Minor changes

Cell_BLAST/__init__.py

@@ -30,4 +30,4 @@
  "config"
 ]
 
-__version__ = "0.1.1"
+__version__ = "0.1.2"

Cell_BLAST/data.py

@@ -294,6 +294,16 @@ def __getitem__(self, slices):
  uns=copy.deepcopy(dict(self.uns))
  )
 
+ def clean_duplicate_vars(self):
+ unique_vars, duplicate_mask = \
+ set(), np.ones(self.var_names.size).astype(np.bool_)
+ for idx, item in enumerate(self.var_names):
+ if item in unique_vars:
+ duplicate_mask[idx] = False
+ else:
+ unique_vars.add(item)
+ return self[:, duplicate_mask]
+
  def get_meta_or_var(self, names, normalize_var=False, log_var=False):
  """
  Get either meta information (column names in ``obs``) or
@@ -796,7 +806,7 @@ def obs_correlation_heatmap(
 
  def violin(
  self, group, var, normalize_var=True, width=7, height=7,
- ax=None, **kwargs
+ ax=None, strip_kws=None, violin_kws=None
  ):
  """
  Violin plot across obs groups.
@@ -816,7 +826,10 @@ def violin(
  ax : matplotlib.axes.Axes
  Specify an existing axes to plot onto, by default None.
  If specified, ``width`` and ``height`` take no effect.
- **kwargs
+ strip_kws : dict
+ Additional keyword arguments will be passed to
+ ``seaborn.stripplot``.
+ violin_kws : dict
  Additional keyword arguments will be passed to
  ``seaborn.violinplot``.
 
@@ -828,15 +841,22 @@ def violin(
  import matplotlib.pyplot as plt
  import seaborn as sns
 
+ strip_kws = {} if strip_kws is None else strip_kws
+ violin_kws = {} if violin_kws is None else violin_kws
+
  df = self.get_meta_or_var(
  [group, var],
  normalize_var=normalize_var, log_var=True
  )
  if ax is None:
  _, ax = plt.subplots(figsize=(width, height))
+ ax = sns.stripplot(
+ x=group, y=var, data=df,
+ color=".3", edgecolor=None, size=3, ax=ax, **strip_kws
+ )
  ax = sns.violinplot(
  x=group, y=var, data=df,
- scale="width", ax=ax, inner="point", **kwargs
+ scale="width", ax=ax, inner=None, **violin_kws
  )
  ax.spines["right"].set_visible(False)
  ax.spines["top"].set_visible(False)

