diff --git "a/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220.ipynb" "b/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220.ipynb" new file mode 100644 index 0000000..a4089b3 --- /dev/null +++ "b/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220/\345\215\225\347\273\206\350\203\236RNA-Seq\345\210\206\346\236\220.ipynb" @@ -0,0 +1,697 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7371b57a", + "metadata": {}, + "source": [ + "### `Seurat` 专用于单细胞RNA-seq数据的QC、分析和探索研究。\n", + "- Seurat 使用各种计算技术(包括降维、聚类和可视化)来分析 scRNA-seq 数据。\n", + "- 它采用主成分分析 (PCA) 和 t 分布随机邻域嵌入 (t-SNE) 算法将高维基因表达数据减少到低维空间。\n", + "- 通过这样做,它揭示了单个细胞之间的潜在模式和关系,使研究人员能够识别不同的细胞类型、状态或种群\n", + "### 数据下载和软件安装\n", + "- [X] 2700个外周血单个核细胞(PMBC)的转录组数据,测序平台是Illumina NextSeq 500测序仪\n", + "\n", + "- [X] 数据下载 https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz\n", + "\n", + "- [X] 安装Seurat软件\n", + "```\n", + "install.packages('Seurat')\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0387240", + "metadata": {}, + "outputs": [], + "source": [ + "library(dplyr)\n", + "library(Seurat)\n", + "library(patchwork)" + ] + }, + { + "cell_type": "markdown", + "id": "e287490b", + "metadata": {}, + "source": [ + "### 1 创建Seurat对象" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "87b114db", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"Feature names cannot have underscores ('_'), replacing with dashes ('-')\"\n" + ] + }, + { + "data": { + "text/plain": [ + "An object of class Seurat \n", + "13714 features across 2700 samples within 1 assay \n", + "Active assay: RNA (13714 features, 0 variable features)\n", + " 1 layer present: counts" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 加载数据,Read10X函数从指定目录中读取数据集,会返回一个UMI计数矩阵\n", + "pbmc.data <- Read10X(data.dir = \"./filtered_gene_bc_matrices/hg19/\")\n", + "\n", + "# 初始化数据, CreateSeuratObject函数创建一个Seurat对象\n", + "pbmc <- CreateSeuratObject(counts = pbmc.data, project = \"pbmc3k\", \n", + " min.cells = 3, min.features = 200)\n", + "pbmc" + ] + }, + { + "cell_type": "markdown", + "id": "7526eb2c", + "metadata": {}, + "source": [ + "### 2 过滤\n", + "- 根据nFeature_RNA和MT基因占比过滤一些细胞" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e3a28c2a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"Default search for \"data\" layer in \"RNA\" assay yielded no results; utilizing \"counts\" layer instead.\"\n" + ] + }, + { + "data": { + "image/png": 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5KfN0eaNWnCDCsWUdFrGyWMhXiKG1LhI50vyiPliWx0CD1oHp5ad7wql0/PJQSJ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\nibQjkP4Opnvj/9kczN/v6fnTnMd2g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yqwFItILyHK7iX698ifvqR/RDki1Whk/thenrixZESLcYqkLVoZouNG3lsbk3nm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\nDkjM04YHY8EP4BXcj+u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tR5Mi/ou4Z0\ngwx/zqPAkQcgABAJgABAJAACAJESQ0gcXRvgC/5UiQGRzgn+VAAEACIBEACIBEAAIBIAAYBI\nAAQAIgEQAIgEQAAgEgABgEgABAAiARAAiARAACASAAGASAAEACIBEACIBEAAIBIAAYBIAAQA\nIgEQAIgEQAAgEgAB+A8StzvZ3qraJgAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# 