tutorials/demo_moca_100k.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running CELLEX on MOCA dataset\n",
    "\n",
    "# The data\n",
    "The full version of Mouse Organogenesis Cell Atlas (MOCA) contains ~2 million cells, derived from 61 embryos staged between 9.5 and 13.5 days of gestation. Please see https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/landing for more information. There are hundreds of cell types, 56 trajectories and thousands of corresponding marker genes. The dynamics of gene expression is defined within cell types and trajectories over time. \n",
    "\n",
    "For downstream analysis, the creators of the dataset (Cao, J. and Spielmann, M et al.) perfomed several rounds of cell filtering: (1) remove cells with less than 400 detected mRNA molecules, (2) remove all detected doublet cells, (3) remove all cells from doublet derived sub-clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the CELLEX repository:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install CELLEX to your env\n",
    "#!pip install cellex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing \n",
    "For testing and demonstration purposes, we choose the filtered MOCA dataset. It contains 100,000 unique cells sampled from the original MOCA dataset. Please download the RDS and cell annotations files with by the commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# macOS\n",
    "# !curl -O https://shendure-web.gs.washington.edu/content/members/cao1025/public/mouse_embryo_atlas/cds_cleaned_sampled_100k.RDS \n",
    "# !curl -O https://shendure-web.gs.washington.edu/content/members/cao1025/public/mouse_embryo_atlas/cell_annotate.csv\n",
    "    \n",
    "# Linux\n",
    "# !wget https://shendure-web.gs.washington.edu/content/members/cao1025/public/mouse_embryo_atlas/cds_cleaned_sampled_100k.RDS    \n",
    "# !wget https://shendure-web.gs.washington.edu/content/members/cao1025/public/mouse_embryo_atlas/cell_annotate.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RDS file contains 10 refined main trajectories (`Main_trajectory_refined_by_cluster`), obtained by splitting the cells during trajectory analysis, and 56 sub-trajectories (`Sub_trajectory_name`). `Main_Cluster` is the main cluster ID and `sub_cluster_id` is the sub-cluster ID after sub-clustering analysis.\n",
    "\n",
    "A .loom file was generated via the following R script. A loom file can conveniently store the data and metadata needed by CELLEX."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```r\n",
    "library(Seurat)\n",
    "library(tidyverse)\n",
    "library(here)\n",
    "library(data.table)\n",
    "library(RLinuxModules)\n",
    "library(loomR)\n",
    "\n",
    "### Read the data\n",
    "cds <- readRDS(here(\"cds_cleaned_sampled_100k.RDS\"))\n",
    "\n",
    "### Get cds cell annotations \n",
    "### These annotations does not contain \n",
    "### all the information in cell_annotate.csv.gz (e.g. Sub_trajectory_Pseudotime is missing)\n",
    "df.cell = Biobase::pData(cds) # cell meta-data | same as cds@phenoData@data\n",
    "df.cell <- df.cell %>% select(sample) %>% as_tibble()\n",
    "\n",
    "### Get annotations for all cells\n",
    "df.annot <- data.table::fread(here(\"cell_annotate.csv\"), nThread=50, showProgress=T) %>% as_tibble() \n",
    "glimpse(df.annot)\n",
    "\n",
    "df.annot.sub <- df.annot %>% \n",
    "  filter(doublet_cluster==FALSE, detected_doublet==FALSE) %>% \n",
    "  select(sample, \n",
    "         celltype = Main_cell_type, \n",
    "         trajectory_main = Main_trajectory_refined_by_cluster, \n",
    "         celltype_subcluster_id = sub_cluster_id,\n",
    "         trajectory_sub = Sub_trajectory_name, \n",
    "         pseudotime_trajectory_sub = Sub_trajectory_Pseudotime, \n",
    "         embryo_id,\n",
    "         embryo_sex,\n",
    "         development_stage) \n",
    "\n",
    "### Join\n",
    "df.cell_w_annot <- df.cell %>% left_join(df.annot.sub, by=\"sample\")\n",
    "\n",
    "df.cell.seurat <- df.cell_w_annot %>% \n",
    "  mutate(cell_id = sample) %>% \n",
    "  select(cell_id, everything()) %>% \n",
    "  as.data.frame() %>% \n",
    "  column_to_rownames(var=\"sample\")\n",
    "gene_count = Biobase::exprs(cds)      # expression count matrix (unnormalized)\n",
    "\n",
    "### Remove gene version number and check for duplicates when removing version number\n",
    "gene_ids_without_version_number <- str_split(rownames(gene_count), pattern=\"\\\\.\") %>% map_chr(1)\n",
    "any(duplicated(gene_ids_without_version_number))    # --> FALSE\n",
    "rownames(gene_count) <- gene_ids_without_version_number\n",
    "\n",
    "### Create Seurat object\n",
