diff --git a/dash_app/DashPalm_prep.py b/dash_app/DashPalm_prep.py
new file mode 100644
index 0000000..679d222
--- /dev/null
+++ b/dash_app/DashPalm_prep.py
@@ -0,0 +1,236 @@
+## Libraries
+
+import rpy2.robjects.packages as rpackages
+import pandas as pd
+import numpy as np
+
+# working with dataframes
+from rpy2.robjects.conversion import localconverter
+import rpy2.robjects as ro
+from rpy2.robjects import pandas2ri
+from rpy2.robjects.vectors import StrVector
+
+# TODO: suppress system warnings from imports
+from rpy2.robjects.packages import importr
+
+utils = importr('utils')
+# getting ready to download these packages...
+importr('PALMO')
+importr('Hmisc')
+rpackages.importr('ggpubr')
+
+
+def rpy_2py(df):
+ ''' convenient function that will convert rpy object to a pandas data.frame
+ '''
+ with localconverter(ro.default_converter + pandas2ri.converter):
+ return (ro.conversion.rpy2py(df))
+
+
+def py2_rpy(df):
+ ''' convenient function that will convert a pandas data.frame to rpy object that can then be used
+ for R functions
+ '''
+ with localconverter(ro.default_converter + pandas2ri.converter):
+ return (ro.conversion.py2rpy(df))
+
+
+def load_data(data_fp, meta_fp, datatype):
+ '''
+ '''
+ if datatype == 'bulk':
+ palmo_obj = ro.r('''
+ library("PALMO")
+ load("{}")
+ load("{}")
+ palmo_obj <- createPALMOobject(anndata=ann, data=data)
+ '''.format(data_fp, meta_fp))
+ elif datatype == 'singlecell':
+ palmo_obj = ro.r('''
+ pbmc <- readRDS("{}")
+ metaData <- pbmc@meta.data
+ pbmc@meta.data$Sample <- pbmc@meta.data$orig.ident
+ pbmc@meta.data$celltype <- gsub(" ", "_", pbmc@meta.data$celltype)
+
+ # load annotation
+ load("{}")
+ palmo_obj <- createPALMOobject(anndata=ann, data=pbmc)
+ '''.format(data_fp, meta_fp))
+
+ return palmo_obj
+
+
+def prep_data(palmo_obj, datatype, na_cutoff):
+ """
+ """
+ if datatype == "bulk":
+ palmo_obj = ro.r('''
+ palmo_obj <- annotateMetadata(data_object=palmo_obj, sample_column="Sample", donor_column="PTID", time_column="Time")
+ palmo_obj <- mergePALMOdata(data_object=palmo_obj, datatype="{dt}")
+ palmo_obj <- checkReplicates(data_object=palmo_obj, mergeReplicates=T)
+ palmo_obj <- naFilter(data_object=palmo_obj, na_cutoff={na})
+ '''.format(dt=datatype, na=float(na_cutoff)))
+ if datatype == "singlecell":
+ palmo_obj = ro.r('''
+ palmo_obj <- annotateMetadata(data_object=palmo_obj, sample_column="Sample", donor_column="PTID", time_column="Time")
+ palmo_obj <- mergePALMOdata(data_object=palmo_obj, datatype="{dt}")
+ palmo_obj <- avgExpCalc(data_object=palmo_obj, assay="RNA", group_column="{ct}")
+ palmo_obj <- checkReplicates(data_object=palmo_obj, mergeReplicates=T)
+ '''.format(dt=datatype, ct='celltype'))
+ return palmo_obj
+
+
+def run_lmeVariance(palmo_obj, mean_threshold, feature_set, datatype):
+ '''
+ '''
+ if datatype == 'bulk':
+ # load lmeVariance object function and run
+ palmo_obj = ro.r('''
+ featureSet <- c("{f1}", "{f2}")
+ palmo_obj <- lmeVariance(data_object=palmo_obj, featureSet=featureSet, meanThreshold={mean}, fileName="olink")
+ '''.format(f1=feature_set[0],
+ f2=feature_set[1],
+ mean=mean_threshold))
+ elif datatype == 'singlecell':
+ palmo_obj = ro.r('''
+ featureSet <- c("{f1}", "{f2}", "{f3}")
+ palmo_obj <- lmeVariance(data_object=palmo_obj, cl=4, featureSet=featureSet, meanThreshold={mean}, fileName="scrna")
+ '''.format(f1=feature_set[0],
+ f2=feature_set[1],
+ f3=feature_set[2],
+ mean=mean_threshold))
+ return palmo_obj
+
+
+def run_outlier_detection(palmo_obj, z_cutoff):
+ '''
+ '''
+ # robject of outlierDetect function
+ palmo_obj = ro.r('''
+ palmo_obj <- outlierDetect(data_object=palmo_obj, z_cutoff={})
+
+ # assign cutoffs
+ '''.format(z_cutoff))
+ return palmo_obj
+
+
+def run_cvCalc(palmo_obj, meanThreshold, cvThreshold, datatype):
+ """ Intro-donor variations over time
+ TODO: label top 10 genes, and most stable ones
+ """
+ if datatype == 'bulk':
+ palmo_obj = ro.r('''
+ palmo_obj <- cvCalcBulk(data_object=palmo_obj, meanThreshold={m}, cvThreshold={c})
+ '''.format(m=meanThreshold, c=cvThreshold))
+ elif datatype == 'singlecell':
+ palmo_obj = ro.r('''
+ palmo_obj <- cvCalcSC(data_object=palmo_obj, meanThreshold={m}, cvThreshold={c})
+ '''.format(m=meanThreshold, c=cvThreshold))
+ return palmo_obj
+
+
+def run_cvcalc_scprofile(palmo_obj, mean_threshold, housekeeping_genes):
+ palmo_obj = ro.r('''
+ palmo_obj <- cvCalcSCProfile(data_object=palmo_obj, meanThreshold={avg}, housekeeping_genes=c("GAPDH", "ACTB"), fileName="scrna")
+ '''.format(avg=mean_threshold))
+ return palmo_obj
+
+
+def find_variable_features(palmo_obj, cv_threshold):
+ palmo_obj = ro.r('''
+ donorThreshold <- 4
+ groupThreshold <- 40
+ celltype_oi <- c("CD4_Naive","CD4_TEM","CD4_TCM","CD4_CTL","CD8_Naive",