计算每个细胞中线粒体(MT)基因的百分比\n", + "pbmc[[\"percent.mt\"]] <- PercentageFeatureSet(pbmc, pattern = \"^MT-\")\n", + "\n", + "# 小提琴图展示每个细胞的RNA特征数\n", + "VlnPlot(pbmc, features = c(\"nFeature_RNA\", \"nCount_RNA\", \"percent.mt\"), \n", + " ncol = 2)\n", + "\n", + "# 根据特征绘制散点图\n", + "plot1 <- FeatureScatter(pbmc, feature1 = \"nCount_RNA\", feature2 = \"percent.mt\")\n", + "plot2 <- FeatureScatter(pbmc, feature1 = \"nCount_RNA\", feature2 = \"nFeature_RNA\")\n", + "plot1 / plot2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d4320d64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "An object of class Seurat \n", + "13714 features across 2638 samples within 1 assay \n", + "Active assay: RNA (13714 features, 0 variable features)\n", + " 1 layer present: counts" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 根据上述小提琴图和散点图的分布情况,\n", + "# 选择nFeature_RNA大于200并且小于2500以及MT基因占比小于5的细胞\n", + "pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)\n", + "\n", + "pbmc" + ] + }, + { + "cell_type": "markdown", + "id": "e0c8f425", + "metadata": {}, + "source": [ + "### 3 特征选择\n", + "- 计算数据集中表现出细胞间高变异的特征(在某些细胞中高表达,而在其他细胞中低表达)\n", + "- 使用`FindVariableFeatures`函数选择在单个细胞之间具有高度变异性的特征(基因),并选择了前2000个作为高变异性的特征\n", + "- 使用`VariableFeaturePlot`函数绘制变异特征的图形,并用`LabelPoints`函数标记top10的特征" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "38746370", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'ggplot2' was built under R version 4.2.3\"\n", + "Finding variable features for layer counts\n", + "\n", + "When using repel, set xnudge and ynudge to 0 for optimal results\n", + "\n", + "Warning message:\n", + "\"\u001b[1m\u001b[22mTransformation introduced infinite values in continuous x-axis\"\n", + "Warning message:\n", + "\"\u001b[1m\u001b[22mTransformation introduced infinite values in continuous x-axis\"\n" + ] + }, + { + "data": { + "image/png": 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fxOqXWpa8myPPycyfBITrAnL0hYRvHN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\n6qFbW77ra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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Vst(方差稳定化变换)方法确定具有高度变异性的特征\n", + "pbmc <- FindVariableFeatures(pbmc, selection.method = \"vst\", nfeatures = 2000)\n", + "# 确定10个变异最大的基因\n", + "top10 <- head(VariableFeatures(pbmc), 10)\n", + "\n", + "# 绘制带标签和不带标签的变量特征图\n", + "plot1 <- VariableFeaturePlot(pbmc) + theme_bw()\n", + "plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) + theme_bw()\n", + "plot1 / plot2" + ] + }, + { + "cell_type": "markdown", + "id": "52736291", + "metadata": {}, + "source": [ + "### 4 数据归一化和线性变换\n", + "- 数据归一化\n", + " - 从数据集中过滤不需要的细胞后,对数据进行归一化处理\n", + " - 默认采用全局尺度归一化方法`LogNormalize`,通过总表达式对每个细胞的特征表达式测量值进行归一化,将其乘比例因子(默认为10,000),并对结果进行对数转换\n", + "- 线性变换\n", + " - 