    "seurat_obj <- CreateSeuratObject(counts=gene_count, meta.data=df.cell.seurat)\n",
    "seurat_obj@meta.data %>% as_tibble() %>% head()\n",
    "\n",
    "df.annot %>% select(Main_Cluster, Sub_cluster, sub_cluster_id)\n",
    "# Main_Cluster Sub_cluster sub_cluster_id\n",
    "# <int>       <int> <chr>         \n",
    "#   1            5          NA NA            \n",
    "# 2           20           1 20-1          \n",
    "# 3           21          NA NA            \n",
    "# 4           17          NA NA            \n",
    "# 5            6          NA NA            \n",
    "# 6           25           1 25-1          \n",
    "# 7           23          NA NA            \n",
    "# 8            8          NA NA            \n",
    "# 9            2          12 2-12          \n",
    "# 10            6          28 6-28   \n",
    "# ----> sub_cluster_id is SUFFICIENT to identify the main_cluster and sub_cluster\n",
    "n_distinct(df.annot$sub_cluster_id) \n",
    "df.annot %>% filter(doublet_cluster==FALSE, detected_doublet==FALSE) %>% distinct(sub_cluster_id) %>% nrow() \n",
    "\n",
    "### Super important step!!! Tranforms seurat_obj$RNA@meta.features to a table. \n",
    "### We need seurat_obj$RNA@meta.features to have columns in order for the .loom file to be generated! \n",
    "seurat_obj <- FindVariableFeatures(object = seurat_obj)    \n",
    "\n",
    "### Create the .loom file from the seurat object\n",
    "pfile <- as.loom(x = seurat_obj, filename = \"cds_cleaned_sampled_100k.loom\", verbose = TRUE, overwrite = TRUE)\n",
    "\n",
    "### Close the .loom file after all\n",
    "pfile$close_all()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages\n",
    "\n",
    "N.B. If you are missing any dependencies in your environment, your imports will fail until all dependencies are installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import loompy         # needed for importing data\n",
    "import numpy as np    # needed for formatting data\n",
    "import pandas as pd   # needed for formatting data\n",
    "import cellex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Set constants\n",
    "\n",
    "dirOut = \"cellex_moca_out\"                    # output directory for results and plots\n",
    "prefixData = \"moca_100k\"                      # prefix to prepend to files\n",
    "pathData = \"/scratch/lily/cds_cleaned_sampled_100k.loom\"\n",
    "\n",
    "nameAnno = \"trajectory_main\"                  # metadata annotation column attribute name\n",
    "nameId = \"CellID\"                             # metadata cell id column attribute name\n",
    "nameClass = \"trajectory_main\"                  # possible classes to group by: trajectory_main, trajectory_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and format data\n",
    "\n",
    "Cellex takes Pandas dataframes as input. These must conform to a certain format. See the output below.\n",
    "\n",
    "## Data\n",
    "If the dataset has duplicate cells, CELLEX is able to handle this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with loompy.connect(pathData) as ds:\n",
    "    rows = (ds.row_attrs[\"Gene\"])\n",
    "    cols = (ds.col_attrs[nameId])\n",
    "    #our data\n",
    "    data = pd.DataFrame(ds[:, :], index=rows, columns=cols)\n",
    "    # the type-annotation for individual cells\n",
    "    metadata = pd.DataFrame(data={\"cell_type\" : ds.col_attrs[nameAnno]}, index=ds.col_attrs[nameId])\n",
    "    metadata_class = pd.DataFrame(data={\"cell_class\" : ds.col_attrs[nameClass]}, index=ds.col_attrs[nameAnno])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "ENSMUSG00000089699                              0.0   \n",
       "\n",
       "                    sci3-me-760.TCAAGATCTTCAATTACCGT  \\\n",
       "ENSMUSG00000051951                               0.0   \n",
       "ENSMUSG00000103377                               0.0   \n",
       "ENSMUSG00000104017                               0.0   \n",
       "ENSMUSG00000103025                               0.0   \n",
       "ENSMUSG00000089699                               0.0   \n",
       "\n",
       "                    sci3-me-760.GACCGAATTCATACTCCGA  \\\n",
       "ENSMUSG00000051951                              0.0   \n",
       "ENSMUSG00000103377                              0.0   \n",
       "ENSMUSG00000104017                              0.0   \n",
       "ENSMUSG00000103025                              0.0   \n",
       "ENSMUSG00000089699                              0.0   \n",
       "\n",
       "                    sci3-me-760.ATTGATCGTTCGTAATGCAG  \\\n",
       "ENSMUSG00000051951                               0.0   \n",
       "ENSMUSG00000103377                               0.0   \n",