+ "CD8_TEM","CD8_TCM","Treg","MAIT","gdT",
+ "NK", "NK_CD56bright",
+ "B_naive", "B_memory", "B_intermediate",
+ "CD14_Mono","CD16_Mono",
+ "cDC2","pDC")
+ palmo_obj <- VarFeatures(data_object=palmo_obj, group_oi=celltype_oi,
+ cvThreshold={cv}, groupThreshold=groupThreshold, topFeatures=25,
+ fileName="scrna")
+ '''.format(cv=cv_threshold))
+ return palmo_obj
+
+
+def find_stable_features(palmo_obj, cv_threshold):
+ '''
+ '''
+ palmo_obj = ro.r('''
+ donorThreshold <- 4
+ groupThreshold <- 40
+ celltype_oi <- c("CD4_Naive","CD4_TEM","CD4_TCM","CD4_CTL","CD8_Naive",
+ "CD8_TEM","CD8_TCM","Treg","MAIT","gdT",
+ "NK", "NK_CD56bright",
+ "B_naive", "B_memory", "B_intermediate",
+ "CD14_Mono","CD16_Mono",
+ "cDC2","pDC")
+ StableFeatures(data_object=palmo_obj, group_oi=celltype_oi, cvThreshold={}, donorThreshold=donorThreshold, groupThreshold=groupThreshold, topFeatures=25, fileName="scrna")
+ '''.format(cv_threshold))
+ return palmo_obj
+
+
+def run(data_filepath, metadata_filepath, datatype, z_cutoff, mean_threshold,
+ cv_threshold, na_threshold, housekeeping_genes, feature_set):
+ ''' main function
+ 1. load datasets
+ 2. run variance custom function
+ 3. run outlier detection custom function
+ '''
+ #palmo_obj = load_data(data_filepath, metadata_filepath, datatype)
+ # palmo_obj = prep_data(palmo_obj, datatype, na_threshold)
+ if datatype == 'bulk':
+ print('in bulk')
+ palmo_obj = run_lmeVariance(palmo_obj, mean_threshold, feature_set,
+ datatype)
+ palmo_obj = run_cvCalc(palmo_obj,
+ meanThreshold=mean_threshold,
+ cvThreshold=cv_threshold,
+ datatype=datatype)
+ palmo_obj = run_outlier_detection(palmo_obj, z_cutoff)
+
+ # extract curated data.frames and results
+ datamatrix = rpy_2py(ro.r("palmo_obj@curated$data"))
+ metadata = rpy_2py(ro.r("palmo_obj@curated$ann"))
+ decomp_var_df = rpy_2py(
+ ro.r("palmo_obj@result$variance_decomposition"))
+ cv_all = rpy_2py(ro.r("palmo_obj@result$cv_all"))
+ outlier_res = rpy_2py(ro.r("palmo_obj@result[['outlier_res']]"))
+ return (datamatrix, metadata, decomp_var_df, cv_all, outlier_res)
+ if datatype == 'singlecell':
+ print('loading datasets...')
+ work_dir = '/Users/james.harvey/workplace/scrna_dash'
+ data = pd.read_csv('{}/data.csv'.format(work_dir))
+ metadata = pd.read_csv('{}/metadata.csv'.format(work_dir))
+ # cv_res = pd.read_csv('{}/cv_res.csv'.format(work_dir))
+ var_decomp = pd.read_csv('{}/var_decomp.csv'.format(work_dir))
+ var_genes = pd.read_csv('{}/var_genes.csv'.format(work_dir))
+ var_genes['is_var_gene'] = 1
+ stable_genes = pd.read_csv('{}/non_var_genes.csv'.format(work_dir))
+ stable_genes['is_var_gene'] = 0
+ cv_res = pd.concat([var_genes, stable_genes])
+ outlier_res = pd.DataFrame()
+ print('got data')
+ return (data, metadata, var_decomp, pd.DataFrame(), outlier_res)
+
+
+if __name__ == "__main__":
+ import sys
+ datamatrix_filepath = sys.argv[1]
+ metadata_filepath = sys.argv[2]
+ datatype = sys.argv[3]
+ z_cutoff = float(sys.argv[4])
+ mean_threshold = float(sys.argv[5])
+ cv_threshold = float(sys.argv[6])
+ na_threshold = float(sys.argv[7])
+ housekeeping_genes = sys.argv[8]
+ feature_set = sys.argv[9]
+ run(datamatrix_filepath=datamatrix_filepath,
+ metadata_filepath=metadata_filepath,
+ datatype=datatype,
+ z_cutoff=z_cutoff,
+ mean_threshold=mean_threshold,
+ cv_threshold=cv_threshold,
+ na_threshold=na_threshold,
+ housekeeping_genes=housekeeping_genes,
+ feature_set=feature_set)
diff --git a/dash_app/PALM_Dash_v2.ipynb b/dash_app/PALM_Dash_v2.ipynb
new file mode 100644
index 0000000..795ffac
--- /dev/null
+++ b/dash_app/PALM_Dash_v2.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "70932572-448e-428c-9773-64f38354736c",
+ "metadata": {},
+ "source": [
+ "## PALM Dash App\n",
+ "\n",
+ "v2.0 (2021-3-3) \n",
+ "James Harvey \n",
+ "\n",
+ "### Purpose\n",
+ "In this notebook we'll be creating a Dash App. The Dash app will be an interactive tool to play around with PALM Library that was developed by Suhas V. \n",
+ "\n",
+ "See PALM repo here: https://github.com/aifimmunology/PALM\n",
+ "\n",
+ "### Contents\n",
+ "\n",
+ "1. Load essential Libraries \n",
+ "2. Run App \n",
+ "\n",
+ "\n",
+ "### NOTES: \n",
+ "- still need to do some setting up with packages:
\n",
+ " NOTE: PALM has a few dependencies I need to install beforehand \n",
+ " - sudo apt install cmake \n",
+ " - install.packages('nloptr') # reminder need to first install this via ubuntu: \"sudo apt-get install libnlopt-dev\" \n",
+ " - install.packages('lme4') \n",
+ " - install.packages('ggforce') \n",
+ " - install.packages('factoextra')\n",
+ " - install.packages(\"/home/jupyter/PALM_Dash/PALM/PALM_0.1.0.tar.gz\", repos = NULL, type =\"source\") # after cloning from repo \n",
+ "- could potentially utilize long_callbacks when users want to run a particular graph with different parameters \n",
