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zKPOwdDSW%T6IgiEPlYC0%#vHYzWe}ob+B^&p7jwm%p%H*hv)%xX#;QEr?D?=lFs3< zC82bdkv1GUSdyZ$33`qsFPX_iS%)L#8+}U0%Pq765g?cBa*}%kw!Z`rFAwMBQ{Eva z-0CW(LWJM#~h0D&LFGLlFAGb{ids!$Bpypw7fuOU}D3e*&}2r*J~#iP}B zJPAzLY!!f$bqHdhOaTb<-{h|!psb+_LjZ!9e1f4m2H~9nEQr` zOat(ru%IoGl3|;`*}@{vNrK1t%&{PYd9~lIT*}Eh>bZhxLqFzi57xgL7C`*4GyZA= zu+?~liv?xRh4ok4y+J~+agd;{t4Jc!6Z*MnzaTAsfleZ2B)II|(^jV6un<5$%!h$r z!^7h_85s}l;Q!&M{r_O<4~N;V^i^#kj&aO#nFRkw=fSt%#KD~A>7t;9W_ACOaoUps zuF9xf+4R4jyMyP$q*k$C{&zFz&}RN$^Z&OQ=LxO9v|`$iKm7& Date: Mon, 4 Jul 2022 13:10:16 +0800 Subject: [PATCH 2/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 300 ++++++++++++++++++++++++++++++++- 1 file changed, 299 insertions(+), 1 deletion(-) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index 99d71c7..e18e78f 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -206,6 +206,9 @@ Mouse embryo gene count matrix cluster by stage(from E4.5 to E7.5) :align: center +Mouse embryo gene count matrix cluster by celltype in E7.5. + + .. code:: r e75samplemeta = samplemeta[which(samplemeta$stage == "E7.5"),] @@ -237,12 +240,20 @@ Mouse embryo gene count matrix cluster by stage(from E4.5 to E7.5) :align: center +<<<<<<< HEAD +Using Signac to cluster WCG/GCH bin count matrix by stage(from E4.5 to E7.5). + + +Take WCG bin matrix for example. + +======= Using Signac to cluster WCG/GCH bin count matrix by stage. Take WCG bin matrix for example. ======= Using Signac to cluster WCG/GCH bin count matrix by stage and celltype. >>>>>>> 86b422a47b1e01ce5968969e5e6d23b0a44b0b5e +>>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 .. code:: r library(Signac) library(Seurat) @@ -288,11 +299,73 @@ Using Signac to cluster WCG/GCH bin count matrix by stage and celltype. :width: 50 % .. image:: ../_static/img/thumbnail/gchclusterbystage.png :width: 50 % +<<<<<<< HEAD + + +Using Signac to cluster WCG/GCH bin count matrix by celltype in E7.5. + + +.. code:: r + e75samplemeta = samplemeta[which(samplemeta$stage == "E7.5"),] + WCG_mat = WCG_mat[,e75samplemeta$sample] + e75WCG <- CreateSeuratObject( + counts = WCG_mat, + assay = "peaks", + min.cells = 3, + meta = e75samplemeta + ) + e75WCG@meta.data$celltype = e75samplemeta$celltype + + e75WCG <- RunTFIDF(e75WCG) + e75WCG <- FindTopFeatures(e75WCG, min.cutoff = "q90") + e75WCG <- RunSVD(e75WCG) + e75WCG <- RunUMAP( + object = e75WCG, + reduction = 'lsi', + dims = 2:20 + ) + + DimPlot(object = e75WCG, label = TRUE, reduction = "umap", group.by = "celltype") + +.. image:: ../_static/img/thumbnail/wcgclusterbycelltype.png + :width: 50% +.. image:: ../_static/img/thumbnail/gchclusterbycelltype.png + :width: 50% + +======= +>>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 Using Signac to cluster WCG/GCH bin count matrix by celltype. .. code:: r +<<<<<<< HEAD + + library(reticulate) + library(Seurat) + library(Signac) + library(dplyr) + library(ggplot2) + library(rhdf5) + library(Matrix) + np <- import("numpy") + scipy <-import("scipy") + h5py <- import("h5py") + + meta <- read.table("sample_metadata.txt",sep = "\t", header = T) + usecell <- read.table("usecells.txt") + samplemeta = merge(meta,usecell, by.x = "sample",by.y = "V1")[,c('sample','stage','lineage10x')] + rownames(samplemeta) = samplemeta$sample + samplemeta$celltype = samplemeta$lineage10x + + RNA_mat <- as.data.frame(read.table("RNA_normalized.txt",header = T,row.names = 1, check.names=FALSE)) + + wcgfeature = read.csv("WCG.bin.merge.peak",header = F) + mydata <- h5read("WCG.bin_peak.h5", "Mcsc") + WCG_mat = sparseMatrix(x = as.numeric(mydata$data),j = as.numeric(mydata$indices),p = as.numeric(mydata$indptr),dims = c(4114260,985),index1 = FALSE) + colnames(WCG_mat) = usecell$V1 + rownames(WCG_mat) = wcgfeature$V1 +======= e75samplemeta = samplemeta[which(samplemeta$stage == "E7.5"),] WCG_mat = WCG_mat[,e75samplemeta$sample] e75WCG <- CreateSeuratObject( @@ -318,8 +391,135 @@ Using Signac to cluster WCG/GCH bin count matrix by celltype. :width: 50% .. image:: ../_static/img/thumbnail/gchclusterbycelltype.png :width: 50% +>>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 + gchfeature = read.csv("GCH.bin.merge.peak",header = F) + mydata <- h5read("GCH.bin_peak.h5", "Mcsc") + GCH_mat = sparseMatrix(x = as.numeric(mydata$data),j = as.numeric(mydata$indices),p = as.numeric(mydata$indptr),dims = c(2645763,985),index1 = FALSE) + colnames(GCH_mat) = usecell$V1 + rownames(GCH_mat) = gchfeature$V1 +<<<<<<< HEAD + seurat <- CreateSeuratObject( + counts = RNA_mat, + project = "RNA", + assay = "RNA", + metadata = samplemeta, + min.cells = 5 + ) + seurat[['WCG']] <- CreateAssayObject(counts = WCG_mat,min.cells = 10) + seurat[['GCH']] <- CreateAssayObject(counts = GCH_mat,min.cells = 10) + seurat@meta.data <- cbind(seurat@meta.data,samplemeta) + + # RNA analysis + DefaultAssay(seurat) <- "RNA" + seurat <- SCTransform(seurat, verbose = FALSE) %>% RunPCA() %>% RunUMAP(dims = 1:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_') + + # WCG analysis + # We exclude the first dimension as this is typically correlated with sequencing depth + DefaultAssay(seurat) <- "WCG" + seurat <- RunTFIDF(seurat) + seurat <- FindTopFeatures(seurat, min.cutoff = 'q90') + seurat <- RunSVD(seurat) + seurat <- RunUMAP(seurat, reduction = 'lsi', dims = 2:50, reduction.name = "umap.wcg", reduction.key = "wcgUMAP_") + + # GCH analysis + # We exclude the first dimension as this is typically correlated with sequencing depth + DefaultAssay(seurat) <- "GCH" + seurat <- RunTFIDF(seurat) + seurat <- FindTopFeatures(seurat, min.cutoff = 'q90') + seurat <- RunSVD(seurat) + seurat <- RunUMAP(seurat, reduction = 'lsi', dims = 2:50, reduction.name = "umap.gch", reduction.key = "gchUMAP_") + + + DefaultAssay(seurat) <- "RNA" + seurat <- FindMultiModalNeighbors(seurat, reduction.list = list("pca", "lsi","lsi"), dims.list = list(1:50, 2:50, 2:50)) + seurat <- RunUMAP(seurat, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_") + seurat <- FindClusters(seurat, graph.name = "wsnn", algorithm = 3, verbose = FALSE) + + p1 <- DimPlot(seurat, reduction = "umap.rna", group.by = "stage", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("RNA") + p2 <- DimPlot(seurat, reduction = "umap.wcg", group.by = "stage", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("WCG") + p3 <- DimPlot(seurat, reduction = "umap.gch", group.by = "stage", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("GCH") + p4 <- DimPlot(seurat, reduction = "wnn.umap", group.by = "stage", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("WNN") + p1 + p2 + p3 +p4 & NoLegend() & theme(plot.title = element_text(hjust = 0.5)) + + +.. image:: ../_static/img/thumbnail/multiclusterbystage.png + :height: 350px + :align: center + + +.. code:: r + + E75meta <- samplemeta[samplemeta$stage == "E7.5" & samplemeta$celltype %in% c("Endoderm","Ectoderm","Mesoderm"),] + RNA_mat <- RNA_mat[,E75meta$sample] + seurat <- CreateSeuratObject( + counts = RNA_mat, + project = "RNA", + assay = "RNA", + metadata = E75meta, + min.cells = 3 + ) + seurat@meta.data <- cbind(seurat@meta.data,E75meta) + GCH_mat <- GCH_mat[,E75meta$sample] + seurat[['GCH']] <- CreateAssayObject(counts = GCH_mat,min.cells = 5) + WCG_mat <- WCG_mat[,E75meta$sample] + seurat[['WCG']] <- CreateAssayObject(counts = WCG_mat,min.cells = 5) + + # RNA analysis + DefaultAssay(seurat) <- "RNA" + seurat <- SCTransform(seurat, verbose = FALSE) %>% RunPCA() %>% RunUMAP(dims = 1:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_') + #Note that this single command replaces NormalizeData(), ScaleData(), and FindVariableFeatures() + # GCH analysis + # We exclude the first dimension as this is typically correlated with sequencing depth + DefaultAssay(seurat) <- "GCH" + seurat <- RunTFIDF(seurat) + seurat <- FindTopFeatures(seurat, min.cutoff = 'q90') + seurat <- RunSVD(seurat) + seurat <- RunUMAP(seurat, reduction = 'lsi', dims = 2:50, reduction.name = "umap.gch", reduction.key = "gchUMAP_") + + # RNA+GCH + DefaultAssay(seurat) <- "RNA" + seurat <- FindMultiModalNeighbors(seurat, reduction.list = list("pca", "lsi"), dims.list = list(1:50, 2:50),knn.range = 170,sd.scale = 1,k.nn = 30) + seurat <- RunUMAP(seurat, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_") + seurat <- FindClusters(seurat, graph.name = "wsnn", algorithm = 3, verbose = FALSE) + + pdf("GCH+RNA.pdf",width = 10,height = 5) + p1 <- DimPlot(seurat, reduction = "umap.rna", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("RNA") + p2 <- DimPlot(seurat, reduction = "umap.gch", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("GCH") + p3 <- DimPlot(seurat, reduction = "wnn.umap", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("WNN") + p1 + p2 +p3 & theme(plot.title = element_text(hjust = 0.5)) + dev.off() + + # RNA analysis + DefaultAssay(seurat) <- "RNA" + seurat <- SCTransform(seurat, verbose = FALSE) %>% RunPCA() %>% RunUMAP(dims = 1:50, reduction.name = 'umap.rna', reduction.key = 'rnaUMAP_') + # WCG analysis + # We exclude the first dimension as this is typically correlated with sequencing depth + DefaultAssay(seurat) <- "WCG" + seurat <- RunTFIDF(seurat) + seurat <- FindTopFeatures(seurat, min.cutoff = 'q90') + seurat <- RunSVD(seurat) + seurat <- RunUMAP(seurat, reduction = 'lsi', dims = 2:50, reduction.name = "umap.wcg", reduction.key = "wcgUMAP_") + + # RNA+WCG + DefaultAssay(seurat) <- "RNA" + seurat <- FindMultiModalNeighbors(seurat, reduction.list = list("pca", "lsi"), dims.list = list(1:50, 2:50),knn.range = 170,sd.scale = 1,k.nn = 30) + seurat <- RunUMAP(seurat, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_") + seurat <- FindClusters(seurat, graph.name = "wsnn", algorithm = 3, verbose = FALSE) + + pdf("WCG+RNA.pdf",width = 10,height = 5) + p1 <- DimPlot(seurat, reduction = "umap.rna", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("RNA") + p2 <- DimPlot(seurat, reduction = "umap.wcg", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("WCG") + p3 <- DimPlot(seurat, reduction = "wnn.umap", group.by = "celltype", label = TRUE, label.size = 2.5, pt.size = 2, repel = TRUE) + ggtitle("WNN") + p1 + p2 +p3 & theme(plot.title = element_text(hjust = 0.5)) + dev.off() + + +.. image:: ../_static/img/thumbnail/multiclusterbycelltype.png + :height: 350px + :align: center +======= Multi omics clustering >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> @@ -327,12 +527,110 @@ Multi omics clustering .. code:: r +>>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 Spatial multi-omics analysis >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> -.. code:: python +.. code:: r + library(reticulate) + library(data.table) + library(dplyr) + library(ggplot2) + library(Seurat) + library(Matrix) + library(Giotto) + library(Signac) + np <- import("numpy") + scipy <-import("scipy") + + temp_dir = getwd() + temp_dir = '~/Temp/' + myinstructions = createGiottoInstructions(save_dir = temp_dir, + save_plot = TRUE, + show_plot = FALSE) + + # initialize spatial expression matrix + expr = scipy$sparse$load_npz('../STRIDE/WCG_methy_mat.npz') + row = read.csv("../STRIDE/WCG_methy_mat_row.txt",header = FALSE) + spa_loc = read.table("Spa_location_Emb1z5.txt",sep = "\t") + rownames(expr) = row$V1 + colnames(expr) = rownames(spa_loc) + + signac <- CreateAssayObject(counts = expr) + signac <- RunTFIDF(signac) + signac <- FindTopFeatures(signac, min.cutoff = "q90") + rownames(expr) <- rownames(signac@data) + # giotto