作者雪标，追風(fēng)少年i
國慶前的最后一周了，大家好好努力劳吠，有句臺詞說得好，Yesterday is history巩趁， Tomorrow is a mystery痒玩， But today is a gift That is why it’s called the present (the gift) 。
這一篇來回答一些項目中遇到的問題议慰，以及簡單分享一下如何衡量樣本間的異質(zhì)性蠢古。
最近很多老師來做分析，其中大多數(shù)拿著疾病的樣本别凹，然后告訴我做什么分析草讶，要分析什么老師也不知道，只是說做分析炉菲，搞得我難以下手堕战，簡單的課題設(shè)計還是要準(zhǔn)備好的坤溃。
疾病樣本：其實單細胞樣本的分析本質(zhì)還是分組找差異，只是這個差異從更高的維度來分析嘱丢，無論是通訊的差異還是轉(zhuǎn)錄狀態(tài)的差異薪介，前提是有組可分，最理想的狀態(tài)就是normal和disease越驻，當(dāng)然很多老師無法取到normal的樣本汁政，那么臨床信息就顯得尤為重要，預(yù)后好和預(yù)后差之間的差異伐谈，才具有臨床指導(dǎo)的價值烂完。
如果說僅僅有疾病樣本，臨床信息也沒有的前提下诵棵，是很難分析出有效的信息抠蚣，這個時候很多老師就開始探索單細胞數(shù)據(jù)庫，從數(shù)據(jù)庫的隊列中尋找normal的數(shù)據(jù)來進行分析履澳，方法是可取的嘶窄，但是要注意數(shù)據(jù)的匹配程度，平臺差異等等距贷。
樣本的注釋問題柄冲，流程化的注釋是不可取的，多次強調(diào)過忠蝗，市面上都說新格元注釋做的好现横，也是浪費了很大的人工，并不是流程化帶來的阁最，資料和經(jīng)驗戒祠，也是科研工作者必備的素質(zhì)。
樣本量的問題速种，單細胞發(fā)展到現(xiàn)在姜盈，僅靠一兩個樣本發(fā)文章是不現(xiàn)實的（二區(qū)及以上），無論從哪個角度分析配阵，都需要在多個樣本中驗證研究的價值馏颂，所以大家如果決定做單細胞分析，就要想好這一點棋傍。
好了救拉，關(guān)于課題設(shè)計，其實也是一個很大的學(xué)問瘫拣，這樣的工作通常是技術(shù)支持來做的亿絮，接下來分享一個簡單的內(nèi)容，衡量樣本間的異質(zhì)性。
- 指標(biāo)1：Cluster entropy壹无，為了測量來自不同樣本的細胞在細胞類型cluster中的混合程度如何葱绒，量化了數(shù)據(jù)集的歸一化相對cluster entropy，按cluster大小加權(quán)斗锭。 cluster entropy為 1 表示樣本在cluster 之間完全混合地淀。
圖片.png
參考文章，10X單細胞（10X空間轉(zhuǎn)錄組）基因表達的熵值分析
圖片.png
- 指標(biāo)2 ：Similarity scores/alignment岖是，為了測量來自相同與不同批次和/或樣品的細胞之間細胞類型內(nèi)細胞狀態(tài)的轉(zhuǎn)錄變異帮毁，測量了每個樣品/批次之間的成對對齊（就是整合），其中批次由同一天處理的樣品組組成豺撑。 這個“相似度分?jǐn)?shù)”檢查特定樣本/批次中每個細胞的局部鄰域烈疚，詢問其 k 個最近鄰居中有多少個（在 PC 或 iNMF（這個大家應(yīng)該都知道） 空間中）屬于第二個樣本/批次，然后在所有細胞上取平均值 . 這里選擇 k 為cluster內(nèi)細胞總數(shù)的 1%聪轿。 結(jié)果通過每個樣品/批次的預(yù)期細胞數(shù)進行標(biāo)準(zhǔn)化爷肝。
最后匯總一下Seurat包的所有函數(shù)
| 函數(shù) | 作用 |
|---|---|
| AddModuleScore | Calculate module scores for feature expression programs in single cells |
| AggregateExpression | Aggregated feature expression by identity class |
| AnchorSet-class | The AnchorSet Class |
| AnnotateAnchors | Add info to anchor matrix |
| Assay-class | The Assay Class |
| AugmentPlot | Augments ggplot2-based plot with a PNG image. |
| AverageExpression | Averaged feature expression by identity class |
| BGTextColor | Determine text color based on background color |
| BarcodeInflectionsPlot | Plot the Barcode Distribution and Calculated Inflection Points |
| BlackAndWhite | Create a custom color palette |
| BuildClusterTree | Phylogenetic Analysis of Identity Classes |
| CalcPerturbSig | Calculate a perturbation Signature |
| CalculateBarcodeInflections | Calculate the Barcode Distribution Inflection |
| CaseMatch | Match the case of character vectors |
| CellCycleScoring | Score cell cycle phases |
| CellScatter | Cell-cell scatter plot |
| CellSelector | Cell Selector |
| Cells.SCTModel | Get Cell Names |
| CellsByImage | Get a vector of cell names associated with an image (or set of images) |
| CollapseEmbeddingOutliers | Move outliers towards center on dimension reduction plot |
| CollapseSpeciesExpressionMatrix | Slim down a multi-species expression matrix,when only one species is primarily of interenst. |
| ColorDimSplit | Color dimensional reduction plot by tree split |
| CombinePlots | Combine ggplot2-based plots into a single plot |
| CreateSCTAssayObject | Create a SCT Assay object |
| CustomDistance | Run a custom distance function on an input data matrix |
| DEenrichRPlot | DE and EnrichR pathway visualization barplot |
| DietSeurat | Slim down a Seurat object |
| DimHeatmap | Dimensional reduction heatmap |
| DimPlot | Dimensional reduction plot |
| DimReduc-class | The DimReduc Class |
| DiscretePalette | Discrete colour palettes from the pals package |
| DoHeatmap | Feature expression heatmap |
| DotPlot | Dot plot visualization |
| ElbowPlot | Quickly Pick Relevant Dimensions |
| ExpMean | Calculate the mean of logged values |
| ExpSD | Calculate the standard deviation of logged values |