+ "- TODO: get these in an image, or find some other way to do this set up under the hood \n",
+ "\n",
+ "___ \n",
+ "new set up: \n",
+ "
\n",
+ "- sudo apt install libgeos-dev\n",
+ "- sudo apt install cmake \n",
+ "- sudo apt-get install libnlopt-dev\n",
+ "\n",
+ "R console: \n",
+ "- library(\"devtools\")\n",
+ "- install_github(\"aifimmunology/PALMO\")\n",
+ "- library(\"PALMO\")\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dce21daf-78c9-4d29-9448-048e4b9877b3",
+ "metadata": {},
+ "source": [
+ "___\n",
+ "### Essential Library for Dash App "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "87663871-622c-4715-aea7-f50256ec05cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from jupyter_dash.comms import _send_jupyter_config_comm_request\n",
+ "_send_jupyter_config_comm_request()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eb68879d-71f3-483e-97e1-09cbaccbecaa",
+ "metadata": {},
+ "source": [
+ "___\n",
+ " NOTE: additional step may be needed for public HISE: \n",
+ " - download repo or datasets locally \n",
+ "___ "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16332d3a-60c0-4957-803a-7c0c57c71b99",
+ "metadata": {},
+ "source": [
+ "TODO: \n",
+ "- ask users format of dataset.\n",
+ " - convert files (.csv -> Rda)\n",
+ " \n",
+ "- single-cell (H5 only) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27b5ca5f-e3f0-44ee-90b8-4c7ea85d69d8",
+ "metadata": {},
+ "source": [
+ "___ "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a7381c65-2a25-4504-8585-d110ccb65d0c",
+ "metadata": {},
+ "source": [
+ "#### Run App \n",
+ "\n",
+ " This function allows you to perform analysis of longitudinal dataset. It requires longitudinal data matrix/data frame and annotation file.\n",
+ " \n",
+ "The following are information about all the arguments you can pass in the below cell-block: \n",
+ "\n",
+ "- metadata : str - A filepath to an annotation table. Table must consist column Sample (Participant sample name), PTID (Participant), Time (longitudinal time points)\n",
+ "- data : str - A filepath for an expression matrix or data frame. Rows represents gene/proteins column represents participant samples (same as annotation table Sample column). For single cell, Single cell RNA Seurat object, if datatype is single cell RNA and Single cell ATAC genescore matrix or data frame\n",
+ "- datatype : str - Data input can be bulk or singlecell\n",
+ "- feature_set : str - Variance analysis carried out on the featureSet provided such as c(\"PTID\", \"Time\", \"Sex\")\n",
+ "- mean_threshold : float - Average expression threshold to filter lowly expressed genes Default is 0.1 (log2 scale)\n",
+ "- cv_threshold : float - Coefficient of variation threshold to select variable and stable genes Default is 10 for single cell RNA (100*SD/mean)\n",
+ "- na_threshold : float - Number of NAs in data (numeric value or NULL). Default, 40% * number of columns.\n",
+ "- do_outlier : bool - Whether to perform outlier analysis (TRUE or FALSE). Default FALSE\n",
+ "- z_cutoff : float - |Z| cutoff threshold to find potential outliers (Eg. z_cutoff= 2, equals to Mean/SD 2)\n",
+ "- output_dir : str - User-defined output directory\n",
+ "\n",
+ " \n",
+ "\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "c1c63f4f-9d0b-4846-bbc7-b65552945b2e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'diskcache'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/home/jupyter/PALM/dash_app/app.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdash\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdependencies\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mState\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdiskcache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdash\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong_callback\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDiskcacheLongCallbackManager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdash_bootstrap_components\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdbc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'diskcache'"
+ ]
+ }
+ ],
+ "source": [
+ "%run app.py --metadata /home/jupyter/PALM_DASHv2/PALM/data/data_Metadata.Rda \\\n",
+ " --data /home/jupyter/PALM_DASHv2/PALM/data/Olink_NPX_log2_Protein.Rda \\\n",
+ " --datatype bulk \\\n",
+ " --do_outlier True \\\n",
+ " --z_cutoff 2 \\\n",
+ " --mean_threshold 1 \\\n",
+ " --cv_threshold 5 \\\n",
+ " --na_threshold 0.4 \\\n",
+ " --feature_set PTID Time \\\n",
+ " --output_dir /home/jupyter \\\n",
+ " --method spearman"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "653ea0cf-25a0-4856-b19e-8ecd2ce1b76c",
+ "metadata": {},
+ "source": [
+ "_____\n"
+ ]
+ }
