英文:
Creating mapping id between two different data frame
问题
Here's the translated code portion without the translation of code itself:
I have data frame which is coming out of seurat analysis pipeline,- one set is for the cells which are annotated which results in one data frame that contains the cells, its cell type annotation and numerical cluster associated with each cell type cluster.- The second data frame is the genes which is how the cell types are annotated based on the expression of genes which are probable markers for the annotated cell types.That was the background.The cluster for the cell type> table(seurat@meta.data$louvain)CD4 T cells CD14+ Monocytes B cells CD8 T cells NK cells FCGR3A+ Monocytes1144 480 342 316 154 150Dendritic cells Megakaryocytes37 15> table(seurat@meta.data$seurat_clusters)0 1 2 3 4 5 6 71140 467 351 247 219 167 32 15The cluster for the genetable(cl_markers$seurat_clusters)0 1 2 3 4 5 6 7341 691 299 640 229 858 815 439The common factor here is the cluster number for both the gene and cell type.Now I can't map directly each cell type to the gene due to the differences in the dimension.> dim(mke_cluster)[1] 2638 2> dim(mke_gene)[1] 4312 3My small subset cluster dataframehead(mke_cluster)louvain seurat_clustersAAACATACAACCAC-1 CD4 T cells 0AAACATTGAGCTAC-1 B cells 2AAACATTGATCAGC-1 CD4 T cells 0AAACCGTGCTTCCG-1 CD14+ Monocytes 5AAACCGTGTATGCG-1 NK cells 3AAACGCACTGGTAC-1 CD8 T cells 0Gene subsethead(mke_gene)seurat_clusters gene avg_log2FCLDHB 0 LDHB 1.6653235RPS12 2 RPS12 0.8438077RPS25 2 RPS25 0.9089848CD3D 0 CD3D 1.5250903RPS27 1 RPS27 0.7858780RPS6 0 RPS6 0.7065248My objective is- Another column in the mke_gene data frame where the gene are labelled with the `*louvain*`.I was not sure how to map I tried merging I'm not sure if this is the right approach or not since both the df are different dimension, which is by default biologically.> merged_data <- merge(mke_cluster, mke_gene, by.x = "seurat_clusters", by.y = "seurat_clusters")Any suggestion or help would be appreciateddput(head(cl_markers, n = 10))structure(list(p_val = c(6.64840265863089e-242, 1.13459111505844e-224,5.8379603130071e-204, 1.10871523912512e-193, 1.2932367269787e-184,3.62844688134858e-184, 2.7796055265616e-178, 9.24017697438729e-173,9.23182535159467e-168, 4.61251612799725e-164), avg_log2FC = c(1.66532350813551,0.843807664890472, 0.90898477536885, 1.52509026397192, 0.785877979378442,0.706524751609463, 0.687290625486455, 0.656853693459277, 0.759283189548348,0.824286733662864), pct.1 = c(0.936, 1, 1, 0.879, 0.999, 1, 1,1, 0.996, 0.997), pct.2 = c(0.477, 0.989, 0.967, 0.247, 0.989,0.993, 0.993, 0.993, 0.978, 0.953), p_val_adj = c(9.1176194060464e-238,1.55597825519115e-220, 8.00617877325794e-200, 1.52049207893619e-189,1.77354484737859e-180, 4.97605205308144e-180, 3.81195101912658e-174,1.26719787026747e-168, 1.26605252871769e-163, 6.32560461793543e-160), cluster = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,1L), levels = c("0", "1", "2", "3", "4", "5", "6", "7"), class = "factor"),gene = c("LDHB", "RPS12", "RPS25", "CD3D", "RPS27", "RPS6","RPS3", "RPS14", "TPT1", "RPL31")), row.names = c("LDHB","RPS12", "RPS25", "CD3D", "RPS27", "RPS6", "RPS3", "RPS14", "TPT1","RPL31"), class = "data.frame")dput(head(seurat@meta.data, n = 10))structure(list(orig.ident = structure(c(1L, 1L, 1L, 1L, 1L, 1L,1L, 1L, 1L, 1L), levels = "SeuratProject", class = "factor"),nCount_RNA = c(2419, 4903, 3147, 2639, 980, 2163, 2175<details><summary>英文:</summary>I have data frame which is coming out of seurat analysis pipeline,- one set is for the cells which are annotated which results in one data frame that contains the cells, its cell type annotation and numerical cluster associated with each cell type cluster.