之后,对数据进行线性变换,作为`PCA`等降维处理之前的标准预处理步骤\n", + " - `ScaleData`函数对每个基因的表达值进行平移,使所有细胞的平均表达值为0,并使所有细胞的表达值的方差为1,这一步骤使得在后续的分析中各个基因的权重相等,避免高表达基因对结果的主导影响" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a07317e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Normalizing layer: counts\n", + "\n", + "Centering and scaling data matrix\n", + "\n" + ] + } + ], + "source": [ + "# 数据归一化\n", + "pbmc <- NormalizeData(pbmc, normalization.method = \"LogNormalize\", \n", + " scale.factor = 10000)\n", + "\n", + "# 线性变换\n", + "all.genes <- rownames(pbmc)\n", + "pbmc <- ScaleData(pbmc, features = all.genes)" + ] + }, + { + "cell_type": "markdown", + "id": "c9596ff7", + "metadata": {}, + "source": [ + "### 5 线性降维\n", + "- 默认情况下,`RunPCA`只对先前确定的变量特征进行降维" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "eb896d35", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PC_ 1 \n", + "Positive: CST3, TYROBP, LST1, AIF1, FTL, FTH1, LYZ, FCN1, S100A9, TYMP \n", + "\t FCER1G, CFD, LGALS1, S100A8, CTSS, LGALS2, SERPINA1, IFITM3, SPI1, CFP \n", + "\t PSAP, IFI30, SAT1, COTL1, S100A11, NPC2, GRN, LGALS3, GSTP1, PYCARD \n", + "Negative: MALAT1, LTB, IL32, IL7R, CD2, B2M, ACAP1, CD27, STK17A, CTSW \n", + "\t CD247, GIMAP5, AQP3, CCL5, SELL, TRAF3IP3, GZMA, MAL, CST7, ITM2A \n", + "\t MYC, GIMAP7, HOPX, BEX2, LDLRAP1, GZMK, ETS1, ZAP70, TNFAIP8, RIC3 \n", + "PC_ 2 \n", + "Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1, HLA-DRA, LINC00926, CD79B, HLA-DRB1, CD74 \n", + "\t HLA-DMA, HLA-DPB1, HLA-DQA2, CD37, HLA-DRB5, HLA-DMB, HLA-DPA1, FCRLA, HVCN1, LTB \n", + "\t BLNK, P2RX5, IGLL5, IRF8, SWAP70, ARHGAP24, FCGR2B, SMIM14, PPP1R14A, C16orf74 \n", + "Negative: NKG7, PRF1, CST7, GZMB, GZMA, FGFBP2, CTSW, GNLY, B2M, SPON2 \n", + "\t CCL4, GZMH, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX \n", + "\t TTC38, APMAP, CTSC, S100A4, IGFBP7, ANXA1, ID2, IL32, XCL1, RHOC \n", + "PC_ 3 \n", + "Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, HLA-DPA1, CD74, MS4A1, HLA-DRB1, HLA-DRA \n", + "\t HLA-DRB5, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, CD37, HVCN1, FCRLA, IRF8 \n", + "\t PLAC8, BLNK, MALAT1, SMIM14, PLD4, LAT2, IGLL5, P2RX5, SWAP70, FCGR2B \n", + "Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU \n", + "\t HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, PTCRA, CA2, ACRBP, MMD, TREML1 \n", + "\t NGFRAP1, F13A1, SEPT5, RUFY1, TSC22D1, MPP1, CMTM5, RP11-367G6.3, MYL9, GP1BA \n", + "PC_ 4 \n", + "Positive: HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1, CD74, HLA-DPB1, HIST1H2AC, PF4, TCL1A \n", + "\t SDPR, HLA-DPA1, HLA-DRB1, HLA-DQA2, HLA-DRA, PPBP, LINC00926, GNG11, HLA-DRB5, SPARC \n", + "\t GP9, AP001189.4, CA2, PTCRA, CD9, NRGN, RGS18, GZMB, CLU, TUBB1 \n", + "Negative: VIM, IL7R, S100A6, IL32, S100A8, S100A4, GIMAP7, S100A10, S100A9, MAL \n", + "\t AQP3, CD2, CD14, FYB, LGALS2, GIMAP4, ANXA1, CD27, FCN1, RBP7 \n", + "\t LYZ, S100A11, GIMAP5, MS4A6A, S100A12, FOLR3, TRABD2A, AIF1, IL8, IFI6 \n", + "PC_ 5 \n", + "Positive: GZMB, NKG7, S100A8, FGFBP2, GNLY, CCL4, CST7, PRF1, GZMA, SPON2 \n", + "\t GZMH, S100A9, LGALS2, CCL3, CTSW, XCL2, CD14, CLIC3, S100A12, CCL5 \n", + "\t RBP7, MS4A6A, GSTP1, FOLR3, IGFBP7, TYROBP, TTC38, AKR1C3, XCL1, HOPX \n", + "Negative: LTB, IL7R, CKB, VIM, MS4A7, AQP3, CYTIP, RP11-290F20.3, SIGLEC10, HMOX1 \n", + "\t PTGES3, LILRB2, MAL, CD27, HN1, CD2, GDI2, ANXA5, CORO1B, TUBA1B \n", + "\t FAM110A, ATP1A1, TRADD, PPA1, CCDC109B, ABRACL, CTD-2006K23.1, WARS, VMO1, FYB \n", + "\n" + ] + }, + { + "data": { + "image/png": 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ppxTjuuUivnYkYkCukfJnfTWX2T/Q8o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\n0pf8dZLsVkjG0aY6biF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wgmJW5n76ylX2JZqym/GIs+1WSzM1TvDh1hr0rQPOWI5CJqb1YHkvFH1OicRUOr\nKZr8+7g6YRmepPxPtUd+erSOg56VckWymzZ27U30I1nz6xn6wORLsehUlgcxvNzk6mYj0hUo\nVyRleWLV78wwh0BVQ1kvxfq27U5tWFeY6e8evhxF50je9KWOeLkiObkF/U6EJidkmb9Wi6r3\n3+xLYSfQfM/0/A4vOXpOg/YiFCySQDZsex1u3BatF4bmL7V7UNwJlCHdQkSaow6RZMPWvLI6\nX+0uWWexY6nlBFoWZNaodCEqEUljJV2V6GKSI1rDS+6xOzUzFTndT5bcTWQGRa7csSqpSSRR\nOmPsCeXvIh6VVuE6nwmPAv2uc4dPVhq8ztvEdVRMoSIFe4fcjkGRuJuqeQS6QC5M2mEI9bvO\nLKE842TNe9G6aqQckfxeVNcAk94Wlz/5cs6/ZlmeUktfrR+bFuyDs7mGS6IYkfxe1LGmbTd0\nzbCG3vpYd9P6a16649Wx5HRNPBpe3mZlGqf9gz9QikihupmOSFbjVUnG930/jvu+QPzxyV6B\nklWCUONz7TbaLZxCRJooGLvxOsYinbEbn6I/xqnwmtotwAA5vqy5cq0+oIErY8OXuWJESvhM\nOU1aWZnTHtljY/Qqmi3AzPgt1WVeTmUpPI9aKpQyRJo80a2Rc05jwAxncKohzlpbKrJczGVG\n1xy1UN48usWmLm9liDTVQBJtI3tecZvFZdMMGwgdMa+HYWLxRZMzzFw4hYjkIVJ2epL1iblF\nSdfwTFByE+cQYKbpM+NRlmjSUAGVKtKEC5ZQY7tJt5/kuKGWqg6FsfzQhkqyoQIqVqQQllwm\nu6DTevpTPd4BtqKcv8O70PGd7r9qvO5dg0imUWRV97Q85seSYuUOaxEVaXu6Pc2vAHgF0HiJ\nVCCSufqN1rwm925+W0Wue7CW2JXJrXf7AWpBQbRRYtGz/b+37vbrZ5xrjRRZI5JMMei45KQY\nhr9tFEwJeIlSd7qZ4i87+enkyqokdrbfuydfw1ynivTE1DDMD8oGR383UjBF4CRK3emBN8G1\nTJdIG8UVOdu/uu6/f/+/DSYdK9LEkZWJuWFWLVTC4rCKSRESLlxXKJHI2f7RfT7//nqZdKhI\nM+Pu+qExZH9wicIamOkAyr3qGVVWVQDaK63I2a7NuT9NOjUiebVtE5DCCzTOzJCE3KvuRcNz\n3ZZ3292SmBPpZdIZbSQn6W3S3c5IBt1rdBmyRiQ/qx2cSzdJvdnkMOKJZae22gKRs/3W/R1f\nfnTfZ4ik3AISfUWq16/Hj9LOh007tMM6zyf1GmRFJGuhWE+Tu2zyHi2auxgiZ/uvoY304O32\ns69SY0AAABaUSURBVJ9I8cMW7tGzSjA28x4RqtWo54T7pJnDWbs8x6fW4xw527+fWbsXP91t\nN5GWHzYlIpTVC6ic/3OXR53lm4iyQ/zUXMmrXLsrK5c7mal+pPH1V3d0RJKpbXuySHTbA1Pd\ncaq1XtlOQgUOd2iu9BVe7PhHz/bvXzf9+ufXsW2k8fro9KwPGYZhjKpbJ3Fv57xWOW4m+4l/\nseNf5lg7JyIN47vlDUjWEFVx6wSshaO3iQVn+9L6XT5HnXy3fP7WOGUyCc45stMx4MBqqhBp\nrM09X5qJbvZoSUfG1djlGKQMXr0K5YukHFtW6HLd4jUcEpHsDqY9tlgsxYskx6OqqQocYecE\nJiLSxeoBxYskTBqHNYwfuLMFp8MyFh29SVmuVQ6li2QFIVk00WFB4cK9VqnOMTGeZPLWowUr\nuhqFizQmtkXx6ug0DmJIafBerJ4xw9TRcD3iwKVRuEi99bt8zwn2AGQVbzSJubh02mQe6cPB\nLV+kXoxdCI6stIY5eItyPT0A7k7uCxXJS3grM2jI+mSYeyKVt3ofIB1nwGP6cnvszEmUJZLI\nzsmJoyhOY8ncbeaXX0tlVAAzXQ6TEekaHeVFieSO4DYfmFFAtkmvD/0q3zXuyjwMpYIPxUga\n33iRoVtFiWTdU+R8Mt58pLzZ5QzBj4IzwBLsQcJmcsgub8IluiPKEmmC2ST3rCfNlV1/6HdS\n4mo2NVvS9aq9q1r8bP98e/39df/Oveq1eNU6WeFor2zmOfo7R2re3jxpa2qK6Nn+3nUvgW5d\n9zvvqtfi9cuGemobLKMJzviuRR/ftNvS0ipXf3913a/vxPljc3x0t+FxxX8/b+tM2jUijck8\nv1V1xch0IEUfXKXCOVyXNJFuz6d2f6fNH31k8e1Hv/m5dWtqdzvefNvL7iXXneBhLLr8KyJ0\n6Srm4CqVaFKSSPfu1+O