From cec735cc254ead53e9e9a27991930c835e9785bb Mon Sep 17 00:00:00 2001 From: Xue Xiao <72620652+Heinyxiao@users.noreply.github.com> Date: Fri, 6 Sep 2024 13:05:38 -0400 Subject: [PATCH] Add files via upload --- AUCell.Rmd | 89 ++++ BCSCdb.Rmd | 60 +++ CellChat2.Rmd | 174 +++++++ MAGIC_OC.ipynb | 1295 ++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 1618 insertions(+) create mode 100644 AUCell.Rmd create mode 100644 BCSCdb.Rmd create mode 100644 CellChat2.Rmd create mode 100644 MAGIC_OC.ipynb diff --git a/AUCell.Rmd b/AUCell.Rmd new file mode 100644 index 0000000..7a1e603 --- /dev/null +++ b/AUCell.Rmd @@ -0,0 +1,89 @@ +--- +title: "AUCell" +author: "Xue Xiao" +date: "2024-06-20" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Install AUCell +```{r eval=FALSE, echo=FALSE, message=FALSE, warning=FALSE} +BiocManager::install("AUCell") +``` + +## Load Packages +```{r} +library(AUCell) +library(Matrix) +library(SummarizedExperiment) +library(Seurat) +``` + + +## Load Genesets and Datasets +```{r} +setwd("/Users/xuexiao/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/Dedifferentiation/Data") +CC1_markers <- read.csv("CC1_markers.csv", header = T, row.names = 1) +CC1_geneset <- CC1_markers$Genes + +CSC_markers_0.007 <- read.csv("filtered_markers_above_0_007.csv", header = T, row.names = 1) +CSC_geneset_0.007 <- CSC_markers_0.007$GENE + +CSC_markers_0.006 <- read.csv("filtered_markers_above_0_006.csv", header = T, row.names = 1) +CSC_geneset_0.006 <- CSC_markers_0.006$GENE + +CSC_markers_0.005 <- read.csv("filtered_markers_above_0_005.csv", header = T, row.names = 1) +CSC_geneset_0.005 <- CSC_markers_0.005$GENE + +gene_sets <- list( + CC1 = CC1_geneset, + CSC_0.007 = CSC_geneset_0.007, + CSC_0.006 = CSC_geneset_0.006, + CSC_0.005 = CSC_geneset_0.005 +) +common_genes <- intersect(CC1_geneset, CSC_geneset_0.005) +``` + + +```{r} +# Open Seurat File +seurat_obj <- readRDS("~/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/Dedifferentiation/Data/Cancer_cell_in_house_magic_cytotrace.rds") + +# Extract the expression matrix +expr_matrix <- GetAssayData(seurat_obj, layer = "counts") + +``` + +## Run AUCell +```{r} +# Build the rankings +cells_rankings <- AUCell_buildRankings(expr_matrix, nCores = 1, plotStats = TRUE) + +# Calculate the AUCell scores +cells_AUC <- AUCell_calcAUC(gene_sets, cells_rankings) + +# Add AUCell scores to Seurat metadata +aucell_scores <- as.data.frame(t(as.data.frame(cells_AUC@assays@data$AUC))) +colnames(aucell_scores) <- paste0("AUCell_", colnames(aucell_scores)) +seurat_obj <- AddMetaData(seurat_obj, metadata = aucell_scores) + +# View the Seurat object metadata to check if scores were added +head(seurat_obj@meta.data) + +``` + +## Visualization +```{r inline_plot, fig.width=7, fig.height=5} + +# Define custom color palette +custom_colors <- scale_color_gradientn(colors = c("gray", "yellow", "red")) + +# Visualize the AUCell scores with custom color scheme +FeaturePlot(seurat_obj, features = colnames(aucell_scores), pt.size = 0.2, label = T) & custom_colors + +``` + + diff --git a/BCSCdb.Rmd b/BCSCdb.Rmd new file mode 100644 index 0000000..3321e9a --- /dev/null +++ b/BCSCdb.Rmd @@ -0,0 +1,60 @@ +--- +title: "BCSCdb" +author: "Xue Xiao" +date: "2024-06-18" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Load packages +```{r} +library(ggplot2) +``` + + +## Load all CSC markers +```{r} +setwd("/Users/xuexiao/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/Dedifferentiation/Data") +all_CSC_markers <- read.csv("CSC_Biomarker_2022_All.csv", header = F) +head(all_CSC_markers) +length(unique(all_CSC_markers$V1)) +``` + +## Filter top 100 markers +```{r} +## Unique markers +unique_CSC_markers <- all_CSC_markers[!duplicated(all_CSC_markers$V1), ] + +## Sort the