       "ENSMUSG00000104017                               0.0   \n",
       "ENSMUSG00000103025                               0.0   \n",
       "ENSMUSG00000089699                               0.0   \n",
       "\n",
       "                    sci3-me-760.TTCTCATTGTATCGGCTATC  \\\n",
       "ENSMUSG00000051951                               0.0   \n",
       "ENSMUSG00000103377                               0.0   \n",
       "ENSMUSG00000104017                               0.0   \n",
       "ENSMUSG00000103025                               0.0   \n",
       "ENSMUSG00000089699                               0.0   \n",
       "\n",
       "                    sci3-me-760.CTAGTACGTCGTAGTTACC  \n",
       "ENSMUSG00000051951                              0.0  \n",
       "ENSMUSG00000103377                              0.0  \n",
       "ENSMUSG00000104017                              0.0  \n",
       "ENSMUSG00000103025                              0.0  \n",
       "ENSMUSG00000089699                              0.0  \n",
       "\n",
       "[5 rows x 100000 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata\n",
    "Index values should be unique, like the cells. Alternatively, CELLEX will handle the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sci3-me-001.ATTGAGGAATCGGAGATCCG</th>\n",
       "      <td>Endothelial trajectory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sci3-me-001.ACGAGGTTTAACTGATCTT</th>\n",
       "      <td>Haematopoiesis trajectory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sci3-me-002.TCTTAGTTCTTATAGACGCA</th>\n",
       "      <td>Neural tube and notochord trajectory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sci3-me-002.TCCTAACTTTCCTTACTCCT</th>\n",
       "      <td>Mesenchymal trajectory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sci3-me-002.CAGTATCCTCCGCAAGTTC</th>\n",
       "      <td>Neural crest (PNS neuron) trajectory 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               cell_type\n",
       "sci3-me-001.ATTGAGGAATCGGAGATCCG                  Endothelial trajectory\n",
       "sci3-me-001.ACGAGGTTTAACTGATCTT                Haematopoiesis trajectory\n",
       "sci3-me-002.TCTTAGTTCTTATAGACGCA    Neural tube and notochord trajectory\n",
       "sci3-me-002.TCCTAACTTTCCTTACTCCT                  Mesenchymal trajectory\n",
       "sci3-me-002.CAGTATCCTCCGCAAGTTC   Neural crest (PNS neuron) trajectory 3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how many cells are there in each <code>cell_type</code>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell_type\n",
      "Endothelial trajectory                     1857\n",
      "Epithelial trajectory                      4978\n",
      "Haematopoiesis trajectory                  3140\n",
      "Hepatic trajectory                          834\n",
      "Lens trajectory                              10\n",
      "Mesenchymal trajectory                    44141\n",
      "Neural crest (PNS glia) trajectory 2       1710\n",
      "Neural crest (PNS neuron) trajectory 3     1910\n",
      "Neural crest (melanocyte) trajectory 1      164\n",
      "Neural tube and notochord trajectory      41256\n",
      "Name: cell_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(metadata.groupby('cell_type').cell_type.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create `ESObject` and compute Expression Specificity\n",
    "\n",
    "The <code>ESObject</code> is the fundamental class of the CELLEX package. It integrates the core features of CELLEX: computation of ES genes, generating plots and saving results to disk. We set <code>verbose=True</code> to get some progress reports.\n",
    "\n",
    "In the following, we \n",
    "\n",
    "1) create the ESOject using the annotation from the metadata,\n",
    "\n",
    "2) compute ESw, ESw* and ESmu\n",
    "\n",
    "3) and inspect the results.\n",
    "\n",
    "N.B. the computations may take a while depending on the data and available computational power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing - checking input ... input parsed in 0 min 0 sec\n",
      "Preprocessing - running remove_non_expressed ... excluded 28 / 26183 genes in 1 min 27 sec\n",
      "Preprocessing - normalizing data ... data normalized in 6 min 7 sec\n",
      "Preprocessing - running ANOVA ... excluded 12346 / 26155 genes in 4 min 50 sec\n"
     ]
    }
   ],
   "source": [
    "eso = cellex.ESObject(data=data, annotation=metadata, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing DET ... \n",
      "    esw ...\n",
      "    empirical p-values ...\n",
      "    esw_s ...\n",
      "    finished in 0 min 19 sec\n",
      "Computing EP ...\n",
      "    esw ...\n",
