302 SNV303 SNV304 SNV305 SNV306 SNV307 SNV308 SNV309 SNV310 SNV311 SNV312 SNV313 SNV314 SNV315 SNV316 SNV317 SNV318 SNV319 SNV320 SNV321 SNV322 SNV323 SNV324 SNV325 SNV326 SNV327 SNV328 SNV329 SNV330 SNV331 SNV332 SNV333 SNV334 SNV335 SNV336 SNV337 SNV338 SNV339 SNV340 SNV341 SNV342 SNV343 SNV344 SNV345 SNV346 SNV347 SNV348 SNV349 SNV350 SNV351 SNV352 SNV353 SNV354 SNV355 SNV356 SNV357 SNV358 SNV359 SNV360 SNV361 SNV362 SNV363 SNV364 SNV365 SNV366 SNV367 SNV368 SNV369 SNV370 SNV371 SNV372 SNV373 SNV374 SNV375 SNV376 SNV377 SNV378 SNV379 SNV380 SNV381 SNV382 SNV383 SNV384 SNV385 SNV386 SNV387 SNV388 SNV389 SNV390 SNV391 SNV392 SNV393 SNV394 SNV395 SNV396 SNV397 SNV398 SNV399 SNV400 SNV401 SNV402 SNV403 SNV404 SNV405 SNV406 SNV407 SNV408 SNV409 SNV410 SNV411 SNV412 SNV413 SNV414 SNV415 SNV416 SNV417 SNV418 SNV419 SNV420 SNV421 SNV422 SNV423 SNV424 SNV425 SNV426 SNV427 SNV428 SNV429 SNV430 SNV431 SNV432 SNV433 SNV434 SNV435 SNV436 SNV437 SNV438 SNV439 SNV440 SNV441 SNV442 SNV443 SNV444 SNV445 SNV446 SNV447 SNV448 SNV449 SNV450 SNV451 SNV452 SNV453 SNV454 SNV455 SNV456 SNV457 SNV458 SNV459 SNV460 SNV461 SNV462 SNV463 SNV464 SNV465 SNV466 SNV467 SNV468 SNV469 SNV470 SNV471 SNV472 SNV473 SNV474 SNV475 SNV476 SNV477 SNV478 SNV479 SNV480 SNV481 SNV482 SNV483 SNV484 SNV485 SNV486 SNV487 SNV488 SNV489 SNV490 SNV491 SNV492 SNV493 SNV494 SNV495 SNV496 SNV497 SNV498 SNV499 SNV500
cell1 3 3 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 3 0 3 1 0 1 1 3 0 3 1 0 0 0 0 3 0 3 1 0 0 0 3 3 3 0 1 3 3 3 1 3 3 1 3 1 0 0 1 0 1 1 1 0 0 3 0 3 1 3 0 0 0 0 0 1 1 3 1 0 3 0 3 3 3 0 1 0 1 1 1 3 0 0 0 1 0 0 3 0 3 1 0 0 3 1 3 0 0 1 0 0 1 0 0 0 0 3 0 0 1 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 3 0 0 0 0 1 0 0 1 3 0 3 0 3 1 0 0 0 0 1 0 0 0 1 0 0 1 3 0 0 0 0 0 0 1 3 3 0 0 3 0 0 3 3 0 0 0 0 1 0 3 3 0 3 0 0 0 3 1 0 3 0 0 3 3 0 0 0 1 1 3 1 1 0 3 0 0 3 1 0 0 0 3 1 3 0 1 1 1 0 3 0 3 3 0 1 0 0 0 0 0 3 0 0 1 3 3 0 0 0 0 0 0 3 3 0 1 0 3 0 0 1 0 0 0 0 3 0 0 0 1 1 1 0 0 0 0 0 3 3 0 0 0 0 0 0 3 0 0 0 0 0 3 0 0 0 0 1 1 3 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 3 0 1 0 1 0 0 0 0 0 0 3 0 0 0 0 0 0 3 0 3 0 0 0 0 0 0 3 3 0 0 3 0 0 1 0 0 0 0 0 1 0 0 0 0 3 0 0 1 3 0 3 0 0 0 1 3 0 0 0 0 0 0 0 1 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 3 3 0 0 0 1 1 3 3 1 1 0 0 0 0 3 1 0 0 1 0 0 0 3 0 1 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 1 1 3 0 0 0 3 1 3 3 0 1 0 0 0 0