+ ],
+ "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": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dash_app/app.py b/dash_app/app.py
new file mode 100644
index 0000000..a70e2f9
--- /dev/null
+++ b/dash_app/app.py
@@ -0,0 +1,1668 @@
+# data manipulation
+import pandas as pd
+import numpy as np
+
+# debugging
+import os
+import time
+
+# dash stuff
+from turtle import width
+from dash import Dash, html, dcc
+import dash
+from dash import dcc
+from dash import html
+import dash_bootstrap_components as dbc
+from dash.dependencies import Input, Output, State
+from dash.exceptions import PreventUpdate
+
+# plotting
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import plotly.io as pio
+
+# custom prep module
+import rpy2.robjects as robjects
+from rpy2.robjects.packages import importr
+# debugging
+import pdb
+import os
+import time
+
+# bootstrap - style sheet
+external_stylesheets = [
+ 'https://storage.googleapis.com/aifi-static-assets/custom2.css'
+]
+
+app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
+app.config['suppress_callback_exceptions'] = True
+
+# Create server variable with Flask server object for use with gunicorn
+server = app.server
+
+THISCOLORSCALE = px.colors.qualitative.Dark2
+pio.templates.default = "plotly_white"
+
+# HISE colors
+# exported from custom 2
+colors_aifi = {
+ "--bs-blue": "#0d6efd",
+ "--bs-indigo": "#6610f2",
+ "--bs-purple": "#6f42c1",
+ "--bs-pink": "#d63384",
+ "--bs-red": "#dc3545",
+ "--bs-orange": "#fd7e14",
+ "--bs-yellow": "#ffc107",
+ "--bs-green": "#198754",
+ "--bs-teal": "#20c997",
+ "--bs-cyan": "#0dcaf0",
+ "--bs-white": "#FFFFFF",
+ "--bs-gray": "#6c757d",
+ "--bs-gray-dark": "#343a40",
+ "--bs-gray-100": "#f8f9fa",
+ "--bs-gray-200": "#e9ecef",
+ "--bs-gray-300": "#dee2e6",
+ "--bs-gray-400": "#ced4da",
+ "--bs-gray-500": "#adb5bd",
+ "--bs-gray-600": "#6c757d",
+ "--bs-gray-700": "#495057",
+ "--bs-gray-800": "#343a40",
+ "--bs-gray-900": "#212529",
+ "--bs-hise-blue-1": "#003056",
+ "--bs-hise-blue-2": "#325876",
+ "--bs-hise-blue-3": "#5286b0",
+ "--bs-hise-blue-4": "#71899c",
+ "--bs-hise-blue-5": "#b4c3cf",
+ "--bs-hise-blue-6": "#d9e0e6",
+ "--bs-hise-teal-1": "#33B0C8",
+ "--bs-hise-teal-2": "#76CFE0",
+ "--bs-hise-teal-3": "#DEF2F6",
+ "--bs-hise-grey-1": "#272D3B",
+ "--bs-hise-grey-2": "#3E3E3E",
+ "--bs-hise-grey-3": "#616161",
+ "--bs-hise-grey-4": "#707070",
+ "--bs-hise-grey-5": "#ECEDEE",
+ "--bs-hise-grey-6": "#FBFBFB",
+ "--bs-hise-green-1": "#E3EADA",
+ "--bs-hise-green-2": "#A0C572",
+ "--bs-hise-green-3": "#94BC62",
+ "--bs-hise-green-4": "#4AD991",
+ "--bs-aifi-new-001": "#003057",
+ "--bs-aifi-new-002": "#5da7e5",
+ "--bs-aifi-new-003": "#74A03E",
+ "--bs-aifi-new-004": "#f4a261",
+ "--bs-aifi-new-005": "#e76f51",
+ "--bs-aifi-new-006": "#FFFFD0"
+}
+
+colors = {
+ 'aiwhite': 'rgb(64, 85, 100)',
+ 'aiblue': 'rgb(0, 48, 87)',
+ 'aigreen': 'rgb(120, 156, 74)',
+ 'aiorange': 'rgb(238, 118, 35)',
+ 'aidarkorange': 'rgb(176, 101, 51)',
+ 'aigrey': 'rgb(124, 125, 127)',
+ 'ailightgrey': 'rgb(231, 230, 230)',
+}
+
+##############################
+# --- correlation matrix --- #
+##############################
+
+
+@app.callback(Output('corr-matrix', 'data'),
+ Output('corr-fig', 'figure'),
+ Input('corr-dropdown', 'value'),
+ State('input-data', 'data'),
+ prevent_initial_call=True)
+def correlation_matrix_plot(correlation_method, data_input):
+ datamatrix = pd.read_json(data_input, orient='split')
+ cols = datamatrix.columns.tolist()
+ cor_res = datamatrix.corr(method=correlation_method)
+ data_input = cor_res[cols].to_numpy().transpose()
+ fig = go.Figure(
+ go.Heatmap(z=data_input,
+ colorscale=[[0, 'white'], [0.5, 'rgb(250, 138, 130)'],
+ [1.0, 'rgb(255, 17, 0)']],
+ x=cols,
+ y=cols,
+ dx=1,
+ dy=1))
+ return (pd.DataFrame(data_input).to_json(orient='split'), fig)
+
+
+@app.callback(Output('download-corr-df', 'data'),
+ Input('corr_btn', 'n_clicks'), State('corr-matrix', 'data'))
+def download_correlation(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv,
+ 'correlation_matrix_viz_data.csv')
+
+
+#########################
+# --- Outlier Plots --- #
+#########################
+
+
+@app.callback(Output('download-outlier-df', 'data'),
+ Input('outlier_dl_button', 'n_clicks'),
+ State('outlier-input-state', 'data'))
+def download_outlier_data(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ return dcc.send_data_frame(dff.to_csv, 'outlier_viz_data.csv')
+
+
+def prep_outlierP_data(outlier_df, z_cutoff, z_score_subset, nGenes, groupby):
+ '''
+ '''
+ # load custom function to calcualte p-values
+ robjects.r.source('{}/PALMO/R/outlierDetectP.R'.format(os.getcwd()))
+ p_value_for_event = robjects.r['p_value_for_event']
+ rpnorm = robjects.r['pnorm']
+ rate = 1 - rpnorm(z_cutoff)[0]