object + seqfish_mini <- createGiottoObject(raw_exprs = expr, + norm_expr = signac@data, + spatial_locs = spa_loc, + instructions = myinstructions,cores = 3) + + ## spatial grid + seqfish_mini <- createSpatialGrid(gobject = seqfish_mini, + sdimx_stepsize = 0.5, + sdimy_stepsize = 0.5,minimum_padding = 0.1) + + spatPlot(gobject = seqfish_mini, show_grid = T, point_size = 1.5, + save_param = c(save_name = '8_a_grid')) + + + ## delaunay network: stats + creation + plotStatDelaunayNetwork(gobject = seqfish_mini, maximum_distance = 400, save_plot = F) + seqfish_mini = createSpatialNetwork(gobject = seqfish_mini, minimum_k = 10, maximum_distance_delaunay = 400) + + + km_spatialgenes = binSpect(seqfish_mini, bin_method = 'rank', cores = 3) + spatGenePlot(seqfish_mini, expression_values = 'normalized', genes = km_spatialgenes[c(1:4),]$genes, + point_shape = 'border', point_border_stroke = 0.1, + show_network = F, network_color = 'lightgrey', point_size = 0.8, + cow_n_col = 2, + save_param = list(save_name = '10_a_spatialgenes_km')) + + + # 1. calculate spatial correlation scores + ext_spatial_genes = km_spatialgenes[1:2500]$genes + spat_cor_netw_DT = detectSpatialCorGenes(seqfish_mini, + method = 'network', + spatial_network_name = 'Delaunay_network', + subset_genes = ext_spatial_genes) + + # 2. cluster correlation scores + spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT, + name = 'spat_netw_clus', k = 2) + heatmSpatialCorGenes(seqfish_mini, spatCorObject = spat_cor_netw_DT, + use_clus_name = 'spat_netw_clus') + + + +.. image:: ../_static/img/thumbnail/binspectclustercorrelation.png + :height: 350px + :align: center + + +.. code::r + + netw_ranks = rankSpatialCorGroups(seqfish_mini, + spatCorObject = spat_cor_netw_DT, + use_clus_name = 'spat_netw_clus') + + cluster_genes_DT = showSpatialCorGenes(spat_cor_netw_DT, + use_clus_name = 'spat_netw_clus', + show_top_genes = 1) + cluster_genes = cluster_genes_DT$clus; names(cluster_genes) = cluster_genes_DT$gene_ID + + seqfish_mini = createMetagenes(seqfish_mini, gene_clusters = cluster_genes, name = 'cluster_metagene') + spatCellPlot(seqfish_mini, + spat_enr_names = 'cluster_metagene', + cell_annotation_values = netw_ranks$clusters, + point_size = 0.7, cow_n_col = 3) + + +.. image:: ../_static/img/thumbnail/epi_cluster.png + :height: 350px + :align: center From c2ffaa57f9106a1c60ba48a50569a976a91d1a80 Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:13:27 +0800 Subject: [PATCH 3/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 53 ++-------------------------------- 1 file changed, 2 insertions(+), 51 deletions(-) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index e18e78f..0b4c50e 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -240,20 +240,11 @@ Mouse embryo gene count matrix cluster by celltype in E7.5. :align: center -<<<<<<< HEAD Using Signac to cluster WCG/GCH bin count matrix by stage(from E4.5 to E7.5). Take WCG bin matrix for example. -======= -Using Signac to cluster WCG/GCH bin count matrix by stage. -Take WCG bin matrix for example. -======= -Using Signac to cluster WCG/GCH bin count matrix by stage and celltype. ->>>>>>> 86b422a47b1e01ce5968969e5e6d23b0a44b0b5e - ->>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 .. code:: r library(Signac) library(Seurat) @@ -299,7 +290,6 @@ Using Signac to cluster WCG/GCH bin count matrix by stage and celltype. :width: 50 % .. image:: ../_static/img/thumbnail/gchclusterbystage.png :width: 50 % -<<<<<<< HEAD Using Signac to cluster WCG/GCH bin count matrix by celltype in E7.5. @@ -332,14 +322,12 @@ Using Signac to cluster WCG/GCH bin count matrix by celltype in E7.5. .. image:: ../_static/img/thumbnail/gchclusterbycelltype.png :width: 50% -======= ->>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 -Using Signac to