Datasets/ACA_datasets.csv

@@ -10,9 +10,9 @@ Dahlin_10x,Mus musculus,Bone Marrow,NA,10x,46447,A single-cell hematopoietic lan
 Dahlin_mutant,Mus musculus,Bone Marrow,NA,10x,14675,A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice,TRUE,c-Kit mutant,collect_dahlin.R
 Quake_10x_Bone_Marrow,Mus musculus,Bone Marrow,,10x,3652,Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris,TRUE,,collect_quake_10x.R
 Quake_Smart-seq2_Bone_Marrow,Mus musculus,Bone Marrow,,Smart-seq2,5037,Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris,TRUE,,collect_quake_smartseq2.R
-Tusi,Mus musculus,Bone Marrow,adult,inDrop,4763,Population snapshots predict early haematopoietic and erythroid hierarchies,FALSE,Continuous,collect_tusi.R
-Velten_QUARTZ-seq,Homo sapiens,Bone Marrow,29-year-old,QUARTZ-seq,379,Human haematopoietic stem cell lineage commitment is a continuous process,FALSE,"no meta, continuous",collect_velten.R
-Velten_Smart-seq2,Homo sapiens,Bone Marrow,25-year-old,Smart-seq2,1035,Human haematopoietic stem cell lineage commitment is a continuous process,FALSE,"no meta, continuous",collect_velten.R
+Tusi,Mus musculus,Bone Marrow,adult,inDrop,4763,Population snapshots predict early haematopoietic and erythroid hierarchies,TRUE,Continuous,collect_tusi.R
+Velten_QUARTZ-seq,Homo sapiens,Bone Marrow,29-year-old,QUARTZ-seq,379,Human haematopoietic stem cell lineage commitment is a continuous process,TRUE,"no meta, continuous",collect_velten.R
+Velten_Smart-seq2,Homo sapiens,Bone Marrow,25-year-old,Smart-seq2,1035,Human haematopoietic stem cell lineage commitment is a continuous process,TRUE,"no meta, continuous",collect_velten.R
 Campbell,Mus musculus,Brain,,Drop-seq,20921,A molecular census of arcuate hypothalamus and median eminence cell types,TRUE,Adult Arc-ME complex,collect_campbell.R
 Chen,Mus musculus,Brain,,Drop-seq,12089,Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity,TRUE,Adult hypothalamus,collect_chen.R
 Lake_2018,Homo sapiens,Brain,,snDrop-seq,35289,Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain,TRUE,,collect_lake_2018.R
@@ -85,4 +85,4 @@ Montoro_10x,Mus musculus,Trachea,adult,10x,7193,A revised airway epithelial hier
 Montoro_Smart-seq2,Mus musculus,Trachea,adult,modified Smart-seq2,301,A revised airway epithelial hierarchy includes CFTR-expressing ionocytes,TRUE,3 WT,collect_montoro_smartseq2.R
 Plasschaert,Mus musculus,Trachea,adult,inDrop,6977,A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte,TRUE,4 WT,collect_plasschaert.R
 Quake_10x_Trachea,Mus musculus,Trachea,,10x,11269,Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris,TRUE,,collect_quake_10x.R
-Quake_Smart-seq2_Trachea,Mus musculus,Trachea,,Smart-seq2,1350,Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris,TRUE,,collect_quake_smartseq2.R
+Quake_Smart-seq2_Trachea,Mus musculus,Trachea,,Smart-seq2,1350,Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris,TRUE,,collect_quake_smartseq2.R

Datasets/aligned_datasets.csv

@@ -15,7 +15,8 @@ ALIGNED_Mus_musculus_Pancreas,Mus musculus,Pancreas,,,,,,"Baron_mouse, Quake_Sma
 ALIGNED_Mus_musculus_Retina,Mus musculus,Retina,,,,,,"Macosko, Shekhar"
 ALIGNED_Mus_musculus_Small_Intestine,Mus musculus,Small Intestine,,,,,,"Haber_10x, Haber_10x_largecell, Haber_10x_region, Haber_10x_FAE, Haber_Smart-seq2"
 ALIGNED_Mus_musculus_Spleen,Mus musculus,Spleen,,,,,,"Quake_10x_Spleen, Quake_Smart-seq2_Spleen"
+ALIGNED_Mus_musculus_Heart_and_Aorta,Mus musculus,"Heart, Aorta",,,,,,"Quake_10x_Heart_and_Aorta, Quake_Smart-seq2_Heart"
 ALIGNED_Mus_musculus_Thymus,Mus musculus,Thymus,,,,,,"Quake_10x_Thymus, Quake_Smart-seq2_Thymus"
 ALIGNED_Mus_musculus_Tongue,Mus musculus,Tongue,,,,,,"Quake_10x_Tongue, Quake_Smart-seq2_Tongue"
 ALIGNED_Mus_musculus_Trachea,Mus musculus,Trachea,,,,,,"Quake_10x_Trachea, Quake_Smart-seq2_Trachea"
-ALIGNED_Tabula_Muris,Mus musculus,Trachea,,,,,,"Quake_10x, Quake_Smart-seq2"
+ALIGNED_Tabula_Muris,Mus musculus,Atlas,,,,,,"Quake_10x, Quake_Smart-seq2"