+ if z_score_subset == 'above':
+ data = outlier_df.loc[outlier_df['z'] >= z_cutoff, ]
+ elif z_score_subset == 'below':
+ data = outlier_df.loc[outlier_df['z'] <= -z_cutoff, ]
+ else:
+ data = outlier_df.loc[np.abs(outlier_df['z']) >= z_cutoff, ]
+ rate = 2 * (1 - rpnorm(z_cutoff)[0])
+
+ # create a data.frame with freq #'s
+ # first, create a table and convert to a data.frame
+ tab = data.groupby([groupby, 'Time']).size()
+ df = tab.unstack().reset_index()
+ df = pd.wide_to_long(df, stubnames="W", i='PTID', j='Time').reset_index()
+
+ # now do some cleaning; and fill NAs
+ df['tmp'] = 'W'
+ df['Time'] = df['Time'].astype(str)
+ df['Time'] = df[['tmp', 'Time']].agg(''.join, axis=1)
+ df.fillna(value=0, inplace=True)
+
+ # rename and drop columns
+ df.rename(columns={groupby: 'Sample', 'W': 'Freq'}, inplace=True)
+ df.drop(columns='tmp', inplace=True)
+ df['pvals'] = p_value_for_event(events=df['Freq'].tolist(),
+ tries=nGenes,
+ rate=rate)
+ df['signP'] = -np.log10(df['pvals'])
+
+ # create an id column
+ df['id'] = df[['Sample', 'Time']].agg(''.join, axis=1)
+ df.sort_values(by=['Time', 'Sample'], inplace=True)
+
+ return df
+
+
+@app.callback(Output('outlier-input-state', 'data'),
+ Input('input-outlier', 'data'),
+ State('z-submit', 'n_clicks'),
+ [State('sample-selector', 'value')],
+ prevent_initial_call=True)
+def run_and_cache_outlier_input(data_input, n_clicks, select_samples):
+ outlier_res_py = pd.read_json(data_input, orient='split')
+ if n_clicks is None:
+ return outlier_res_py.to_json(date_format='iso', orient='split')
+ else:
+ outlier_res_py = outlier_res_py.loc[
+ outlier_res_py['Sample'].isin(select_samples), ]
+ return outlier_res_py.to_json(date_format='iso', orient='split')
+
+
+@app.callback(Output('number-features-bar', 'figure'),
+ Input('outlier-input-state', 'data'),
+ State('params-z-score', 'value'),
+ [State('sample-selector', 'value')])
+def plot_no_features(df, z_cutoff, select_samples):
+ '''
+ '''
+ # create column for number of features
+ nofeatures_df = pd.read_json(df, orient='split')
+ nofeatures_df['no_features'] = 1
+ nofeatures_df.loc[nofeatures_df['z'] > 0, 'zgroup'] = '> Z'
+ nofeatures_df.loc[nofeatures_df['z'] < 0, 'zgroup'] = '< -Z'
+
+ nofeatures_df = nofeatures_df.groupby(
+ ['Sample', 'zgroup'])['no_features'].sum().reset_index()
+
+ if select_samples is not None:
+ nofeatures_df = nofeatures_df.loc[
+ nofeatures_df['Sample'].isin(select_samples), ]
+ nofeat = px.bar(nofeatures_df,
+ x='Sample',
+ y='no_features',
+ color='zgroup',
+ width=500,
+ barmode='group',
+ text='no_features',
+ color_discrete_sequence=THISCOLORSCALE)
+ nofeat.update_traces(texttemplate='%{text:.2s}', textposition='outside')
+ return nofeat
+
+
+@app.callback(Output('p-scatter', 'figure'),
+ Input('outlier-input-state', 'data'),
+ Input('z-score-cutoff', 'value'), State('params-z-score',
+ 'value'),
+ [State('sample-selector', 'value')])
+def outlier_p_scatter_plot(data_input, z_subset, z_score_cutoff,
+ select_samples):
+ '''
+ '''
+ d_input = pd.read_json(data_input, orient='split')
+ input_df = prep_outlierP_data(d_input,
+ z_cutoff=z_score_cutoff,
+ z_score_subset=z_subset,
+ nGenes=1042,
+ groupby="PTID")
+ if select_samples is not None:
+ input_df = input_df.loc[input_df['id'].isin(select_samples), ]
+ fig = px.scatter(input_df,
+ x='Freq',
+ y='signP',
+ hover_name='id',
+ labels={
+ 'Freq': '# Features',
+ 'signP': '-log10(p-value)'
+ },
+ color_discrete_sequence=THISCOLORSCALE)
+ return fig
+
+
+@app.callback(Output('p-bar', 'figure'), Input('outlier-input-state', 'data'),
+ Input('z-score-cutoff', 'value'), State('params-z-score',
+ 'value'),
+ [State('sample-selector', 'value')])
+def outlier_p_bar_plot(data_input, z_subset, z_score_cutoff, select_samples):
+ '''
+ '''
+ d_input = pd.read_json(data_input, orient='split')
+ input_df = prep_outlierP_data(d_input,
+ z_cutoff=z_score_cutoff,
+ z_score_subset=z_subset,
+ nGenes=1042,
+ groupby="PTID")
+ input_df['sample_time'] = input_df[['Sample', 'Time']].agg(''.join, axis=1)
+ input_df.sort_values(by=['sample_time'], inplace=True)
+
+ if select_samples is not None:
+ input_df = input_df.loc[input_df['sample_time'].isin(select_samples), ]
+ fig = px.bar(input_df,
+ x='sample_time',
+ y='signP',
+ labels={
+ 'sample_time': 'Sample',
+ 'signP': '-log10(pvalue)'
+ },
+ color_discrete_sequence=THISCOLORSCALE)
+ return fig
+
+
+@app.callback(Output('outlier-plot', 'figure'), Input('center-measure',
+ 'value'),
+ Input('outlier-input-state', 'data'),
+ State('params-z-score', 'value'),
+ [State('sample-selector', 'value')])
+def outlier_detect_plot(measure_col, data_input, z_cut_off, these_samples):
+ '''
+ TODO: order the samples correctly
+ '''
+ # sort values
+ d_input = pd.read_json(data_input, orient='split')