| ExpVar | Calculate the variance of logged values |
| FastRowScale | Scale and/or center matrix rowwise |
| FeaturePlot | Visualize 'features' on a dimensional reduction plot |
| FeatureScatter | Scatter plot of single cell data |
| FilterSlideSeq | Filter stray beads from Slide-seq puck |
| FindAllMarkers | Gene expression markers for all identity classes |
| FindClusters | Cluster Determination |
| FindConservedMarkers | Finds markers that are conserved between the groups |
| FindIntegrationAnchors | Find integration anchors |
| FindMarkers | Gene expression markers of identity classes |
| FindMultiModalNeighbors | Construct weighted nearest neighbor graph |
| FindNeighbors | (Shared) Nearest-neighbor graph construction |
| FindSpatiallyVariableFeatures | Find spatially variable features |
| FindSubCluster | Find subclusters under one cluster |
| FindTransferAnchors | Find transfer anchors |
| FindVariableFeatures | Find variable features |
| FoldChange | Fold Change |
| GetAssay | Get an Assay object from a given Seurat object. |
| GetImage.SlideSeq | Get Image Data |
| GetIntegrationData | Get integration data |
| GetResidual | Calculate pearson residuals of features not in the scale.data |
| GetTissueCoordinates.SlideSeq | Get Tissue Coordinates |
| GetTransferPredictions | Get the predicted identity |
| Graph-class | The Graph Class |
| GroupCorrelation | Compute the correlation of features broken down by groups with another covariate |
| GroupCorrelationPlot | Boxplot of correlation of a variable (e.g.number of UMIs) with expression data |
| HTODemux | Demultiplex samples based on data from cell 'hashing' |
| HTOHeatmap | Hashtag oligo heatmap |
| HVFInfo.SCTAssay | Get Variable Feature Information |
| HoverLocator | Hover Locator |
| IFeaturePlot | Visualize features in dimensional reduction space interactively |
| ISpatialDimPlot | Visualize clusters spatially and interactively |
| ISpatialFeaturePlot | Visualize features spatially and interactively |
| IntegrateData | Integrate data |
| IntegrateEmbeddings | Integrate low dimensional embeddings |
| IntegrationAnchorSet-class | The IntegrationAnchorSet Class |
| IntegrationData-class | The IntegrationData Class |
| JackStraw | Determine statistical significance of PCA scores. |
| JackStrawData-class | The JackStrawData Class |
| JackStrawPlot | JackStraw Plot |
| L2CCA | L2-Normalize CCA |
| L2Dim | L2-normalization |
| LabelClusters | Label clusters on a ggplot2-based scatter plot |
| LabelPoints | Add text labels to a ggplot2 plot |
| LinkedPlots | Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework |
| Load10X_Spatial | Load a 10x Genomics Visium Spatial Experiment into a 'Seurat' object |
| LoadAnnoyIndex | Load the Annoy index file |
| LoadSTARmap | Load STARmap data |
| LocalStruct | Calculate the local structure preservation metric |
| LogNormalize | Normalize raw data |
| LogVMR | Calculate the variance to mean ratio of logged values |
| MULTIseqDemux | Demultiplex samples based on classification method from MULTI-seq |
| MapQuery | Map query cells to a reference |
| MappingScore | Metric for evaluating mapping success |
| MetaFeature | Aggregate expression of multiple features into a single feature |
| MinMax | Apply a ceiling and floor to all values in a matrix |
| MixingMetric | Calculates a mixing metric |
| MixscapeHeatmap | Differential expression heatmap for mixscape |
| MixscapeLDA | Linear discriminant analysis on pooled CRISPR screen data. |
| ModalityWeights-class | The ModalityWeights Class |
| NNPlot | Highlight Neighbors in DimPlot |
| Neighbor-class | The Neighbor Class |
| NormalizeData | Normalize Data |
| PCASigGenes | Significant genes from a PCA |
| PercentageFeatureSet | Calculate the percentage of all counts that belong to a given set of features |