- The second data frame is the genes which is how the cell types are annotated based on the expression of genes which are probable markers for the annotated cell types.That was the background.The cluster for the cell type> table(seurat@meta.data$louvain)CD4 T cells CD14+ Monocytes B cells CD8 T cells NK cells FCGR3A+ Monocytes1144 480 342 316 154 150Dendritic cells Megakaryocytes37 15> table(seurat@meta.data$seurat_clusters)0 1 2 3 4 5 6 71140 467 351 247 219 167 32 15The cluster for the genetable(cl_markers$seurat_clusters)0 1 2 3 4 5 6 7341 691 299 640 229 858 815 439The common factor here is the cluster number for both the gene and cell type.Now I can't map directly each cell type to the gene due to the differences in the dimension.> dim(mke_cluster)[1] 2638 2> dim(mke_gene)[1] 4312 3My small subset cluster dataframehead(mke_cluster)louvain seurat_clustersAAACATACAACCAC-1 CD4 T cells 0AAACATTGAGCTAC-1 B cells 2AAACATTGATCAGC-1 CD4 T cells 0AAACCGTGCTTCCG-1 CD14+ Monocytes 5AAACCGTGTATGCG-1 NK cells 3AAACGCACTGGTAC-1 CD8 T cells 0Gene subsethead(mke_gene)seurat_clusters gene avg_log2FCLDHB 0 LDHB 1.6653235RPS12 2 RPS12 0.8438077RPS25 2 RPS25 0.9089848CD3D 0 CD3D 1.5250903RPS27 1 RPS27 0.7858780RPS6 0 RPS6 0.7065248My objective is- Another column in the mke_gene data frame where the gene are labelled with the `*louvain*`.I was not sure how to map I tried merging I'm not sure if this is the right approach or not since both the df are different dimension, which is by default biologically.> merged_data <- merge(mke_cluster, mke_gene, by.x = "seurat_clusters", by.y = "seurat_clusters")Any suggestion or help would be appreciateddput(head(cl_markers, n = 10))structure(list(p_val = c(6.64840265863089e-242, 1.13459111505844e-224,5.8379603130071e-204, 1.10871523912512e-193, 1.2932367269787e-184,3.62844688134858e-184, 2.7796055265616e-178, 9.24017697438729e-173,9.23182535159467e-168, 4.61251612799725e-164), avg_log2FC = c(1.66532350813551,0.843807664890472, 0.90898477536885, 1.52509026397192, 0.785877979378442,0.706524751609463, 0.687290625486455, 0.656853693459277, 0.759283189548348,0.824286733662864), pct.1 = c(0.936, 1, 1, 0.879, 0.999, 1, 1,1, 0.996, 0.997), pct.2 = c(0.477, 0.989, 0.967, 0.247, 0.989,0.993, 0.993, 0.993, 0.978, 0.953), p_val_adj = c(9.1176194060464e-238,1.55597825519115e-220, 8.00617877325794e-200, 1.52049207893619e-189,1.77354484737859e-180, 4.97605205308144e-180, 3.81195101912658e-174,1.26719787026747e-168, 1.26605252871769e-163, 6.32560461793543e-160), cluster = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,1L), levels = c("0", "1", "2", "3", "4", "5", "6", "7"), class = "factor"),gene = c("LDHB", "RPS12", "RPS25", "CD3D", "RPS27", "RPS6","RPS3", "RPS14", "TPT1", "RPL31")), row.names = c("LDHB","RPS12", "RPS25", "CD3D", "RPS27", "RPS6", "RPS3", "RPS14", "TPT1","RPL31"), class = "data.frame")#######################################################dput(head(seurat@meta.data, n = 10))structure(list(orig.ident = structure(c(1L, 1L, 1L, 1L, 1L, 1L,1L, 1L, 1L, 1L), levels = "SeuratProject", class = "factor"),nCount_RNA = c(2419, 4903, 3147, 2639, 980, 2163, 2175, 2260,1275, 1103), nFeature_RNA = c(779L, 1352L, 1129L, 960L, 521L,781L, 782L, 790L, 532L, 550L), n_genes = c(781, 1352, 1131,960, 522, 782, 783, 790, 533, 550), percent_mito = c(0.0301777590066195,0.0379359573125839, 0.00889736227691174, 0.0174308456480503,0.0122448978945613, 0.016643550246954, 0.0381609201431274,0.0309734512120485, 