CSC markers in descending order of global score +sorted_CSC_markers <- unique_CSC_markers[order(-as.numeric(unique_CSC_markers$V11)), ] + +## Check distribution of global score +ggplot(unique_CSC_markers, aes(x = V11)) + + geom_histogram(binwidth = 0.05, fill = "blue", color = "black", alpha = 0.7) + + scale_x_continuous(limits = c(-0.1, 1), breaks = seq(-1, 1, by = 0.05)) + + labs(title = "Distribution of Global Scores", + x = "Global Score", + y = "Frequency") + + theme_minimal() + + stat_bin(binwidth = 0.05, geom = "text", aes(label = ..count..), vjust = -0.5, color = "black") +## Select the top 100 markers according to global score +table(unique_CSC_markers$V11) +``` + + +```{r} +sum(unique_CSC_markers$V11 > 0.007, na.rm = TRUE) # 105 genes +sum(unique_CSC_markers$V11 > 0.006, na.rm = TRUE) # 158 genes +sum(unique_CSC_markers$V11 > 0.005, na.rm = TRUE) # 269 genes +``` +## Filter genes with global score +```{r} +filtered_markers <- unique_CSC_markers[unique_CSC_markers$V11 > 0.005, ] +unique_gene_names <- as.list(filtered_markers$V1) +colnames(filtered_markers) <- c("GENE", "MARKER_TYPE", "EXPRESSION_LEVEL", "HGNC_ID", "CANCER_TYPE", "HISTOLOGICAL_TYPE", "CELL_LINE", "CSC_ENRICHMENT", "METHOD", "CONFIDENCE_SCORING", "GLOBAL_SCORING", "PUBMED_ID") +write.csv(filtered_markers, "/Users/xuexiao/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/Dedifferentiation/Data/filtered_markers_above_0_005.csv") +``` + diff --git a/CellChat2.Rmd b/CellChat2.Rmd new file mode 100644 index 0000000..d581884 --- /dev/null +++ b/CellChat2.Rmd @@ -0,0 +1,174 @@ +--- +title: "OC_CAF_Crosstalk" +output: html_document +date: "2024-05-21" +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Package Installation +```{r, eval=FALSE} +# install.packages("devtools") +devtools::install_github("immunogenomics/presto") +devtools::install_github("jinworks/cellchat") +``` + +## Load Packages +```{r} +library(Seurat) +library(cellchat) +library(ggplot2) +library(ggplotify) +``` + + +## Load Data +```{r} +load("~/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/OC_CAF_Crosstalk/GSE165897/Secreted_Signaling_GSE165897_Object.RData") +load("~/Desktop/Lab/Projects/OC_CAF_Crosstalk/Secreted_Signalingin_house_Object.RData") +load("~/Desktop/Lab/Projects/OC_CAF_Crosstalk/Secreted_Signaling_Object.RData") +cellchat@meta$labels[cellchat@meta$labels == "Epithelial_cells"] <- "OC_cells" +cellchat@meta$labels[cellchat@meta$labels == "Smooth_muscle_cells"] <- "CAFs" +table(cellchat@idents) +cellchat <- setIdent(cellchat, ident.use = "labels") +``` + +## Create cellchat Object +```{r} +data.input <- in_house_seurat[["RNA"]]$data # normalized data matrix +# For Seurat version >= “5.0.0”, get the normalized data via `seurat_object[["RNA"]]$data` +Idents(in_house_seurat) <- "subcluster" +labels <- Idents(in_house_seurat) +colnames(in_house_seurat@meta.data) +meta <- data.frame(labels = labels, row.names = names(labels)) # create a dataframe of the cell labels + +cellchat <- createcellchat(object = in_house_seurat, group.by = "subcluster", assay = "RNA") + +``` +## Set the ligand-receptor interaction database +```{r} +cellchatDB <- cellchatDB.human +cellchatDB.use <- subsetDB(cellchatDB, search = "Secreted Signaling", key = "annotation") +cellchat@DB <- cellchatDB.use +cellchat <- subsetData(cellchat) +``` + +## Run cellchat +```{r} +ptm = Sys.time() +future::plan("multisession", workers = 8) +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +execution.time = Sys.time() - ptm +print(as.numeric(execution.time, units = "secs")) +cellchat <- computeCommunProb(cellchat, type = "triMean") +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +``` + +```{r} +cellchat <- aggregateNet(cellchat) +execution.time = Sys.time() - ptm +print(as.numeric(execution.time, units = "secs")) +``` + + +## Visualization +```{r} +# Aggregated Cell-Cell Communication Network (Total Interactions) +ptm = Sys.time() +groupSize <- as.numeric(table(cellchat@idents)) +par(mfrow = c(1,2), xpd=TRUE) +netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") +netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength") + +plot1 <- as.ggplot(~netVisual_circle(cellchat@net$count, vertex.weight = groupSize, + weight.scale = TRUE, label.edge = FALSE, + title.name = "Number of interactions")) +# Save the first plot +ggsave("Number_of_interactions.pdf", plot = plot1, width = 6, height = 6) + +# Convert the second plot to a ggplot object +plot2 <- as.ggplot(~netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, + weight.scale = TRUE, label.edge = FALSE, + title.name = "Interaction weights/strength")) +# Save the second plot +ggsave("Interaction_weights_strength.pdf", plot = plot2, width = 6, height = 6) +getwd() +setwd("/Users/xuexiao/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Figure/Data") +``` + + +```{r} +# Network Centrality Scores +cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") +netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 13, height = 5, font.size = 10) +``` +```{r} +# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways +ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing") +ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming") +ht1 + ht2 +netAnalysis_signalingRole_heatmap(cellchat, signaling = c("PDGF", "ncWNT"), width = 8, height = 5, font.size = 10) +ht +``` +```{r} +# show all the significant signaling pathways from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use') +netVisual_chord_gene(cellchat, sources.use = c(1:3), targets.use = c(4:13), slot.name = "netP", legend.pos.x = 10, small.gap = 0.2, lab.cex = 0.5) +``` + + +```{r} +pathways.show <- c("PDGF") +``` + +### Circle plot +```{r} +par(mfrow=c(1,1)) +netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle") +``` +### Chord diagram +```{r} +par(mfrow=c(1,1)) +netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord") +``` +### Heatmap +```{r} +par(mfrow=c(1,1)) +netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds") +``` +### Contribution +```{r} +netAnalysis_contribution(cellchat, signaling = pathways.show, font.size = 10, width = 20) + +``` +### Single L-R pair +```{r} +pairLR.PDGF <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE) +LR.show <- pairLR.PDGF[4,] # show one ligand-receptor pair +``` + +```{r} +# Chord plot +netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord") +# Circle plot +#netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle") +``` +```{r} +netVisual_bubble(cellchat, sources.use = c(4:13), targets.use = c(1:3), signaling = c("PDGF"), remove.isolate = FALSE, sort.by.target = TRUE) +netVisual_bubble(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("PDGF"), remove.isolate = FALSE) +netVisual_bubble(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("ncWNT"), remove.isolate = FALSE) +``` +```{r} +netVisual_chord_gene(cellchat, sources.use = c(4:13), targets.use = c(1:3), signaling = c("PDGF"),legend.pos.x = 8) +netVisual_chord_gene(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("ncWNT"),legend.pos.x = 8, small.gap = 0.1) + +``` + +## Save CellChat object +```{r} +saveRDS(cellChat, file = "~/Library/CloudStorage/GoogleDrive-heinyxiao@gmail.com/My Drive/Lab/Projects/OC_CAF_Crosstalk/Imputed_in_house/cellchat_in_house.rds") +``` + diff --git a/MAGIC_OC.ipynb b/MAGIC_OC.ipynb new file mode 100644 index 0000000..9152bf6 --- /dev/null +++ b/MAGIC_OC.ipynb @@ -0,0 +1,1295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cbf4bb1b-046e-4e72-b71b-3d3c2d3605a8", + "metadata": {}, + "source": [ + "## MAGIC Imputation of ovarian cancer cells" + ] + }, + { + "cell_type": "markdown", + "id": "0caa3e47-ce4e-4a2f-a33f-461c42a1975a", + "metadata": {}, + "source": [ + "MAGIC first learns the data's underlying structure and then smooths gene expression values over this structure using a cell-cell affinity graph to construct a Markov diffusion operator. This operator is used to diffuse data between cells, filling in missing values and denoising the data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4301bf07-0b11-494a-bfed-bf5f8b62af95", + "metadata": {}, + "outputs": [], + "source": [ + "# Load packages\n", + "import magic\n", + "import scprep\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Matplotlib command for Jupyter notebooks only\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e9205d2b-6bcc-48c9-9ff7-1d9f54e8d4c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + " \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", + " \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", + "
MIR1302-2HGFAM138AOR4F5AL627309.1AL627309.3AL627309.2AL627309.5AL627309.4AP006222.2AL732372.1...AC133551.1AC136612.1AC136616.1AC136616.3AC136616.2AC141272.1AC023491.2AC007325.1AC007325.4AC007325.2
AS_AAACCCAAGCGTTAGG-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACCCACAAACCACT-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACCCACAACGTTAC-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACCCAGTCCGGTGT-10.00.00.00.00.00.01.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACGAAAGTGGACTG-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACGAACAATTCTTC-10.00.00.00.00.00.01.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACGAAGTAGGAGGG-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACGAATCTATGCCC-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAACGCTTCTGATGGT-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
AS_AAAGAACGTCGAGTTT-10.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
\n", + "

10 rows × 36601 columns

\n", + "
" + ], + "text/plain": [ + " MIR1302-2HG FAM138A OR4F5 AL627309.1 AL627309.3 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " AL627309.2 AL627309.5 AL627309.4 AP006222.2 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 1.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 1.0 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 \n", + "\n", + " AL732372.1 ... AC133551.1 AC136612.1 AC136616.1 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 ... 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 ... 0.0 0.0 0.0 \n", + "\n", + " AC136616.3 AC136616.2 AC141272.1 AC023491.2 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 \n", + "\n", + " AC007325.1 AC007325.4 AC007325.2 \n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 \n", + "\n", + "[10 rows x 36601 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load data - expression matrix\n", + "sc_data = scprep.io.load_csv(\"/Users/xuexiao/Desktop/Lab/Projects/Dedifferentiation/Data/in_house_oc_data.csv\", cell_axis= 'column')\n", + "\n", + "# Check data (first 10 rows)\n", + "sc_data.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "ea65f9ae-3844-4968-95fa-8713550ba91b", + "metadata": {}, + "source": [ + "After loading your data, you're going to want to determine the molecule per cell and molecule per gene cutoffs with which to filter the data, in order to remove lowly expressed genes and cells with a small library size." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4f1f2d21-22b0-4d02-a7f8-3adf4ed192a2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/xuexiao/.local/lib/python3.10/site-packages/scprep/plot/utils.py:104: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + " \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", + " \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", + "