cluster WCG/GCH bin count matrix by celltype. +Multi omics clustering +>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> .. code:: r -<<<<<<< HEAD library(reticulate) library(Seurat) @@ -365,33 +353,6 @@ Using Signac to cluster WCG/GCH bin count matrix by celltype. WCG_mat = sparseMatrix(x = as.numeric(mydata$data),j = as.numeric(mydata$indices),p = as.numeric(mydata$indptr),dims = c(4114260,985),index1 = FALSE) colnames(WCG_mat) = usecell$V1 rownames(WCG_mat) = wcgfeature$V1 -======= - e75samplemeta = samplemeta[which(samplemeta$stage == "E7.5"),] - WCG_mat = WCG_mat[,e75samplemeta$sample] - e75WCG <- CreateSeuratObject( - counts = WCG_mat, - assay = "peaks", - min.cells = 3, - meta = e75samplemeta - ) - e75WCG@meta.data$celltype = e75samplemeta$celltype - - e75WCG <- RunTFIDF(e75WCG) - e75WCG <- FindTopFeatures(e75WCG, min.cutoff = "q90") - e75WCG <- RunSVD(e75WCG) - e75WCG <- RunUMAP( - object = e75WCG, - reduction = 'lsi', - dims = 2:20 - ) - - DimPlot(object = e75WCG, label = TRUE, reduction = "umap", group.by = "celltype") - -.. image:: ../_static/img/thumbnail/wcgclusterbycelltype.png - :width: 50% -.. image:: ../_static/img/thumbnail/gchclusterbycelltype.png - :width: 50% ->>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 gchfeature = read.csv("GCH.bin.merge.peak",header = F) mydata <- h5read("GCH.bin_peak.h5", "Mcsc") @@ -399,7 +360,6 @@ Using Signac to cluster WCG/GCH bin count matrix by celltype. colnames(GCH_mat) = usecell$V1 rownames(GCH_mat) = gchfeature$V1 -<<<<<<< HEAD seurat <- CreateSeuratObject( counts = RNA_mat, project = "RNA", @@ -519,15 +479,6 @@ Using Signac to cluster WCG/GCH bin count matrix by celltype. .. image:: ../_static/img/thumbnail/multiclusterbycelltype.png :height: 350px :align: center -======= - -Multi omics clustering ->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> - -.. code:: r - - ->>>>>>> bce01ef200fa7dfb5764846a79b76189127373d9 Spatial multi-omics analysis From e34334067800dc6d972e67dae2a5802bb2d75ff9 Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:14:46 +0800 Subject: [PATCH 4/9] Add files via upload --- docs/index.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index 1fe494b..3fcb018 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,7 +15,6 @@ Welcome to MultiSpace's documentation! .. |Docs| image:: https://readthedocs.org/projects/multispace/badge/?version=latest :target: https://multispace.readthedocs.io - image:: https://anaconda.org/changzhanhe/multispace/badges/version.svg .. |Conda| image:: https://anaconda.org/changzhanhe/multispace/badges/version.svg From 13c9fcc9f907e4d0caa1c694ccd7963a4c538973 Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:20:47 +0800 Subject: [PATCH 5/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index 0b4c50e..90596ef 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -240,12 +240,13 @@ Mouse embryo gene count matrix cluster by celltype in E7.5. :align: center -Using Signac to cluster WCG/GCH bin count matrix by stage(from E4.5 to E7.5). - +Using Signac to cluster WCG/GCH bin count matrix by stage(from E4.5 to E7.5). Take WCG bin matrix for example. + .. code:: r + library(Signac) library(Seurat) library(ggplot2) @@ -292,10 +293,12 @@ Take WCG bin matrix for example. :width: 50 % + Using Signac to cluster WCG/GCH bin count matrix by celltype in E7.5. .. code:: r + e75samplemeta = samplemeta[which(samplemeta$stage == "E7.5"),] WCG_mat = WCG_mat[,e75samplemeta$sample] e75WCG <- CreateSeuratObject( @@ -485,6 +488,7 @@ Spatial multi-omics analysis >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> .. code:: r + library(reticulate) library(data.table) library(dplyr) From 58d11f3444c94819ae33257cd17bd2c90fbb8fdb Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:22:01 +0800 Subject: [PATCH 6/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index 