+ d_input.sort_values(by='Sample', inplace=True)
+ if these_samples is not None:
+ d_input = d_input.loc[d_input['Sample'].isin(these_samples), ]
+
+ out_fig = go.Figure()
+ d_input.loc[d_input['z'] > 0, 'zgroup'] = '> Z'
+ d_input.loc[d_input['z'] < 0, 'zgroup'] = '< -Z'
+ z_group_list = list(d_input['zgroup'].unique())
+ z_group_list.sort()
+ for this_group in z_group_list:
+ violin_input = d_input.loc[
+ d_input['zgroup'].eq(this_group), ].sort_values(by='Sample')
+ out_fig.add_trace(
+ go.Violin(
+ x=violin_input['Sample'],
+ y=violin_input[measure_col],
+ legendgroup=this_group,
+ name=this_group,
+ box_visible=True,
+ spanmode='hard',
+ opacity=0.6,
+ ))
+
+ out_fig.update_traces(width=0.5)
+ sample_order = d_input['Sample'].unique().tolist()
+ sample_order.sort()
+ out_fig.update_xaxes(categoryorder='array', categoryarray=sample_order)
+ return out_fig
+
+
+@app.callback(Output('sample-selector', 'options'),
+ Input('input-outlier', 'data'))
+def define_sample_options(outlier_data):
+ outlier = pd.read_json(outlier_data, orient='split')
+ sample_list = outlier['Sample'].unique().tolist()
+ sample_list.sort()
+ return sample_list
+
+
+##################################
+# --- Expression Level Plots --- #
+##################################
+
+
+@app.callback(Output('this-gene-scrna', 'options'),
+ Input('this-gene-scrna', 'value'), State('var-decomp', 'data'))
+def define_gene_options(this_gene, var_decomp):
+ var_decomp = pd.read_json(var_decomp, orient='split')
+ gene_list = var_decomp['Gene'].unique().tolist()
+ return gene_list
+
+
+@app.callback(Output('download-scrna-exp-df', 'data'),
+ Input('scrna_exp_dl_btn', 'n_clicks'),
+ State('scrna-subset-data', 'data'))
+def download_scrna_exp(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'scrna_exp_data.csv')
+
+
+@app.callback(Output('scrna_exp_gene_plot', 'figure'),
+ Output('scrna-subset-data', 'data'),
+ Input('this-gene-scrna', 'value'), State('input-data', 'data'),
+ State('input-metadata', 'data'))
+def plot_scrna_gene_exp(this_gene, input_data, input_metadata):
+ data = pd.read_json(input_data, orient='split')
+ metadata = pd.read_json(input_metadata, orient='split')
+ this_gene_vals = data.loc[data.index == this_gene, ].values
+ metadata['exp'] = list(this_gene_vals[0])
+
+ # plot
+ gfig = px.strip(metadata,
+ x='PTID',
+ y='exp',
+ color='Time',
+ color_discrete_sequence=THISCOLORSCALE)
+ boxf = px.box(metadata,
+ x='PTID',
+ y='exp',
+ color_discrete_sequence=THISCOLORSCALE)
+ combined_plot = go.Figure(data=boxf.data + gfig.data)
+ return combined_plot, metadata.to_json(orient='split')
+
+
+@app.callback(
+ Output('scrna_exp_gene_time_plot', 'figure'),
+ Input('this-gene-scrna', 'value'),
+ State('input-data', 'data'),
+ State('input-metadata', 'data'),
+)
+def plot_scrna_gene_bytime(this_gene, input_data, input_metadata):
+ data = pd.read_json(input_data, orient='split')
+ metadata = pd.read_json(input_metadata, orient='split')
+ this_gene_vals = data.loc[data.index == this_gene, ].values
+ metadata['exp'] = list(this_gene_vals[0])
+ fig = px.strip(metadata,
+ x='PTID',
+ y='exp',
+ color='Time',
+ facet_col='Time',
+ color_discrete_sequence=THISCOLORSCALE)
+ fig.for_each_annotation(
+ lambda a: a.update(text="")) # remove annotation from each subplot
+ return fig
+
+
+@app.callback(Output('download-expression-df', 'data'),
+ Input('expression_dl_button', 'n_clicks'),
+ State('expression-out', 'data'))
+def download_outlier_data(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ return dcc.send_data_frame(dff.to_csv, 'expression_lvl_viz_data.csv')
+
+
+@app.callback(Output('expression-out', 'data'),
+ Input('this-gene', 'value'),
+ State('input-data', 'data'),
+ State('input-metadata', 'data'),
+ prevent_initial_call=True)
+def store_gene_data(this_gene, input_data, input_metadata):
+ ''' Caches and stores data for callbacks
+ '''
+ datamatrix = pd.read_json(input_data, orient='split')
+ metadata = pd.read_json(input_metadata, orient='split')
+ this_gene_vals = datamatrix[datamatrix.index == this_gene].values
+ ann = metadata.copy(deep=True)
+ ann['exp'] = list(this_gene_vals[0])
+ return ann.to_json(orient='split')
+
+
+@app.callback(Output('var-gene-plot', 'figure'), Input('expression-out',
+ 'data'))
+def var_gene_plot(df):
+ '''
+ '''
+ gene_df = pd.read_json(df, orient='split')
+ gfig = px.scatter(gene_df,
+ x='PTID',
+ y='exp',
+ symbol='Time',
+ color='Time',
+ color_discrete_sequence=THISCOLORSCALE)
+ boxf = px.box(gene_df,
+ x='PTID',
+ y='exp',
+ color_discrete_sequence=THISCOLORSCALE)
+ combined_plot = go.Figure(data=boxf.data + gfig.data)
+ return combined_plot
+
+
+@app.callback(Output('geneplot', 'figure'), Input('expression-out', 'data'))
+def geneplot(df):