AL627309.1AL627309.5LINC01409LINC01128LINC00115FAM41CLINC02593SAMD11NOC2LKLHL17...MT-ND6MT-CYBBX004987.1AC145212.1MAFIPAC011043.1AL354822.1AL592183.1AC240274.1AC007325.4
AS_AAACCCAAGCGTTAGG-10.00.00.00.00.00.00.00.00.00.0...0.08.00.00.00.00.00.00.00.00.0
AS_AAACCCACAAACCACT-10.00.01.00.00.00.00.00.01.00.0...4.0137.00.00.00.00.00.00.00.00.0
AS_AAACCCACAACGTTAC-10.00.01.00.00.01.00.00.00.00.0...7.051.01.00.00.00.00.00.00.00.0
AS_AAACCCAGTCCGGTGT-10.01.00.00.00.00.00.00.02.00.0...5.0108.00.00.00.00.00.00.00.00.0
AS_AAACGAAAGTGGACTG-10.00.00.00.00.00.00.00.03.00.0...1.0136.00.00.00.00.00.01.00.00.0
AS_AAACGAACAATTCTTC-10.01.01.01.00.01.00.00.02.00.0...9.0417.00.01.01.00.00.04.00.00.0
AS_AAACGAAGTAGGAGGG-10.00.00.00.00.00.00.01.00.00.0...0.050.00.00.00.00.00.00.00.00.0
AS_AAACGAATCTATGCCC-10.00.00.00.00.00.00.00.00.00.0...5.0163.00.00.00.00.00.00.01.00.0
AS_AAACGCTTCTGATGGT-10.00.00.00.00.00.00.00.01.00.0...1.063.00.00.00.00.00.01.00.00.0
AS_AAAGAACGTCGAGTTT-10.00.00.00.00.00.00.00.01.00.0...0.03.00.00.00.00.00.00.00.00.0
\n", + "

10 rows × 20555 columns

\n", + "
" + ], + "text/plain": [ + " AL627309.1 AL627309.5 LINC01409 LINC01128 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 1.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 1.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 1.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 1.0 1.0 1.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 \n", + "\n", + " LINC00115 FAM41C LINC02593 SAMD11 NOC2L KLHL17 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 0.0 1.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 1.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.0 0.0 0.0 2.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 0.0 0.0 3.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 1.0 0.0 0.0 2.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 1.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 0.0 0.0 1.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 1.0 0.0 \n", + "\n", + " ... MT-ND6 MT-CYB BX004987.1 AC145212.1 MAFIP \\\n", + "AS_AAACCCAAGCGTTAGG-1 ... 0.0 8.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 ... 4.0 137.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 ... 7.0 51.0 1.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 ... 5.0 108.0 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 ... 1.0 136.0 0.0 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 ... 9.0 417.0 0.0 1.0 1.0 \n", + "AS_AAACGAAGTAGGAGGG-1 ... 0.0 50.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 ... 5.0 163.0 0.0 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 ... 1.0 63.0 0.0 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 ... 0.0 3.0 0.0 0.0 0.0 \n", + "\n", + " AC011043.1 AL354822.1 AL592183.1 AC240274.1 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.0 1.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.0 4.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.0 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.0 0.0 1.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.0 1.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.0 0.0 0.0 \n", + "\n", + " AC007325.4 \n", + "AS_AAACCCAAGCGTTAGG-1 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 \n", + "\n", + "[10 rows x 20555 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter data\n", + "scprep.plot.plot_library_size(sc_data, cutoff = 1500)\n", + "sc_data = scprep.filter.filter_library_size(sc_data, cutoff=1500)\n", + "sc_data = scprep.filter.filter_rare_genes(sc_data, min_cells=10)\n", + "sc_data.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ae5e56de-3ceb-41e4-acde-a570ac541794", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + " \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", + " \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", + "