      "    empirical p-values ...\n",
      "    esw_s ...\n",
      "    finished in 0 min 0 sec\n",
      "Computing GES ...\n",
      "    esw ...\n",
      "    empirical p-values ...\n",
      "    esw_s ...\n",
      "    finished in 2 min 1 sec\n",
      "Computing NSI ...\n",
      "    esw ...\n",
      "    empirical p-values ...\n",
      "    esw_s ...\n",
      "    finished in 0 min 0 sec\n",
      "Computing ESmu ...\n",
      "    finished in 0 min 0 sec\n",
      "Computing ESsd ...\n",
      "    finished in 0 min 0 sec\n",
      "Computed ['det.esw', 'det.esw_null', 'det.pvals', 'det.esw_s', 'ep.esw', 'ep.esw_null', 'ep.pvals', 'ep.esw_s', 'ges.esw', 'ges.esw_null', 'ges.pvals', 'ges.esw_s', 'nsi.esw', 'nsi.esw_null', 'nsi.pvals', 'nsi.esw_s', 'esmu', 'essd'].\n"
     ]
    }
   ],
   "source": [
    "eso.compute(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endothelial trajectory</th>\n",
       "      <th>Epithelial trajectory</th>\n",
       "      <th>Haematopoiesis trajectory</th>\n",
       "      <th>Hepatic trajectory</th>\n",
       "      <th>Lens trajectory</th>\n",
       "      <th>Mesenchymal trajectory</th>\n",
       "      <th>Neural crest (PNS glia) trajectory 2</th>\n",
       "      <th>Neural crest (PNS neuron) trajectory 3</th>\n",
       "      <th>Neural crest (melanocyte) trajectory 1</th>\n",
       "      <th>Neural tube and notochord trajectory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000051951</th>\n",
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       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000103377</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.673055</td>\n",
       "      <td>0.234674</td>\n",
       "      <td>0.179480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000104017</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.695298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.456519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000103025</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.700069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.390880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSMUSG00000089699</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.426948</td>\n",
       "      <td>0.126812</td>\n",
       "      <td>0.195254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Endothelial trajectory  Epithelial trajectory  \\\n",
       "ENSMUSG00000051951                     0.0                    0.0   \n",
       "ENSMUSG00000103377                     0.0                    0.0   \n",
       "ENSMUSG00000104017                     0.0                    0.0   \n",
       "ENSMUSG00000103025                     0.0                    0.0   \n",
       "ENSMUSG00000089699                     0.0                    0.0   \n",
       "\n",
       "                    Haematopoiesis trajectory  Hepatic trajectory  \\\n",
       "ENSMUSG00000051951                        0.0                 0.0   \n",
       "ENSMUSG00000103377                        0.0                 0.0   \n",
       "ENSMUSG00000104017                        0.0                 0.0   \n",
       "ENSMUSG00000103025                        0.0                 0.0   \n",
       "ENSMUSG00000089699                        0.0                 0.0   \n",
       "\n",
       "                    Lens trajectory  Mesenchymal trajectory  \\\n",
       "ENSMUSG00000051951              0.0                     0.0   \n",
       "ENSMUSG00000103377              0.0                     0.0   \n",
       "ENSMUSG00000104017              0.0                     0.0   \n",
       "ENSMUSG00000103025              0.0                     0.0   \n",
       "ENSMUSG00000089699              0.0                     0.0   \n",
       "\n",
       "                    Neural crest (PNS glia) trajectory 2  \\\n",
       "ENSMUSG00000051951                                   0.0   \n",
       "ENSMUSG00000103377                                   0.0   \n",
       "ENSMUSG00000104017                                   0.0   \n",
       "ENSMUSG00000103025                                   0.0   \n",
       "ENSMUSG00000089699                                   0.0   \n",
       "\n",
       "                    Neural crest (PNS neuron) trajectory 3  \\\n",
       "ENSMUSG00000051951                                0.589615   \n",
       "ENSMUSG00000103377                                0.673055   \n",
       "ENSMUSG00000104017                                0.695298   \n",
       "ENSMUSG00000103025                                0.700069   \n",
       "ENSMUSG00000089699                                0.426948   \n",
       "\n",