90596ef..f0c6125 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -560,11 +560,13 @@ Spatial multi-omics analysis + .. image:: ../_static/img/thumbnail/binspectclustercorrelation.png :height: 350px :align: center + .. code::r netw_ranks = rankSpatialCorGroups(seqfish_mini, @@ -583,6 +585,7 @@ Spatial multi-omics analysis point_size = 0.7, cow_n_col = 3) + .. image:: ../_static/img/thumbnail/epi_cluster.png :height: 350px :align: center From 0b7c00a5f6fda4a612f180ea60b5936dd1f89c7c Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:24:34 +0800 Subject: [PATCH 7/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index f0c6125..a12997b 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -566,7 +566,6 @@ Spatial multi-omics analysis :align: center - .. code::r netw_ranks = rankSpatialCorGroups(seqfish_mini, From 4bd1da29a42b16e1f3c4a35f116eabfc37472e9c Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:27:38 +0800 Subject: [PATCH 8/9] Add files via upload --- docs/examples/Mouse_embryo.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/examples/Mouse_embryo.rst b/docs/examples/Mouse_embryo.rst index a12997b..4eb8085 100644 --- a/docs/examples/Mouse_embryo.rst +++ b/docs/examples/Mouse_embryo.rst @@ -566,7 +566,8 @@ Spatial multi-omics analysis :align: center -.. code::r + +.. code:: r netw_ranks = rankSpatialCorGroups(seqfish_mini, spatCorObject = spat_cor_netw_DT, From 56830899634e96ed771948d5ae857f05ef43efa8 Mon Sep 17 00:00:00 2001 From: Sherry_Chang <41400407+Changzhanhe@users.noreply.github.com> Date: Mon, 4 Jul 2022 13:31:02 +0800 Subject: [PATCH 9/9] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 13148f5..9a6abcc 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,8 @@ [![Anaconda-Server Badge](https://anaconda.org/changzhanhe/multispace/badges/platforms.svg)](https://anaconda.org/changzhanhe/multispace) -MultiSpace(Single-cell Multi Omics Analysis In Space) is a multi-omics pipeline integrated RNA Expression, DNA methylation and Chromatin Accessibility analysis built using snakemake.MultiSpace support scCOOL-seq, scNMT-seq for DNA analysis; Smart-seq2 for RNA analysis. MultiSpace combines several tools and packages to create an integrative pipeline, which enables three omics anaylsis from raw sequencing data (fastq files as input) through alignment, quality control, cell filtering, methylation site calling and filtering, generate site by cell and bin by cell matrix in single cell level. Besides preprocessing, MultiSpace also provides several downstream analysis functions, including (1) gene genebody/promoter DNA methylation ratio, (2) using the regulatory potential model to calculate gene activity score, (3) mapping single cell to spatial location and get spatial epigenetic (DNA methylation/Chromatin Accessibility) signal. +MultiSpace(Single-cell Multi Omics Analysis In Space) is a multi-omics pipeline integrated RNA Expression, DNA methylation and Chromatin Accessibility analysis built using snakemake.MultiSpace support scCOOL-seq, scNMT-seq for DNA analysis; Smart-seq2 for RNA analysis. MultiSpace combines several tools and packages to create an integrative pipeline, which enables three omics anaylsis from raw sequencing data (fastq files as input) through alignment, quality control, cell filtering, methylation site calling and filtering, generate site by cell and bin by cell matrix in single cell level. +Besides preprocessing, MultiSpace also provides downstream analysis functions, including (1) calculate gene genebody/promoter DNA methylation ratio and gene activity score matrix, (2) mapping single cell to spatial location and get spatial epigenetic (DNA methylation/Chromatin Accessibility) signal. ![avatar](docs/_static/img/workflow.png)

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