AL627309.1AL627309.5LINC01409LINC01128LINC00115FAM41CLINC02593SAMD11NOC2LKLHL17...MT-ND6MT-CYBBX004987.1AC145212.1MAFIPAC011043.1AL354822.1AL592183.1AC240274.1AC007325.4
AS_AAACCCAAGCGTTAGG-10.00.0000000.0000000.0000000.00.0000000.00.0000000.0000000.0...0.0000003.6898110.0000000.0000000.0000000.00.00.0000000.0000000.0
AS_AAACCCACAAACCACT-10.00.0000000.5281620.0000000.00.0000000.00.0000000.5281620.0...1.0563256.1819830.0000000.0000000.0000000.00.00.0000000.0000000.0
AS_AAACCCACAACGTTAC-10.00.0000000.5810620.0000000.00.5810620.00.0000000.0000000.0...1.5373444.1496090.5810620.0000000.0000000.00.00.0000000.0000000.0
AS_AAACCCAGTCCGGTGT-10.00.4929280.0000000.0000000.00.0000000.00.0000000.6971050.0...1.1022205.1226570.0000000.0000000.0000000.00.00.0000000.0000000.0
AS_AAACGAAAGTGGACTG-10.00.0000000.0000000.0000000.00.0000000.00.0000000.8465760.0...0.4887715.7000020.0000000.0000000.0000000.00.00.4887710.0000000.0
AS_AAACGAACAATTCTTC-10.00.3080410.3080410.3080410.00.3080410.00.0000000.4356360.0...0.9241236.2903750.0000000.3080410.3080410.00.00.6160820.0000000.0
AS_AAACGAAGTAGGAGGG-10.00.0000000.0000000.0000000.00.0000000.00.7205070.0000000.0...0.0000005.0947520.0000000.0000000.0000000.00.00.0000000.0000000.0
AS_AAACGAATCTATGCCC-10.00.0000000.0000000.0000000.00.0000000.00.0000000.0000000.0...1.0134055.7861780.0000000.0000000.0000000.00.00.0000000.4532080.0
AS_AAACGCTTCTGATGGT-10.00.0000000.0000000.0000000.00.0000000.00.0000000.5972840.0...0.5972844.7407930.0000000.0000000.0000000.00.00.5972840.0000000.0
AS_AAAGAACGTCGAGTTT-10.00.0000000.0000000.0000000.00.0000000.00.0000000.8511330.0...0.0000001.4742060.0000000.0000000.0000000.00.00.0000000.0000000.0
\n", + "

10 rows × 20555 columns

\n", + "
" + ], + "text/plain": [ + " AL627309.1 AL627309.5 LINC01409 LINC01128 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.000000 0.000000 0.000000 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.000000 0.528162 0.000000 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.000000 0.581062 0.000000 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.492928 0.000000 0.000000 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.000000 0.000000 0.000000 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.308041 0.308041 0.308041 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.000000 0.000000 0.000000 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.000000 0.000000 0.000000 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.000000 0.000000 0.000000 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.000000 0.000000 0.000000 \n", + "\n", + " LINC00115 FAM41C LINC02593 SAMD11 NOC2L \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 0.000000 0.0 0.000000 0.000000 \n", + "AS_AAACCCACAAACCACT-1 0.0 0.000000 0.0 0.000000 0.528162 \n", + "AS_AAACCCACAACGTTAC-1 0.0 0.581062 0.0 0.000000 0.000000 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 0.000000 0.0 0.000000 0.697105 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 0.000000 0.0 0.000000 0.846576 \n", + "AS_AAACGAACAATTCTTC-1 0.0 0.308041 0.0 0.000000 0.435636 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 0.000000 0.0 0.720507 0.000000 \n", + "AS_AAACGAATCTATGCCC-1 0.0 0.000000 0.0 0.000000 0.000000 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 0.000000 0.0 0.000000 0.597284 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 0.000000 0.0 0.000000 0.851133 \n", + "\n", + " KLHL17 ... MT-ND6 MT-CYB BX004987.1 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.0 ... 0.000000 3.689811 0.000000 \n", + "AS_AAACCCACAAACCACT-1 0.0 ... 1.056325 6.181983 0.000000 \n", + "AS_AAACCCACAACGTTAC-1 0.0 ... 