/j7T5Ex7H9a+51N0TNpy46gkW9+ZZ/RiRql1ANYiTlkqwbzwq5eDm\nFen1bMfnrOtFEg+I7B8P5HpL2HDiquP4ByDSGRTMkgfKWLSNCynqIpg6FqmphBIjklKTJv2z\n4d7d7v7L393t9/O5Wa9wcb9176Y61t36UaT7VBNn9pHFobdpbI1I7hO+n9PM89eiA/6VNXOR\nRX4q066sOEKlHNRZkX4/2jzvz5cf5uVz6tf74/+HSc8XtzGO3J9Vs+f5f3/OH6MokSzCvenC\njYRO9dA6YekxmBl/VcxBnRXp9t1/3x5PPrVevv/913Z5/f8v+vz3ePVrCE7/DVHqcf5PexQ7\n29+sqt3f7haebZKtyYZw0mDbSONCirwq/NAfvKVy4tAedNRnRXrkob8e2QPr5Z/nZz/9y5eP\nx/vxhP/8eGWsn1XBSY9iZ/vdPLF43OJilom0OMpEL4TIkpmZiDRMiz/35LCQNZ1sGKpVjz/e\nS/O/W/l6pt3+BayXcHGiz/6W6YXxh/uWsUikSLsnOOvwX8yjUmoaV0FZf54vncbpUQWyh0jP\n4PRoNc0k3OIdsiaS3Vcl7bZEpKnsncg3BOclIu2Jn7oJnbbJF8XMTGa/14k0TPwjf1YiuPbY\nB7fu9vnohv3+fJOdswvY+Oxv/32o04gAdCih/qNQg8mffhATnUivytnXo5t1fPnhi/Su20iv\nLqBn18/jg9/TiYL42f7RjXz8jc60btWzBCNS5BAxkCEDwZ4E6YTyXvlLPSeVe10bU3VfzsvX\nZ+P/n4+s3f2RrHuObPj78Woj9Y8GztRIuYmz/fv+SKl//F4VjqZXnUi4o2iofy/sViy2fEvA\nOV5ug1VWl1J7bGPvz+OZMeiGIT+/zMve/t/0I73G2r2PH/yr3E1ElDJ/jeKFCt1LFnpCV2DR\n4LrmZrouyRFp6VErKEL9s+Gje/scXt6fwxkCIj2GOHy8gsf9ZubvH5W7ieR1ySLpe8n8Wrjq\n52/k89blTimnjOth8SErZ2DJqlEF6WuPffD5L8S9r8l6z696ESp2xgcGAC1fNcQIH5xtF59z\nL13niPT+yjOsGfU9t+rlRAq1P7toGiZ2YKenzhXG9SLSZ3f70/dft1U9sdOrniWSFpqwaeJz\nWMmSwxkYeF8gp4j0/hoQsW5s0PSq55B9rc5DGqZ6/souxBLZfLz8X39LWWWjxTQz+nvVaNXp\nVc8SjUijS7ZdupdpptoBNkvy2MHXc5nTdZutlTmRNoTDVYvGjvLQhz7kvv3ZnYS46XJvteC2\nM+OR/0xbd3rs0rVls9VSmEj+kH1nejTvbZkkx+M1WnC7Y0WhxXevXO6ony+S30kk31pPZbAn\n+SsKRSTIwsJjeb16wOkizRzywNiGmeFBUAKXK6HTRQqOfLTeBMbNXe56dyXqLNuoSA75Vj2P\nO4QyMEd4MjRApVfJEkVK644IPjgA6qfOkix60KpNLI0kJtRZBlWRfIgvVhaViOT8vGWgK3Dq\nJmPIRvIhvlpZ1CGSkWSik5WItDPL+hQuVhZ1iKT7iF6jhK52tTsZp1N82bG/SkkVL5LfQ+uP\nqrtKYZ2D0ym+7Nbky1zzyhbJ+elSZVpHsppxmcI6iQWXrUDbNffeFErRIg3Bx7rdRZlxdFqy\nqxRWBUwURdulVLRI+vEM4m0v1JpK1LVdbGUTG1V89H4cSdki9TFZxFjw6GJZNg/LWfIAwmYo\nXqSgLHb7N0+xtV3Oh3LFQ1m+SEFkmiFP8CGEwRbqE8nKLljJu+3rBVhJ+SIFbvULDg/PGVKQ\nChZSvEjyiSc6/li3wq7rcZ/bZsM0/eXOoniRxrEMMhC5D2fIsyG5zexrLIjGLxMnUYdIvYlM\nr2m9eBU/LThhgnBYdqB8keZyCfELLJdeOIwKRJqDiATn04BIAOdTlUjWmLvAaFUiEJxFTSK5\no8DDv9gIcAI1iRTtT7JHhwMcTy0i+V1I8jMMgpOpRCRlj/fu7de5RzYALKUSkcRPUGihnEfZ\nEZfgRGoRSdw4Yd0cu/j3RgD2oBaRhuhjniGk3AodEQlOpBaRxmHe1oNPxGc9LSU4k2pEehC5\nv9x5mHH2zQLMUpVIMUKZPIAjaUKkBwgEZ9KKSJd8BNTOcPAW0IpIl3wo4b5w8JZQh0hLSjTw\nXBRYBQdvAVWIFH/UatK8ALtThUgTj1pNmBdgf+oQaUD0I7m39aEPnEp5IoVvfx0fJGT+WM+z\no0IH51KcSOHbX4dHcg1vxmn+RwDnUJxIExHJmW/86T7G2MH5lCfSNO5QVbeeB3AKlYkU/41S\nPIIzqUwkHsAFZVKbSC+sZ+j7kwGOpkqRxK9RhCeHPgXYkSpFcgyJPbWBBAQcRlUihb2Y+DmK\n7HsAEKYmkWLG4AucTk0iYQwUS/kiYQ9UQPEibcsYYCEcQ/EiRX4lNs0Q8nZwEOWLFL5BNtEQ\nPIJjqEGk9REJ4CCqEMnHjUh4BedSqUju2AbaQnAutYrkEPgVMoADqVskBtdBIVQtEsO9oRTq\nESkkCeJAIVQj0twjIXEKzqQSkcLDG4bP9AO8Mm4QYBF1iDSdRTC/zpxviwCLqEOklGBDRIIT\nqUQkgLKpRqT0H3YBOJ5aRPK7jEItIuSCk6hFpKSIJORCKTiUakRKwnhEBg8OpXiR1t3Ah0dw\nLKWLlBZavNuTEAmOpXSR/FgTmmr91hi/OwbHU7xIDsPv8zkPzx8HCfX+ZynrDL4EWEBtIkUi\nkpi2WCSZ66NKCOuoTqR5Fo8VIiLBZuoWafKp+kgBx1GrSNYPyC76EGAH6hRJpQQdPILjqEUk\nN0u34LF2CAX7U4lI0z8WO1mLo4oHB1CBSAmZAyISnEz5IhFRoALKFykpouAanEsFInn4jxQi\nasHJVCiSCjwwCI/gXCoUafIhdwCnUKNIARALzqUekSYfEUkbCc6lGpHmHraKSXAm1YjkDBIi\nbwdFUY9IkvS8HXrBIdQjkheR1jwVBWAfqhEp7VGr/mLrNrZqKbgw1YgkHv44RKP9znbiGCyl\nPpFUaGSDNUe+bQEkUo1Ixp1Y+4g4AudRjUgJUQKP4DTqEckm+Hg7gLOoVKTgA1fxCk6jUpGC\nEYlGEpxG+SKpwKvZWdPmB8hF8SKJbN3CHlgCFBxH8SIlRaSwPXgEh1G+SAlgD5xNEyKRDIez\nqVwk6Q5tIjiPmkTyPbHdQSQ4jSJESvzl8oAo/i9grlkzwFZKECm1TjYXkfwZqO3BQZQg0oa4\nMbckHsExFCFSceAfLASRAlAjhKXUKNL+ZzkewUIqFIl4AeVRoUjECyiPCkXCIyiP+kSiZgcF\nUp9I8gF3AIVQoUgjhCYoh4pFYlgDlEPNIs0wG7EQDbLRsEhzolA1hHy0LNIceATZqE0kTn4o\nko1n++e4/P3W3e5/c646CNUxKJNtZ/t3Nyz/3j14y7jqCHgERbLpbP++DSL96W7fj3d/sq0a\noCq2nO2f3fsg0r37+vf/f93vXKsGqIstZ3t37weRPrqf/lHR+8i16vUE6n5UB2F3tpzt3/0o\nkv0nw6pXE3qSEAkK2J2NZ3tYpO7FtlUvJvq4VTyC3dlFpCyrXspc4EEn2JF2RJp5ADgVPNiT\nFWe7rLcNf2+niqTE37gveAQ7kkekV9bu59isnfsDZM+/r/923jKAS56q3e9nP9JXd8+46jnE\nT/nJv0p8glBwFHlEOmVkw+wP+NEsgsPII1L/9qzuvedcdQbU8x/A/mQS6e9z9HfWVS8gKotS\nRCU4hNruRwoxIQsRCY6hBZFeP9WHMXAijYikyCzAqTQg0tBxhEdwIvWLRCiCAqhfpOlQhGRw\nCA2I5CHkIVzBMTQokiUPg4XgEBoQyb8l1p+FwAT7UrdISgUlUfo/awrAblQtklJBk8JTAXak\napFeQxp6Txk/IgHsS0UiTQ1NzbwpgIXUI1JUl+jAVPSCw6hHpKguUcEIVHAYFYkUY+IeitlZ\nAPLQgEgPZh5pR2yCnWlCpNn7YPEIdqYFkbifHE6nXpH8AXUAp1GtSHYUQiU4lxpF8h/yTd0O\nTqZCkULW4BGcS4Uipdw3EZsIsA81iuQSrNhR24MjaVakxLgFkIUWREpThBAFO9KESGngEexH\nyyIxaBUOo2GRxC+RUauDnWlNpPBwBzyCnalUpFiajtgD51CZSBM/aqn4cRc4j7pEmvyZZRyC\n86hLJGSBQqlMpCUgHRxHuyKRd4ADaVckIhIcSMMiARxHOyIRgOBEKhbJNmdsEqmJ8Qy4BntR\nr0huMmH0KD7CjvQD7Ea1IsUenU9EgjMoXKSFP+WCKXASZYs0URkLehT5FUyAvSlbpAUe6F/p\nU9aEqcF5ANkoXKQo3q9damN09k7+ACZ5BtiXSkXyxVDui1BOD2AnihVp5syf+xkXxIFDKVWk\nTXUxxQ+9wMGUKtJUSJl3JByRppbDO9hEsSLFWfvzfFPLEcFgGxWKFDJF/urYmLZLWS7pM4BZ\nahTJxwooyp/kzL37/sDlaEOkYJBaU8UDWEcjIgWID2rAI8hOuyKN44UIQHAADYvUDxLhEexP\n2yIhERxEQyJRhYPzaEckhgXBidQskvdIhsQZAbJTsUjJEYhQBbtTsUjpgQaPYG9qFikGv5ME\nh9OgSI+aHLU5OJYGRdIRCZfgMFoUaYCoBMfRsEjmCV0Ae9OySA8mn8iKZJCLekVyu2PTZntN\nU/bTIwG2Uq1IjgWTUsTuSIrdkw6wlGpFCkekoBrTkYe4BBmoV6QQLymWBKueiAQ5aEMk53nF\nic0ngGw0IVJK7QybYE+aECkhnU1LCHalWpGCD+CaydzhEuxFrSKFfpzPyTGkLAOQh1pFmnlu\ncdgaPIK9qFakfv43kgAOo2KRvJjDI1XhNCoWaXYAA20iOIyaRXIhIsFptCSSCx7BYbQpEndJ\nwME0KdJ4v9FpOwCXo0mRUAiOpnaRUAaKoHKR0h9bvO9+wNWpXKTUlDeJB9iX2kVyiRmDR7Ar\nDYikJt4BHEP9IlFrgwKoXyRiEBRAAyIBnA8iAWQAkQAycAWRaETB7lxAJNJ6sD8XEImIBPtz\nBZEAdgeRADJwAZGo2cH+tC8SuQY4gPZFIiLBAVxAJID9QSSADCASQAYuJhLtJdiHa4lEBg92\nolWRIsLgEexDoyI9Qw/WwGE0KtJDIupxcBytivQAj+AwWhYJ4DAQ6QXRCzaBSE9oT8E2EOkF\nHsEmEAkgA4gEkAFEAshAOyLRyoETaUak2bwbosGONCNSSBQ5iQQ37Ek7IvnY7uAR7EjLIuEO\nHEbTIgEcBSIBZKBVkajVwaG0I5KlDjk6OJZmRHLUwSM4lGZE8tRBJTiQdkRyoHIHR9KsSEQk\nOJJ2RQI4EEQCyAAiAWTgIiLRYIJ9uYZIpPBgZ64hEhEJdqZdkXAHDqRZkajNwZE0KxIRCY6k\nXZEADgSRADKASAAZQCSADCASQAYQCSADiASQAUQCyAAiAWQAkQAygEgAGUAkgAwgEkAGEAkg\nA4gEkAFEAsgAIgFkAJEAMoBIABlAJIAMIBJABhAJIAOIBJABRALIwCVE8p8VydMjIS9XEMl/\nejHPM4bMXEEkIhLsziVEAtgbRALIACIBZACRADKASAAZQCSADCASQAYQCSADiASQAUQCyAAi\nAWQAkQAygEgAGUAkgAwgEkAGEAkgA4gEkAFEAsgAIgFkAJEAMoBIABlAJIAMIBJABhAJIAOI\nBJABRALIACIBZACRADKASAAZQCSADCASQAYQCSADe4oEkJn9ztatFLxrp9Dq8Wj1exUDB9im\n1ePR6vcqBg6wTavHo9XvVQwcYJtWj0er36sYOMA2rR6PVr9XMXCAATKASAAZQCSADCASQAYQ\nCSADiASQAUQCyAAiCe637nb/e/ZeZOZzLOImv10xIJLh/TnA+O3s3cjL9zhkuslvVw6IpPnT\n3b7771v35+wdycm/7/Mq4ia/XUEgkubeff37/7/u99k7kpHP7n0QqcVvVxKIpPnofvpHVejj\n7B3JSHfvB5Fa/HYlgUia4Ywr+TbMxXy7X6upb1cSHFdNo6caIh0Cx1XT6KmGSIfAcdU0eqoh\n0iFwXDW3Nk+14fs0+u2KgeOqeeW1flrLa1lZu+a+XTEgkub3s6flq7ufvSN5GURq9NsVAyJp\nGu37Z2TDISCS4e05Gu397N3IzNgqavPbFQMiGf4+x0efvRe5GUVq89sVAyIBZACRADKASAAZ\nQCSADCASQAYQCSADiASQAUQCyAAiAWQAkQAygEgAGUCkoxh/4f79c5zyfX/rutuvr9gSvymc\neqCsjqLTDCOwP8b3kXvtvribtSIoq6MYtfhz654x6da9/fe37/9+3sL3NvzziMKpB8rqKLQW\nf57ifGh9fgazbH53N0SqCMrqKIwWj1ff3U1/8hV4tP2/ePWDSBVBWR2FLdJdRqHvwNyfPPGn\nKiiro9BafD0qde9dwJ7YElA+lNVRjFp83R7P80mRBJEqgrI6CpP+vveI1ByU1VEMFt0+vl7v\nUpbYe58gG5TVUdhafNBGagvK6ihsLX6LrN3f26+EJaBoKKujsLX4EX1Hn5HnCCNSRVBWR+Fo\n8aHt+blFqnmIVBGU1VE4Wvy9dW9ff/9p9HmL/UAyIlUEZXUUrhY/b2M6PPZD44hUEZTVUfha\nfH3cuu7tHk3fIVJFUFYAGUAkgAwgEkAGEKkIOouz9waWQ6EVASLVDoUGkAFEAsgAIgFkAJEA\nMoBIABlAJIAMIBJABhAJIAOIBJABRALIACIBZACRADKASAAZQCSADCASQAYQCSADiASQAUQC\nyAAiAWQAkQAy8D8JmXwT7vZUDgAAAABJRU5ErkJggg==", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# PCA降维\n", + "pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))\n", + "\n", + "# 可视化\n", + "VizDimLoadings(pbmc, dims = 1:2, reduction = \"pca\")\n", + "DimPlot(pbmc, reduction = \"pca\")" + ] + }, + { + "cell_type": "markdown", + "id": "69e4f45d", + "metadata": {}, + "source": [ + "### 6 确定数据集维度\n", + "- 单细胞`RNA-Seq`数据中,每个基因的表达受到大量的“技术“噪音影响。为了克服这种噪音,Seurat使用`PCA`对细胞进行聚类,其中每个主成分实质上表示一组相关特征的综合信息。\n", + "- 选择多少个主成分进行聚类?\n", + " - 1 随机对数据的子集进行置换(默认为1%),重新运行PCA,并构建特征分数的“零分布”,重复此过程,将具有显著富集低p值特征的主成分视为“显著”的,保留这些主成分即可。\n", + " - `JackStrawPlot()`函数用于将每个主成分的p值分布与均匀分布(下图虚线)进行比较。\n", + " - 具有低p值特征富集的主成分将显示出在虚线上方的低p值特征的显著富集(实线曲线)。\n", + " - 在这种情况下,第1到12个主成分之后的显著性显著下降,因此对于这个数据集,可以选用的数据数据维度为10,也就是保留10个主成分。" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "47b11953", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"\u001b[1m\u001b[22mRemoved 23496 rows containing missing values (`geom_point()`).\"\n" + ] + }, + { + "data": { + "image/png": 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IZL9At390tuB8/9SG01AxYGWcprIIq0DY+K2TURn5+htyfNFFKXiQDHlWn1jGf5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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pbmc <- JackStraw(pbmc, num.replicate = 100)\n", + "pbmc <- ScoreJackStraw(pbmc, dims = 1:20)\n", + "JackStrawPlot(pbmc, dims = 1:15)" + ] + }, + { + "cell_type": "markdown", + "id": "b6abfd8d", + "metadata": {}, + "source": [ + "### 7 细胞聚类和UMAP降维\n", + "- 细胞聚类\n", + " - 目前多数细胞聚类的算法都是基于图聚类的,具体而言,是将细胞嵌入到一个图结构中,例如K最近邻(KNN)图,其中相似的特征表达模式的细胞之间有边连接,然后试图将该图划分为高度相互连接的“拟团”(quasi-clique)或“群集”(‘communities)。\n", + " - `Seurat`使用的聚类算法也是基于图的,首先在`PCA`空间中基于欧氏距离构建一个KNN图,并根据局部邻域中两个细胞的共享重叠(Jaccard相似度)来调整边的权重。\n", + " - 首先,通过调用`FindNeighbors`函数,将细胞嵌入到一个图结构中。这里使用了数据集的前10个主成分(PCs)作为维度来计算细胞之间的相似性。\n", + " - 通过计算细胞间的欧氏距离,构建了一个K最近邻(KNN)图。该函数还根据局部邻域中细胞的重叠程度(Jaccard相似度),进一步优化了细胞之间的边权重。\n", + " - 接下来,调用`FindClusters`函数对细胞进行聚类分析.该函数通过迭代地将细胞分组,以最大化标准模块度函数来优化聚类结果,模块度函数衡量了细胞在群集中的连接紧密程度。