cell2 0 0 1 0 1 3 3 3 1 0 0 0 1 1 0 1 1 0 0 0 0 3 3 1 0 0 0 3 1 3 1 0 1 1 1 1 3 3 0 0 3 1 1 0 0 1 3 0 3 0 3 0 3 0 0 1 1 0 3 0 0 3 3 1 0 1 3 3 3 0 3 1 3 1 1 0 0 3 1 1 0 1 1 1 1 0 1 1 0 1 3 1 1 0 0 0 1 1 3 0 1 0 0 0 1 0 0 0 0 3 1 0 0 3 0 3 1 3 3 0 0 0 0 3 1 1 0 0 1 0 1 1 3 3 3 3 3 1 0 0 1 0 0 1 0 0 1 0 0 0 3 0 1 1 1 0 0 3 1 0 0 0 1 0 0 3 0 1 1 3 1 3 1 0 0 3 0 0 3 0 0 1 0 3 3 0 1 3 1 0 1 0 1 3 3 0 1 0 3 3 1 1 1 3 0 1 1 0 3 0 3 1 0 1 0 1 1 0 3 0 1 0 0 3 3 1 1 3 0 0 0 3 1 1 0 0 1 0 1 3 0 1 3 1 0 1 0 0 3 0 1 1 0 0 3 1 3 0 0 3 1 1 1 1 0 1 1 0 1 3 0 0 3 1 3 1 0 3 0 0 0 1 0 1 0 1 1 1 1 3 1 0 0 1 0 0 1 1 1 0 0 3 3 3 3 0 1 3 0 0 0 1 3 1 1 3 1 3 1 0 3 0 1 3 3 1 3 0 0 0 1 1 1 3 3 1 0 1 0 3 0 0 0 3 0 3 0 0 1 0 0 0 0 1 0 1 0 1 3 0 0 1 0 1 0 0 3 0 0 3 1 1 0 0 0 0 3 3 1 1 3 3 0 0 1 0 0 3 0 0 0 3 0 1 1 1 0 3 0 3 0 1 0 0 0 3 1 0 1 0 0 3 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 3 1 0 3 0 0 0 0 0 1 1 1 3 1 0 3 3 1 3 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 1 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 1 0 1 1 3 1 1 0 3 0 1 0 0 3 1 3 0 1 3
cell3 1 0 1 0 1 0 0 0 1 3 1 0 1 1 3 1 1 0 0 0 3 3 3 1 3 0 0 3 1 0 3 0 1 1 1 1 1 1 3 1 3 3 3 3 1 1 1 1 1 0 1 0 1 3 1 0 1 0 1 0 3 1 0 1 3 0 3 1 1 3 0 3 1 3 1 0 1 0 1 0 0 1 1 3 1 3 0 1 1 3 1 1 1 3 0 0 0 0 3 3 1 0 0 0 1 0 1 1 0 0 1 1 3 1 1 1 1 1 1 3 3 0 0 1 1 3 3 1 0 1 3 0 0 1 3 0 0 1 0 0 1 1 0 1 3 1 1 3 1 1 3 0 3 3 0 1 0 1 3 1 0 3 1 1 0 1 0 1 1 1 1 1 0 3 1 1 3 1 1 0 3 1 0 0 0 3 1 1 1 3 3 3 1 1 1 0 1 0 0 1 1 1 1 1 1 3 3 1 3 0 1 0 0 1 0 1 0 1 1 0 1 3 0 1 3 0 1 3 0 0 3 0 3 3 3 1 1 1 1 0 0 1 3 1 0 1 0 0 1 0 1 1 0 3 1 3 3 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 3 0 3 0 0 1 0 0 1 1 1 3 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 1 1 0 1 3 0 1 1 3 1 0 1 1 0 3 1 0 3 1 3 1 1 3 0 1 1 1 1 0 1 3 1 0 0 3 1 0 1 0 1 3 0 1 3 1 1 1 0 1 0 1 0 1 3 3 1 0 1 1 3 0 1 0 3 0 0 3 3 0 1 3 0 0 0 3 0 3 1 1 1 1 0 1 0 1 3 0 1 1 1 0 3 1 3 0 1 0 0 0 3 0 1 0 1 1 1 1 1 1 0 0 1 3 0 0 0 1 1 1 0 1 3 0 3 1 0 1 0 0 3 1 1 1 3 0 3 0 0 1 3 1 1 3 1 1 0 1 3 0 1 1 3 0 1 1 1 1 3 0 0 3 1 0 1 0 1 1 3 1 1 0 0 1 0 0 3 0 0 0 1 3 3 1 3 1 1 1 1 0 3 1 1 0 3 3 1 1 1 1 1