+ '''
+ '''
+ ann = pd.read_json(df, orient='split')
+ fig = px.scatter(ann,
+ x='PTID',
+ y='exp',
+ color='Time',
+ facet_col='Time',
+ color_discrete_sequence=THISCOLORSCALE)
+ fig.for_each_annotation(
+ lambda a: a.update(text="")) # remove annotation from each subplot
+ return fig
+
+
+@app.callback(Output('this-gene', 'options'), Input('var-decomp', 'data'))
+def define_gene_options(var_decomp):
+ var_decomp = pd.read_json(var_decomp, orient='split')
+ gene_list = var_decomp['Gene'].unique().tolist()
+ return gene_list
+
+
+##########################
+# --- Variance Plots --- #
+##########################
+
+
+@app.callback(Output('download-scrna-variance', 'data'),
+ Input('scrna_variance_dl_btn', 'n_clicks'),
+ State('scrna_gene_subset_output', 'data'))
+def download_scrna_variance(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'scrna_variance_data.csv')
+
+
+@app.callback(Output('scrna_variance_decomp', 'figure'),
+ Input('var-decomp', 'data'))
+def scrna_variance_decomp_plot(var_input):
+ var_decomp = pd.read_json(var_input, orient='split')
+ var_decomp.index = var_decomp['Gene']
+ var_decomp = pd.melt(var_decomp,
+ id_vars='Gene',
+ value_vars=['PTID', 'Time', 'Residual', 'celltype'])
+ feature_list = var_decomp['variable'].unique().tolist()
+ fig = go.Figure()
+ for f in feature_list:
+ fig.add_trace(
+ go.Violin(x=var_decomp.loc[var_decomp['variable'].eq(f),
+ 'variable'],
+ y=var_decomp.loc[var_decomp['variable'].eq(f), 'value'],
+ name=f,
+ box_visible=True,
+ opacity=0.6,
+ spanmode='hard',
+ showlegend=True))
+ fig.update_traces(width=0.5)
+ fig.update_layout(legend_title_text='featureList')
+ return fig
+
+
+@app.callback(Output('scrna_features_decomp', 'figure'),
+ Output('scrna_gene_subset_output', 'data'),
+ Input('var-decomp', 'data'),
+ Input('var-select-btn', 'n_clicks'),
+ State('gene-dropdown-select', 'value'),
+ prevent_initial_call=True)
+def scrna_features_decomp_plot(var_input, clicks, var_chosen):
+ var_decomp = pd.read_json(var_input, orient='split')
+ var_decomp.index = var_decomp['Gene']
+ var_decomp = pd.melt(var_decomp,
+ id_vars='Gene',
+ value_vars=['PTID', 'Time', 'Residual', 'celltype'])
+ donors_sorted = var_decomp.loc[var_decomp['variable'].eq('donor'), ]
+ if clicks is not None:
+ var_decomp = var_decomp.loc[var_decomp['Gene'].isin(var_chosen), ]
+ else:
+ # choose some default genes - top 15
+ top_vars = var_decomp.sort_values(
+ by='value', ascending=False)[0:15]['Gene'].unique().tolist()
+ var_decomp = var_decomp.loc[var_decomp['Gene'].isin(top_vars), ]
+ donors_sorted.sort_values(by='value', ascending=False, inplace=True)
+ var_fig = px.bar(var_decomp,
+ x='value',
+ y='Gene',
+ color='variable',
+ orientation='h',
+ category_orders={
+ 'Gene': donors_sorted['Gene'].tolist(),
+ 'variable': ['PTID', 'Time', 'celltype', 'Residual']
+ },
+ labels={
+ 'Gene': 'Features',
+ 'value': '% variance explained'
+ },
+ color_discrete_sequence=THISCOLORSCALE)
+ var_fig.update_traces().update_layout(title_x=0.5,
+ legend_title_text='FeatureList')
+ return var_fig, var_decomp.to_json(orient='split')
+
+
+def prep_var_contribute_df(tbl: pd.DataFrame, mean_threshold):
+ ''' Takes table of residuals and does some formatting and subsetting
+ '''
+ featureList = ['PTID', 'Time'] + ['Residual']
+ res_tbl = tbl.loc[tbl['max'] > mean_threshold, featureList]
+ # res_tbl.sort_values(by='PTID', ascending=False, inplace=True)
+
+ # reshape
+ res_tbl['genes'] = res_tbl.index
+ var_df = pd.melt(res_tbl, id_vars='genes', value_vars=featureList)
+ return var_df
+
+
+@app.callback(Output('variance-out', 'data'),
+ Input('feature-button', 'n_clicks'),
+ State('var-decomp', 'data'),
+ State('params-mean', 'value'),
+ [State('gene-dropdown-select', 'value')],
+ prevent_initial_call=True)
+def subset_var_df(n, input_var, mean_threshold, var_chosen):
+ '''
+ '''
+ lmem_py = pd.read_json(input_var, orient='split')
+ var_df = prep_var_contribute_df(lmem_py, mean_threshold)
+ if (n == 0):
+ ptid_entries = var_df.loc[var_df['variable'].eq('PTID'), ].copy(
+ deep=True)
+ top_vars = ptid_entries.sort_values(
+ by='value', ascending=False)[0:15]['genes'].unique().tolist()
+ dff = var_df.loc[var_df['genes'].isin(top_vars), ]
+ elif n > 0:
+ dff = var_df[var_df['genes'].isin(var_chosen)]
+ return dff.to_json(orient='split')
+
+
+@app.callback(Output('variance-contribution', 'figure'),
+ Input('variance-out', 'data'),
+ suppress_callback_exceptions=True,
+ prevent_initial_callback=False)
+def var_contribute_plot(dff):
+ ''' separate function that renames and subsets data
+ '''
+ dff = pd.read_json(dff, orient='split')
+ # sort by donor
+ donors_sorted = dff.loc[dff['variable'].eq('PTID'), ]
+ donors_sorted.sort_values(by='value', ascending=False, inplace=True)