       "                    Neural crest (melanocyte) trajectory 1  \\\n",
       "ENSMUSG00000051951                                0.000000   \n",
       "ENSMUSG00000103377                                0.234674   \n",
       "ENSMUSG00000104017                                0.000000   \n",
       "ENSMUSG00000103025                                0.000000   \n",
       "ENSMUSG00000089699                                0.126812   \n",
       "\n",
       "                    Neural tube and notochord trajectory  \n",
       "ENSMUSG00000051951                              0.554942  \n",
       "ENSMUSG00000103377                              0.179480  \n",
       "ENSMUSG00000104017                              0.456519  \n",
       "ENSMUSG00000103025                              0.390880  \n",
       "ENSMUSG00000089699                              0.195254  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso.results[\"esmu\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (Optional) Map mouse Ensembl gene id's to human Ensembl gene id's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping: mouse ensembl gene id's --> human ensembl gene id's ...\n",
      "23.58 pct of genes are unmapped ...\n",
      "Removed 3256 unmapped genes ...\n",
      "Mapping: human ensembl gene id's --> gene names ...\n",
      "0.61 pct of genes are unmapped ...\n",
      "Removed 64 unmapped genes ...\n",
      "Mapping: mouse ensembl gene id's --> human ensembl gene id's ...\n",
      "23.58 pct of genes are unmapped ...\n",
      "Removed 3256 unmapped genes ...\n",
      "Mapping: human ensembl gene id's --> gene names ...\n",
      "0.61 pct of genes are unmapped ...\n",
      "Removed 64 unmapped genes ...\n"
     ]
    }
   ],
   "source": [
    "cellex.utils.mapping.mouse_ens_to_human_ens(eso.results[\"esmu\"], drop_unmapped=True, verbose=True)\n",
    "cellex.utils.mapping.human_ens_to_human_symbol(eso.results[\"esmu\"], drop_unmapped=True, verbose=True)\n",
    "\n",
    "cellex.utils.mapping.mouse_ens_to_human_ens(eso.results[\"essd\"], drop_unmapped=True, verbose=True)\n",
    "cellex.utils.mapping.human_ens_to_human_symbol(eso.results[\"essd\"], drop_unmapped=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endothelial trajectory</th>\n",
       "      <th>Epithelial trajectory</th>\n",
       "      <th>Haematopoiesis trajectory</th>\n",
       "      <th>Hepatic trajectory</th>\n",
       "      <th>Lens trajectory</th>\n",
       "      <th>Mesenchymal trajectory</th>\n",
       "      <th>Neural crest (PNS glia) trajectory 2</th>\n",
       "      <th>Neural crest (PNS neuron) trajectory 3</th>\n",
       "      <th>Neural crest (melanocyte) trajectory 1</th>\n",
       "      <th>Neural tube and notochord trajectory</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>XKR4</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>RP1</th>\n",
       "      <td>0.768983</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.123431</td>\n",
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       "    <tr>\n",
       "      <th>SOX17</th>\n",
       "      <td>0.989063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>MRPL15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018166</td>\n",
       "      <td>0.049267</td>\n",
       "      <td>0.277541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LYPLA1</th>\n",
       "      <td>0.189016</td>\n",
       "      <td>0.268122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.197348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148387</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Endothelial trajectory  Epithelial trajectory  \\\n",
       "XKR4                  0.000000               0.000000   \n",
       "RP1                   0.768983               0.000000   \n",
       "SOX17                 0.989063               0.000000   \n",
       "MRPL15                0.000000               0.018166   \n",
       "LYPLA1                0.189016               0.268122   \n",
       "\n",
       "        Haematopoiesis trajectory  Hepatic trajectory  Lens trajectory  \\\n",
       "XKR4                     0.000000            0.000000              0.0   \n",
       "RP1                      0.000000            0.000000              0.0   \n",
       "SOX17                    0.000000            0.000000              0.0   \n",
       "MRPL15                   0.049267            0.277541              0.0   \n",
       "LYPLA1                   0.000000            0.197348              0.0   \n",
       "\n",
       "        Mesenchymal trajectory  Neural crest (PNS glia) trajectory 2  \\\n",
       "XKR4                       0.0                                   0.0   \n",