cell4 0 0 3 0 1 0 1 1 3 0 1 0 0 1 0 1 1 0 3 1 3 1 1 1 3 0 0 0 1 1 3 3 3 0 3 1 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 1 1 1 1 0 3 1 1 3 1 3 3 3 1 1 1 3 0 0 0 1 3 1 1 1 1 3 3 1 0 3 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 3 0 3 0 1 0 1 3 1 1 1 3 1 1 1 0 0 0 1 1 1 3 3 3 0 0 1 0 0 1 1 0 0 3 1 1 1 3 1 1 3 1 0 0 1 3 1 1 1 1 3 0 1 1 1 0 0 3 1 1 1 1 0 3 1 1 0 1 0 3 0 1 1 0 0 0 0 3 1 3 0 0 1 1 3 3 3 0 3 1 3 0 0 1 0 1 1 1 1 1 1 0 1 3 1 0 0 0 1 1 3 1 3 3 1 1 1 0 1 0 1 0 3 1 1 1 1 0 1 3 3 3 1 0 1 0 1 1 0 1 1 0 3 0 0 1 1 1 0 0 1 1 0 3 0 0 1 0 1 1 0 3 1 3 1 1 3 1 0 1 3 1 0 0 0 1 1 1 1 3 3 1 1 1 1 3 1 0 1 1 3 0 0 0 0 3 1 3 1 1 0 0 1 0 1 0 3 3 1 0 1 0 0 0 3 3 0 0 1 1 1 1 3 0 0 0 1 3 0 0 1 1 0 3 0 0 0 3 3 0 0 1 0 1 3 0 3 0 0 1 0 0 0 3 0 3 1 1 1 1 0 0 1 1 3 1 3 0 0 0 1 0 3 1 1 1 1 3 0 0 1 3 0 1 3 3 0 1 3 3 0 1 1 1 1 0 1 0 3 0 3 0 0 1 1 0 3 1 1 0 1 1 0 3 0 1 1 0 1 1 3 1 1 0 0 0 1 0 0 0 3 1 1 0 1 3 1 0 1 0 1 3 1 1 0 1 3 0 1 1 0 1 1 0 1 1 0 3 1 3 1 1 0 1 1 0 3 1 0 1 0 0 0 0 1 0 3 1 1 0 1 3 1 1 1 1 0 3 0 1 0 1 0 0 1 1
cell5 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 3 0 3 1 0 0 0 1 3 1 3 3 3 1 0 1 3 3 3 3 0 1 3 1 1 1 1 1 1 0 3 3 0 1 0 1 1 3 0 1 1 1 0 0 0 1 1 0 3 1 1 1 0 1 1 1 1 1 1 1 3 1 0 3 3 3 1 1 3 1 1 0 3 1 3 1 1 1 1 3 1 3 1 1 0 3 0 1 1 1 1 1 0 1 1 0 1 3 1 3 3 1 3 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 3 1 3 1 1 0 0 1 3 1 1 0 1 1 3 0 1 0 3 0 1 1 1 1 1 0 1 1 1 3 0 0 1 3 1 1 1 0 0 1 1 3 1 1 1 1 1 1 1 3 0 1 0 1 1 1 3 1 1 0 3 1 1 1 1 1 1 0 1 1 1 1 0 3 3 1 1 0 1 1 3 1 0 1 0 3 1 1 1 1 3 1 1 0 1 0 1 3 3 0 1 3 1 1 0 1 1 1 1 1 3 1 1 1 3 1 3 1 1 1 1 3 1 3 3 3 0 1 1 0 0 1 3 1 3 3 1 1 1 1 1 1 1 3 0 3 1 1 1 3 1 1 1 1 3 1 1 3 3 1 1 1 0 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 3 0 0 1 0 3 1 1 0 1 3 1 1 3 1 0 1 1 1 3 0 3 1 3 1 0 3 3 1 1 1 0 0 0 3 0 1 3 1 1 3 3 0 0 0 3 1 3 3 3 1 1 0 1 1 1 3 0 0 0 3 0 1 1 1 1 1 1 0 1 3 1 1 0 1 0 0 3 1 1 0 1 0 0 3 1 0 1 3 1 1 1 1 3 1 3 3 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 3 3 0 1 1 3 0 1 1 1 0 3 3 1 1 0 0 1 0 3 3 0 1 1 1 1 1 3 1 3 0 1 0 3 1 3 0 1 3 1 3 1 0 1 1 3 1 0
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而CNV格式较为简单，即为整数，如下：
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- 关于运行问题
该软件包运行主要分两个步骤：
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运行 MATLAB脚本文件，名为"carryout_RPCA.m" 生成基因型矩阵；
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运行R 语言脚本文件，名为 “carryout_clonal_tree.R” 对细胞进行聚类并且重构克隆进化树。
其中carryout_clonal_tree.R运行需要改一下路径以及一些命名问题，如下：
setwd('./RobustClone/example')
source('./RobustClone/matlab_and_R_scripts/Clustering_EvolutionaryTree_function.R')
## example for SNV data
AA1_example <- R.matlab:::readMat('example_RobustClone.mat')
AA <- AA1_example[[1]] # Read GTM recovered by RPCA model or extended RPCA model