Datasets/collect/collect_ariss.R

@@ -0,0 +1,70 @@
+#! /usr/bin/env Rscript
+# by wangshuai
+# 11 Mar 2019
+# 14:36:35 PM
+
+suppressPackageStartupMessages({
+ library(Seurat)
+})
+source("../../Utilities/data.R", chdir = TRUE)
+
+#READ label file
+cat("Reading label file...\n")
+metadata1 <- read.table("../download/Ariss/wt_Rbf_and_populations.txt",header=T,stringsAsFactors = F)
+row.names(metadata1) <- metadata1[,'CellName']
+metadata1 <- metadata1[,c('Genotype','cell_type1')]
+
+metadata2 <- read.table("../download/Ariss/Cells_and_population.txt",header=T,row.names=1,stringsAsFactors = F)
+metadata2$Genotype <- 'wt'
+
+includedcells<-union(row.names(metadata1),row.names(metadata2))
+metadata <- rbind(metadata2,metadata1)
+metadata <- metadata[which(row.names(metadata) %in% includedcells),]
+
+celltypes <- read.csv('../download/celltypes',sep='\t')
+
+metadata$lifestage <- 'third instar larva stage'
+metadata$organ <- 'eye disc'
+metadata$race <- 'Drosophila melanogaster'
+
+#READ DGE
+cat("Reading DGE\n")
+path <- "../download/Ariss/GSE115476"
+fileNames <- dir(path) 
+filePath <- sapply(fileNames, function(x){ 
+ paste(path,x,sep='/')}) 
+data <- lapply(filePath, function(x){
+ read.table(x, header=T,stringsAsFactors = F)})
+
+i <- 1
+for (name in names(data)){
+ perfix<-substr(name,gregexpr(pattern = '_',text = name)[[1]]+1,gregexpr(pattern = "\\.",text = name)[[1]]-1)
+ colnames(data[[i]]) <- lapply(colnames(data[[i]]),function(x){
+ paste(perfix,x,sep='_')})
+ genes <- data[[i]][,1]
+ included_cells <- intersect(rownames(metadata), colnames(data[[i]]))
+ data[[i]] <- data.frame(genes,data[[i]][, included_cells])
+ i <- i+1
+}
+
+expmerge <- Reduce(function(x,y) merge(x,y,by=1,all=T),data)
+row.names(expmerge)<-expmerge[,1]
+expmerge<-expmerge[,-1]
+included_cells <- intersect(rownames(metadata), colnames(expmerge)) 
+metadata <- metadata[included_cells, ] 
+expmerge <- expmerge[, included_cells]
+expmerge[is.na(expmerge)]<-0
+
+expressed_genes <- rownames(expmerge)[rowSums(expmerge > 1) > 5]
+expmerge <- Matrix(as.matrix(expmerge),sparse = T)
+
+message("Constructing dataset...") 
+dataset <- new("ExprDataSet",
+ exprs = expmerge, obs = metadata,
+ var = data.frame(row.names = rownames(expmerge)),
+ uns = list(expressed_genes = expressed_genes) 
+)
+
+message("Saving data...") 
+write_dataset(dataset, "../data/Ariss/data.h5")
+cat("Done!\n")

Datasets/collect/collect_tusi.R

@@ -27,10 +27,15 @@ colnames(potential) <- "potential"
 meta_df <- Reduce(cbind, list(meta_df, fate, potential))
 rownames(meta_df) <- meta_df$cell_id
 meta_df$cell_id <- NULL
+meta_df$cell_type1 = "HSPC"
 
 expr_mat <- expr_mat[rownames(meta_df), ]
 
+#assign cell ontology
+cell_ontology <- read.csv("../cell_ontology/bone_marrow_cell_ontology.csv")
+cell_ontology <- cell_ontology[, c("cell_type1", "cell_ontology_class", "cell_ontology_id")]
+
 #datasets_meta
 datasets_meta <- read.csv("../ACA_datasets.csv", header = TRUE, row.names = 1)
-construct_dataset("../data/Tusi", t(expr_mat), meta_df, datasets_meta, grouping = "batch")
+construct_dataset("../data/Tusi", t(expr_mat), meta_df, datasets_meta, cell_ontology, grouping = "batch")
 message("Done!")