       "RP1                        0.0                                   0.0   \n",
       "SOX17                      0.0                                   0.0   \n",
       "MRPL15                     0.0                                   0.0   \n",
       "LYPLA1                     0.0                                   0.0   \n",
       "\n",
       "        Neural crest (PNS neuron) trajectory 3  \\\n",
       "XKR4                                  0.589615   \n",
       "RP1                                   0.000000   \n",
       "SOX17                                 0.000000   \n",
       "MRPL15                                0.000000   \n",
       "LYPLA1                                0.148387   \n",
       "\n",
       "        Neural crest (melanocyte) trajectory 1  \\\n",
       "XKR4                                       0.0   \n",
       "RP1                                        0.0   \n",
       "SOX17                                      0.0   \n",
       "MRPL15                                     0.0   \n",
       "LYPLA1                                     0.0   \n",
       "\n",
       "        Neural tube and notochord trajectory  \n",
       "XKR4                                0.554942  \n",
       "RP1                                 0.123431  \n",
       "SOX17                               0.000000  \n",
       "MRPL15                              0.000000  \n",
       "LYPLA1                              0.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eso.results[\"esmu\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save results to disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, only ESmu is saved.\n",
    "\n",
    "Function signature: <code>eso.save_as_csv(keys: list=None, path: str=None, file_prefix: str=None, verbose: bool=False)</code>\n",
    "\n",
    "<code>keys</code>\n",
    "<ul>\n",
    "    <li>Using 'keys', the user may specify which results are saved.</li>\n",
    "    <li>If keys is None, only ESmu is saved.</li>\n",
    "    <li>If keys contains the keyword: \"all\", all results are saved.</li>\n",
    "</ul>\n",
    "\n",
    "<code>path</code>\n",
    "<ul>\n",
    "    <li>dir_name defaults to \"out\"</li>\n",
    "    <li>To save in the current working directory, pass dir_name=\".\"</li>\n",
    "</ul>\n",
    "\n",
    "<code>file_prefix</code>\n",
    "<ul>\n",
    "<li>Prefix is appended to the saved files. A dot is used to separate prefix from the result key.</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving results as csv to disk ...\n",
      "  Saved: cellex_moca_out/moca_100k.esmu.csv.gz\n",
      "  Saved: cellex_moca_out/moca_100k.essd.csv.gz\n",
      "Finished saving results to cellex_moca_out\n"
     ]
    }
   ],
   "source": [
    "eso.save_as_csv(file_prefix=prefixData, path=dirOut, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results\n",
    "\n",
    "CELLEX provides a handful of plotting features for quick visualization of results. Below we plot the top 5 ES genes for each cell-type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_esmu = eso.results[\"esmu\"].copy()\n",
    "es_max_genes = [(df_esmu.nlargest(n=5, columns=[col]).index.values) for col in df_esmu]\n",
    "genes_to_plot = list(set(np.array(es_max_genes).flatten().tolist()))       # set() removes duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Endothelial trajectory</th>\n",
       "      <th>Epithelial trajectory</th>\n",
       "      <th>Haematopoiesis trajectory</th>\n",
       "      <th>Hepatic trajectory</th>\n",
       "      <th>Lens trajectory</th>\n",
       "      <th>Mesenchymal trajectory</th>\n",
       "      <th>Neural crest (PNS glia) trajectory 2</th>\n",
       "      <th>Neural crest (PNS neuron) trajectory 3</th>\n",
       "      <th>Neural crest (melanocyte) trajectory 1</th>\n",
       "      <th>Neural tube and notochord trajectory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RFX6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.979412</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GABRP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988678</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FYB1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.98969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C8A</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.993127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TACSTD2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.985130</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Endothelial trajectory  Epithelial trajectory  \\\n",
       "RFX6                        0.0               0.979412   \n",
       "GABRP                       0.0               0.988678   \n",
