文章阅读目录大纲

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imports "clustering" from "MLkit";

require(graphics2D);

multishapes = read.csv("./multishapes.csv");
[x, y] = list(multishapes[, "x"], multishapes[, "y"]);
print(multishapes, max.print = 13);

# detect object shapes
objects = graphics2D::pointVector(multishapes[, "x"], multishapes[, "y"]) |> dbscan_objects();
objects[objects == "-1"] = "noise";
objects = ifelse(objects == "noise", objects, `object_${objects}`);

# show object detection result
bitmap(file = "./object_detection.png") {
    plot(multishapes[, "x"], multishapes[, "y"], 
        class     = objects, 
        grid.fill = "white",
        padding   = "padding: 125px 300px 200px 200px;",
        colorSet  = "paper"
    );
}
谢桂纲

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  2. 文中提出的"能否基于单细胞数据分析方法来分析和可视化我的基因组功能嵌入的结果矩阵呢?"让我想到一个需要先厘清的问题:把Family层级的分类当作细胞分群标签时,功能相似性与亲缘关系可能会混在一起,图上的聚集未必能直接解释为代谢功能接近。如果借用单细胞分析中的降维流程,是否可以同时标注物种分类和基因组注释完整度,看看分离主要对应哪一种差异?尤其是这里用的是EC注释的TF-IDF矩阵,缺少某项注释究竟代表功能缺失还是未被识别,也可能影响对分群结果的判断。

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