robust_clone <- LJClustering(AA) # Louvain-Jaccard clustering
clone_gety <- subclone_GTM(AA, robust_clone, 'SNV') # obtain subclonal GTM
MST <- plot_MST(clone_gety, robust_clone, 'SNV', 'exampledata') # calculate and plot clonal MST
clones_mt <- new_variant_site(clone_gety, MST, 'SNV') # obtain the variant SNV loci each subclone compared with its parent subclone
## example for CNV data
AA1_example <- R.matlab:::readMat('SA501X3F_RobustClone.mat')
AA <- AA1_example[[1]] # Read copy number profiles recovered by RPCA model or extended RPCA model
robust_clone <- LJClustering(AA) # Louvain-Jaccard clustering
clone_gety <- subclone_GTM(AA, robust_clone, 'CNV') # obtain subclonal copy number profiles
MST <- plot_MST(clone_gety, robust_clone, 'CNV', 'exampledata') # calculate and plot clonal MST
clones_vt <- new_variant_site(clone_gety, MST, 'CNV', 'parent') # obtain the CNV genome fragments of each subclone compared with its parent subclone or normal cell
data_info <- read.csv('SA501X3F.integer_copy_number.csv',header = TRUE) # the chromosome and copy number profiles information of SA501X3F data
chr <- data_info$chr # the chromosomes in which each genome fragment is located
clones_change_chr <- clonal_CNV_chr(clones_vt, chr) # obtain the variant chromosomes in which all genome fragments with CNV in clones_vt variable are located
clones_vt_state <- new_CNV_chr_state(clone_gety, MST, chr, clones_change_chr, 'parent') # get the statistical frequency of how many copy number of each genome segment have changed for each chromosome and chromosome states when compared to parent subclone or normal cell or the root/first subclone of evolutionary tree
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关于结果解读
该软件包给出例子结果，如下：
文章中使用真实数据进行测试，数据来源于高级别浆液性卵巢癌数据集 HGSOC，该数据包括 420 细胞 和 43 SNV 其中10%多缺失。
利用 RobustClone 去噪前后对比的热图，如下：
利用 RobustClone 重构的克隆进化树，同时重构的进化树中包含的 HGSOC 数据的重叠细胞数，以及由 SCG 识别的克隆中包含的细胞数，如下：
RobustClone 在算法上同其他四款软件进行比较，包括 SCG，BEAM，SCITE 和 SiFit， 后期如果时间允许我们也将在公众号进行解读，敬请关注！
Reference：
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Ziwei Chen, Fuzhou Gong, Lin Wan, Liang Ma, RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data, Bioinformatics, Volume 36, Issue 11, June 2020, Pages 3299–3306.
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McPherson A, Roth A, Laks E, et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat Genet. 2016;48(7):758-767.