       "FYB1                        0.0               0.000000   \n",
       "C8A                         0.0               0.000000   \n",
       "TACSTD2                     0.0               0.985130   \n",
       "\n",
       "         Haematopoiesis trajectory  Hepatic trajectory  Lens trajectory  \\\n",
       "RFX6                       0.00000            0.000000              0.0   \n",
       "GABRP                      0.00000            0.000000              0.0   \n",
       "FYB1                       0.98969            0.000000              0.0   \n",
       "C8A                        0.00000            0.993127              0.0   \n",
       "TACSTD2                    0.00000            0.000000              0.0   \n",
       "\n",
       "         Mesenchymal trajectory  Neural crest (PNS glia) trajectory 2  \\\n",
       "RFX6                   0.000000                                   0.0   \n",
       "GABRP                  0.000000                                   0.0   \n",
       "FYB1                   0.000000                                   0.0   \n",
       "C8A                    0.000000                                   0.0   \n",
       "TACSTD2                0.070848                                   0.0   \n",
       "\n",
       "         Neural crest (PNS neuron) trajectory 3  \\\n",
       "RFX6                                        0.0   \n",
       "GABRP                                       0.0   \n",
       "FYB1                                        0.0   \n",
       "C8A                                         0.0   \n",
       "TACSTD2                                     0.0   \n",
       "\n",
       "         Neural crest (melanocyte) trajectory 1  \\\n",
       "RFX6                                        0.0   \n",
       "GABRP                                       0.0   \n",
       "FYB1                                        0.0   \n",
       "C8A                                         0.0   \n",
       "TACSTD2                                     0.0   \n",
       "\n",
       "         Neural tube and notochord trajectory  \n",
       "RFX6                                      0.0  \n",
       "GABRP                                     0.0  \n",
       "FYB1                                      0.0  \n",
       "C8A                                       0.0  \n",
       "TACSTD2                                   0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_esmu.loc[genes_to_plot].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# profile_gene plot\n",
    "\n",
    "(There is a known bug with the labels. Sometimes they are shifted one place to the left or do not show)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363278846852366)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_esmu = cellex.plotting.gene_profile(genes=[\"SOX17\"], weights=eso.results[\"esmu\"], \n",
    "                                      stddev=eso.results[\"essd\"], highlight_n=3)\n",
    "p_esmu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `heatmap` plot\n",
    "It is advisable to select a limited number of genes and annotations. Heatmaps generated for the full dataset are tough on the eyes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8759313664737)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_heatmap = cellex.plotting.heatmap(df_esmu, genes=genes_to_plot)\n",
    "p_heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363277541102206)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmi_genes = [\"ZFHX3\", \"BDNF\", \"CEP19\", \"KSR2\", \"ENTPD6\", \"ACHE\", \"ZFR2\", \"BBS4\", \"TRIM32\", \"PCSK1\", \"BBS12\", \"POMC\", \"WNT10B\", \"GPC3\", \"ARL6\", \"BBS9\", \"PRKAG1\", \"OFD1\", \"RAPGEF3\", \"TBX3\", \"MKS1\", \"LEP\", \"LEPR\", \"PHF6\", \"WDPCP\", \"BBS2\", \"BBS10\", \"RAB23\", \"MC4R\", \"HIP1R\", \"GIPR\", \"SIM1\", \"ALMS1\", \"ZNF169\", \"RAB21\", \"GNAS\", \"PRKAR1A\", \"BBS7\", \"RAI1\", \"SH2B1\", \"NTRK2\", \"CEP290\", \"MKKS\", \"LZTFL1\", \"VPS13B\", \"SDCCAG8\", \"TTC8\"]\n",
    "cellex.plotting.heatmap(df_esmu, genes=bmi_genes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save plots to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: cellex_moca_out/moca_100k.esmu.heatmap.pdf\n"
     ]
    }
   ],
   "source": [
    "fp = \"{}/{}.esmu.heatmap.pdf\".format(dirOut, prefixData)\n",
    "cellex.plotting.save_as_pdf(p_heatmap, filename=fp, path=dirOut, dpi=600, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete ESObject when completed to release memory\n",
    "del eso"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (jp)",
   "language": "python",
   "name": "jp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}