0.0117647061124444, 0.0290117859840393), n_counts = c(2419, 4903, 3147, 2639, 980, 2163, 2175,2260, 1275, 1103), louvain = structure(c(1L, 3L, 1L, 2L,5L, 4L, 4L, 4L, 1L, 6L), levels = c("CD4 T cells", "CD14+ Monocytes","B cells", "CD8 T cells", "NK cells", "FCGR3A+ Monocytes","Dendritic cells", "Megakaryocytes"), class = "factor"),percent.mt = c(3.01777594047127, 3.79359575769937, 0.889736256752463,1.74308450170519, 1.22448979591837, 1.66435506241331, 3.81609195402299,3.09734513274336, 1.17647058823529, 2.9011786038078), RNA_snn_res.1 = structure(c(1L,3L, 1L, 6L, 4L, 1L, 5L, 5L, 5L, 6L), levels = c("0", "1","2", "3", "4", "5", "6", "7"), class = "factor"), seurat_clusters = structure(c(1L,3L, 1L, 6L, 4L, 1L, 5L, 5L, 5L, 6L), levels = c("0", "1","2", "3", "4", "5", "6", "7"), class = "factor")), row.names = c("AAACATACAACCAC-1","AAACATTGAGCTAC-1", "AAACATTGATCAGC-1", "AAACCGTGCTTCCG-1", "AAACCGTGTATGCG-1","AAACGCACTGGTAC-1", "AAACGCTGACCAGT-1", "AAACGCTGGTTCTT-1", "AAACGCTGTAGCCA-1","AAACGCTGTTTCTG-1"), class = "data.frame")So here was my code to generate mke_cluster and mke_genemke_cluster = seurat@meta.data %>% select(louvain,seurat_clusters)mke_gene = cl_markers %>% select(cluster,gene,avg_log2FC)names(mke_gene)[1] = "seurat_clusters"**UPDATE**Seurat dataframedput(head(seurat, n = 10))structure(list(orig.ident = structure(c(1L, 1L, 1L, 1L, 1L, 1L,1L, 1L, 1L, 1L), levels = "SeuratProject", class = "factor"),nCount_RNA = c(2419, 4903, 3147, 2639, 980, 2163, 2175, 2260,1275, 1103), nFeature_RNA = c(779L, 1352L, 1129L, 960L, 521L,781L, 782L, 790L, 532L, 550L), n_genes = c(781, 1352, 1131,960, 522, 782, 783, 790, 533, 550), percent_mito = c(0.0301777590066195,0.0379359573125839, 0.00889736227691174, 0.0174308456480503,0.0122448978945613, 0.016643550246954, 0.0381609201431274,0.0309734512120485, 0.0117647061124444, 0.0290117859840393), n_counts = c(2419, 4903, 3147, 2639, 980, 2163, 2175,2260, 1275, 1103), louvain = structure(c(1L, 3L, 1L, 2L,5L, 4L, 4L, 4L, 1L, 6L), levels = c("CD4 T cells", "CD14+ Monocytes","B cells", "CD8 T cells", "NK cells", "FCGR3A+ Monocytes","Dendritic cells", "Megakaryocytes"), class = "factor"),percent.mt = c(3.01777594047127, 3.79359575769937, 0.889736256752463,1.74308450170519, 1.22448979591837, 1.66435506241331, 3.81609195402299,3.09734513274336, 1.17647058823529, 2.9011786038078), RNA_snn_res.1 = structure(c(1L,3L, 1L, 6L, 4L, 1L, 5L, 5L, 5L, 6L), levels = c("0", "1","2", "3", "4", "5", "6", "7"), class = "factor"), seurat_clusters = structure(c(1L,3L, 1L, 6L, 4L, 1L, 5L, 5L, 5L, 6L), levels = c("0", "1","2", "3", "4", "5", "6", "7"), class = "factor")), row.names = c("AAACATACAACCAC-1","AAACATTGAGCTAC-1", "AAACATTGATCAGC-1", "AAACCGTGCTTCCG-1", "AAACCGTGTATGCG-1","AAACGCACTGGTAC-1", "AAACGCTGACCAGT-1", "AAACGCTGGTTCTT-1", "AAACGCTGTAGCCA-1","AAACGCTGTTTCTG-1"), class = "data.frame")</details># 答案1**得分**: 1在您的`mke_gene`示例表格中是否存在错误，或者群集0是否真的具有两个不同的标签，即在第一行和第三行上标记为CD4，在第六行上标记为CD8？您是如何设置这些标签的？如果您只想在基因表格中添加群集注释，并且这些注释是唯一的，您可以避免多次匹配问题，像这样：```Rclusters <- unique(mke_cluster)merged_data <- left_join(mke_gene, clusters, by = "seurat_clusters")
英文:
Do you have a mistake in your mke_gene example table or does cluster 0 really have two different labels, CD4 on the first and third rows and CD8 on the sixth row? How did you set the labels?
If you just want to add the cluster annotations in the genes table and the annotations are unique, you can avoid the multiple matches problem like this:
clusters <- unique(mke_cluster)merged_data <- left_join(mke_gene, clusters, by = "seurat_clusters")
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