1.537344 4.149609 0.581062 \n", + "AS_AAACCCAGTCCGGTGT-1 0.0 ... 1.102220 5.122657 0.000000 \n", + "AS_AAACGAAAGTGGACTG-1 0.0 ... 0.488771 5.700002 0.000000 \n", + "AS_AAACGAACAATTCTTC-1 0.0 ... 0.924123 6.290375 0.000000 \n", + "AS_AAACGAAGTAGGAGGG-1 0.0 ... 0.000000 5.094752 0.000000 \n", + "AS_AAACGAATCTATGCCC-1 0.0 ... 1.013405 5.786178 0.000000 \n", + "AS_AAACGCTTCTGATGGT-1 0.0 ... 0.597284 4.740793 0.000000 \n", + "AS_AAAGAACGTCGAGTTT-1 0.0 ... 0.000000 1.474206 0.000000 \n", + "\n", + " AC145212.1 MAFIP AC011043.1 AL354822.1 \\\n", + "AS_AAACCCAAGCGTTAGG-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.308041 0.308041 0.0 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.000000 0.000000 0.0 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.000000 0.000000 0.0 0.0 \n", + "\n", + " AL592183.1 AC240274.1 AC007325.4 \n", + "AS_AAACCCAAGCGTTAGG-1 0.000000 0.000000 0.0 \n", + "AS_AAACCCACAAACCACT-1 0.000000 0.000000 0.0 \n", + "AS_AAACCCACAACGTTAC-1 0.000000 0.000000 0.0 \n", + "AS_AAACCCAGTCCGGTGT-1 0.000000 0.000000 0.0 \n", + "AS_AAACGAAAGTGGACTG-1 0.488771 0.000000 0.0 \n", + "AS_AAACGAACAATTCTTC-1 0.616082 0.000000 0.0 \n", + "AS_AAACGAAGTAGGAGGG-1 0.000000 0.000000 0.0 \n", + "AS_AAACGAATCTATGCCC-1 0.000000 0.453208 0.0 \n", + "AS_AAACGCTTCTGATGGT-1 0.597284 0.000000 0.0 \n", + "AS_AAAGAACGTCGAGTTT-1 0.000000 0.000000 0.0 \n", + "\n", + "[10 rows x 20555 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalization\n", + "sc_data = scprep.normalize.library_size_normalize(sc_data)\n", + "sc_data = scprep.transform.sqrt(sc_data)\n", + "sc_data.head(10)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2aad7866-f478-4689-ac44-287d6adfa2c0", + "metadata": {}, + "source": [ + "Default setting: MAGIC creates an operator with the following default values: knn=5, knn_max = 3 * knn, decay=1, t=3." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "3f664a63-2962-45ab-bfbd-3e7b4628d14f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating MAGIC...\n", + " Running MAGIC on 3576 cells and 20555 genes.\n", + " Calculating graph and diffusion operator...\n", + " Calculating PCA...\n", + " Calculated PCA in 12.43 seconds.\n", + " Calculating KNN search...\n", + " Calculated KNN search in 0.71 seconds.\n", + " Calculating affinities...\n", + " Calculated affinities in 0.63 seconds.\n", + " Calculated graph and diffusion operator in 13.83 seconds.\n", + " Running MAGIC with `solver='exact'` on 20555-dimensional data may take a long time. Consider denoising specific genes with `genes=` or using `solver='approximate'`.\n", + " Calculating imputation...\n", + " Calculated imputation in 3.31 seconds.\n", + "Calculated MAGIC in 17.26 seconds.\n" + ] + } + ], + "source": [ + "# Run MAGIC\n", + "magic_operator = magic.MAGIC()\n", + "sc_magic = magic_operator.fit_transform(sc_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "00b82a1e-694c-40ec-94e6-90ec969d5dc4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16, 6))\n", + "\n", + "scprep.plot.scatter(x=sc_data['ROR2'], y=sc_data['ALDH1A1'], c=sc_data['CREB1'], ax=ax1,\n", + " xlabel='ROR2', ylabel='ALDH1A1', legend_title=\"CREB1\", title='Before MAGIC')\n", + "\n", + "scprep.plot.scatter(x=sc_magic['ROR2'], y=sc_magic['ALDH1A1'], c=sc_magic['CREB1'], ax=ax2,\n", + " xlabel='ROR2', ylabel='ALDH1A1', legend_title=\"CREB1\", title='After MAGIC')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "268a2264-3ce4-49cc-94cc-d5972314271d", + "metadata": {}, + "outputs": [], + "source": [ + "# Save imputed data\n", + "sc_magic.to_csv(\"/Users/xuexiao/Desktop/Lab/Projects/Dedifferentiation/Data/in_house_oc_magic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff979669-1a60-41ce-b9e9-1a0a66f38c41", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}