| PlotClusterTree | Plot clusters as a tree |
| PlotPerturbScore | Function to plot perturbation score distributions. |
| PolyDimPlot | Polygon DimPlot |
| PolyFeaturePlot | Polygon FeaturePlot |
| PredictAssay | Predict value from nearest neighbors |
| PrepLDA | Function to prepare data for Linear Discriminant Analysis. |
| PrepSCTIntegration | Prepare an object list normalized with sctransform for integration. |
| ProjectDim | Project Dimensional reduction onto full dataset |
| ProjectUMAP | Project query into UMAP coordinates of reference |
| Radius.SlideSeq | Get Spot Radius |
| Read10X | Load in data from 10X |
| Read10X_Image | Load a 10X Genomics Visium Image |
| Read10X_h5 | Read 10X hdf5 file |
| ReadMtx | Load in data from remote or local mtx files |
| ReadSlideSeq | Load Slide-seq spatial data |
| RegroupIdents | Regroup idents based on meta.data info |
| RelativeCounts | Normalize raw data to fractions |
| RenameCells.SCTAssay | Rename Cells in an Object |
| RidgePlot | Single cell ridge plot |
| RunCCA | Perform Canonical Correlation Analysis |
| RunICA | Run Independent Component Analysis on gene expression |
| RunLDA | Run Linear Discriminant Analysis |
| RunMarkVario | Run the mark variogram computation on a given position matrix and expression matrix. |
| RunMixscape | Run Mixscape |
| RunMoransI | Compute Moran's I value. |
| RunPCA | Run Principal Component Analysis |
| RunSPCA | Run Supervised Principal Component Analysis |
| RunTSNE | Run t-distributed Stochastic Neighbor Embedding |
| RunUMAP | Run UMAP |
| SCTAssay-class | The SCTModel Class |
| SCTResults | Get SCT results from an Assay |
| SCTransform | Use regularized negative binomial regression to normalize UMI count data |
| STARmap-class | The STARmap class |
| SampleUMI | Sample UMI |
| SaveAnnoyIndex | Save the Annoy index |
| ScaleData | Scale and center the data. |
| ScaleFactors | Get image scale factors |
| ScoreJackStraw | Compute Jackstraw scores significance. |
| SelectIntegrationFeatures | Select integration features |
| SetIntegrationData | Set integration data |
| Seurat-class | The Seurat Class |
| Seurat-package | Seurat: Tools for Single Cell Genomics |
| SeuratCommand-class | The SeuratCommand Class |
| SeuratTheme | Seurat Themes |
| SlideSeq-class | The SlideSeq class |
| SpatialImage-class | The SpatialImage Class |
| SpatialPlot | Visualize spatial clustering and expression data. |
| SplitObject | Splits object into a list of subsetted objects. |
| SubsetByBarcodeInflections | Subset a Seurat Object based on the Barcode Distribution Inflection Points |
| TopCells | Find cells with highest scores for a given dimensional reduction technique |
| TopFeatures | Find features with highest scores for a given dimensional reduction technique |
| TopNeighbors | Get nearest neighbors for given cell |
| TransferAnchorSet-class | The TransferAnchorSet Class |
| TransferData | Transfer data |
| UpdateSCTAssays | Update pre-V4 Assays generated with SCTransform in the Seurat to the new SCTAssay class |
| UpdateSymbolList | Get updated synonyms for gene symbols |
| VariableFeaturePlot | View variable features |
| VisiumV1-class | The VisiumV1 class |
| VizDimLoadings | Visualize Dimensional Reduction genes |
| VlnPlot | Single cell violin plot |
| as.CellDataSet | Convert objects to CellDataSet objects |
| as.Seurat.CellDataSet | Convert objects to 'Seurat' objects |
| as.SingleCellExperiment | Convert objects to SingleCellExperiment objects |
| as.sparse.H5Group | Cast to Sparse |
| cc.genes | Cell cycle genes |
| cc.genes.updated.2019 | Cell cycle genes: 2019 update |
| contrast-theory | Get the intensity and/or luminance of a color |
| merge.SCTAssay | Merge SCTAssay objects |
| subset.AnchorSet | Subset an AnchorSet object |
周一了，偷個懶陆错，生活很好灯抛，有你更好