\n", + " - 该函数还有一个`resolution`参数,用于设置聚类的“粒度”,增加参数值将产生更多的聚类。通常情况下,对于包含大约3K个细胞的单细胞数据集,将参数值设置在0.4-1.2之间可以得到良好的结果。对于更大的数据集,通常需要增加分辨率参数值\n", + " - 调用`idents()`函数,可以获取到聚类的结果\n", + " - UMAP 降维\n", + " - Seurat 提供了多种非线性降维技术,例如 tSNE 和 UMAP,以可视化和探索这些数据集。这些算法的目标是学习数据的底层流形,以便将相似的单元格放在低维空间中。" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "29dc7d9d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing nearest neighbor graph\n", + "\n", + "Computing SNN\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n", + "\n", + "Number of nodes: 2638\n", + "Number of edges: 95965\n", + "\n", + "Running Louvain algorithm...\n", + "Maximum modularity in 10 random starts: 0.8723\n", + "Number of communities: 9\n", + "Elapsed time: 0 seconds\n" + ] + }, + { + "data": { + "text/html": [ + "
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'8'\n", + "\n", + "\n" + ], + "text/plain": [ + "AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 \n", + " 2 3 2 1 \n", + "AAACCGTGTATGCG-1 \n", + " 6 \n", + "Levels: 0 1 2 3 4 5 6 7 8" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 细胞聚类\n", + "pbmc <- FindNeighbors(pbmc, dims = 1:10)\n", + "pbmc <- FindClusters(pbmc, resolution = 0.5)\n", + "\n", + "#查看前5个细胞簇的ID\n", + "head(Idents(pbmc), 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "50dc8f40", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "10:09:51 UMAP embedding parameters a = 0.9922 b = 1.112\n", + "\n", + "10:09:51 Read 2638 rows and found 10 numeric columns\n", + "\n", + "10:09:51 Using Annoy for neighbor search, n_neighbors = 30\n", + "\n", + "10:09:51 Building Annoy index with metric = cosine, n_trees = 50\n", + "\n", + "0% 10 20 30 40 50 60 70 80 90 100%\n", + "\n", + "[----|----|----|----|----|----|----|----|----|----|\n", + "\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "*\n", + "|\n", + "\n", + "10:09:51 Writing NN index file to temp file C:\\Users\\21145\\AppData\\Local\\Temp\\Rtmpc9Ugn1\\file4c80704d79c7\n", + "\n", + "10:09:51 Searching Annoy index using 1 thread, search_k = 3000\n", + "\n", + "10:09:52 Annoy recall = 100%\n", + "\n", + "10:09:52 Commencing smooth kNN distance calibration using 1 thread\n", + " with target n_neighbors = 30\n", + "\n", + "10:09:53 Initializing from normalized Laplacian + noise (using RSpectra)\n", + "\n", + "10:09:53 Commencing optimization for 500 epochs, with 105124 positive edges\n", + "\n", + "10:10:00 Optimization finished\n", + "\n" + ] + }, + { + "data": { + "image/png": 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yDSh/joeWvapp3+QTYpj5qHVbyLAvPCK4I9yuqrA0pVnfrWe8++lCLRVJ3ZRAPT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\nBCASABGASABEACIBEIH/D3U1Lq5AxiyiAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# UMAP降维\n", + "pbmc <- RunUMAP(pbmc, dims = 1:10)\n", + "DimPlot(pbmc, reduction = \"umap\", label = TRUE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d50fac5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.2.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}