+ var_fig = px.bar(
+ dff,
+ x='value',
+ y='genes',
+ color='variable',
+ orientation='h',
+ category_orders={'genes': donors_sorted['genes'].tolist()},
+ labels={
+ 'genes': 'Features',
+ 'value': '% variance explained'
+ },
+ color_discrete_sequence=THISCOLORSCALE)
+ var_fig.update_traces().update_layout(title_x=0.5,
+ legend_title_text='FeatureList')
+ return (var_fig)
+
+
+@app.callback(Output('violin-box-var-plot', 'figure'),
+ Input('var-decomp', 'data'), State('params-mean', 'value'))
+def violin_box_plot(input_var, mean_threshold):
+ ''' creates violin with box plot overlayed
+ TODO: plot sigFeature datapoints
+ '''
+ lmem_py = pd.read_json(input_var, orient='split')
+ featureList = ['PTID', 'Time'] + ['Residual']
+ res = lmem_py.loc[lmem_py['max'] > mean_threshold, featureList]
+ res['genes'] = res.index
+ df_py = pd.melt(res,
+ id_vars='genes',
+ value_vars=['PTID', 'Time', 'Residual'])
+ df_py['feature'] = df_py[['genes', 'variable']].agg('_'.join, axis=1)
+
+ # list features
+ feature_list = df_py['variable'].unique().tolist()
+ fig = go.Figure()
+ for f in feature_list:
+ fig.add_trace(
+ go.Violin(x=df_py.loc[df_py['variable'].eq(f), 'variable'],
+ y=df_py.loc[df_py['variable'].eq(f), 'value'],
+ name=f,
+ box_visible=True,
+ opacity=0.6,
+ spanmode='hard',
+ showlegend=True))
+ fig.update_traces(width=0.5)
+ fig.update_layout(legend_title_text='featureList')
+
+ return fig
+
+
+@app.callback(Output('download-var-df', 'data'), Input('var_btn', 'n_clicks'),
+ State('variance-out', 'data'))
+def download_var_test(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'variance_viz_data.csv')
+
+
+##################################
+# --- Intra-donor variations --- #
+##################################
+
+
+@app.callback(Output('download-scrna-cv-gene-df', 'data'),
+ Input('scrna_cv_gene_btn', 'n_clicks'),
+ State('scrna_stablevar_output', 'data'))
+def download_scrna_cv_gene(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'scrna_cv_gene_data.csv')
+
+
+@app.callback(Output('cvprof-hg-fig', 'figure'), Input('input-cvprof', 'data'))
+def scrna_hg_cvprofile(input_data):
+ cv_input = pd.read_json(input_data, orient='split')
+ housekeeping_genes = ['GAPDH', 'ACTB']
+ housekeep_input = cv_input.loc[cv_input['gene'].isin(housekeeping_genes), ]
+ fig = px.scatter(housekeep_input,
+ x='mean',
+ y='CV',
+ facet_col='gene',
+ color_discrete_sequence=THISCOLORSCALE)
+ return fig
+
+
+@app.callback(Output('this-gene-intra', 'options'),
+ Input('input-cvprof', 'data'))
+def define_group_options(cvprof):
+ cvprof = pd.read_json(cvprof, orient='split')
+ gene_list = cvprof['group'].unique().tolist()
+ return gene_list
+
+
+@app.callback(Output('scrna_cv_profile', 'figure'),
+ Input('input-cvprof', 'data'),
+ Input('this-gene-intra', 'value'),
+ State('params-cv', 'value'),
+ prevent_initial_call=True)
+def scrna_cv_profile_plot(input_cv, this_gene, cv_threshold):
+ cv_input = pd.read_json(input_cv, orient='split')
+ housekeeping_genes = ['GAPDH', 'ACTB']
+ var_genes = cv_input.loc[cv_input['is_var_gene'].eq(1), ]
+ stable_genes = cv_input.loc[cv_input['is_var_gene'].eq(0), ]
+ fig = go.Figure()
+ fig.add_trace(
+ go.Scatter(mode='markers',
+ name='variable_genes',
+ x=var_genes.loc[var_genes['group'].eq(this_gene), 'mean'],
+ y=var_genes.loc[var_genes['group'].eq(this_gene), 'CV'],
+ marker={'color': 'black'}))
+
+ fig.add_trace(
+ go.Scatter(mode='markers',
+ name='non_variable_genes',
+ x=stable_genes.loc[stable_genes['group'].eq(this_gene),
+ 'mean'],
+ y=stable_genes.loc[stable_genes['group'].eq(this_gene),
+ 'CV']))
+ # add horizontal line
+ fig.add_hline(y=cv_threshold)
+
+ fig.add_trace(
+ go.Scatter(mode='markers',
+ name='housekeeping_genes',
+ x=cv_input.loc[cv_input['group'].isin(housekeeping_genes),
+ 'mean'],
+ y=cv_input.loc[cv_input['group'].isin(housekeeping_genes),
+ 'CV'],
+ marker={'color': 'blue'}))
+
+ return fig
+
+
+def prep_cvcalc_bulk(meanThreshold, cvThreshold, datamatrix, metadata):
+ ''' Intro-donor variations over time
+
+ TODO: label top 10 genes, and most stable ones
+ '''
+ mat = datamatrix
+ ann = metadata
+
+ unigene = mat.index.tolist()
+ uniSample = ann['PTID'].unique().tolist()
+
+ variable_gene = pd.DataFrame()
+ stable_gene = pd.DataFrame()
+
+ for i in list(range(0, len(uniSample))): #len(uniSample))):
+ uS = uniSample[i]
+ meta_df = ann.loc[ann['PTID'].eq(uS), ]
+ if len(meta_df) > 1:
+ samples = meta_df['Sample'].unique().tolist()
+
+ df = mat.loc[mat.index.isin(unigene), samples]
+
+ # column of how many NAs exist
+ df['NAs'] = df.isna().sum(axis=1)
+
+ # mean column (rowwise calc)
+ df['mean'] = df[samples].mean(axis=1)
+
+ # row-wise sd
+ df['sd'] = df[samples].std(axis=1)
+
+ # row-wise var
+ df['var'] = df[samples].var(axis=1)
+
+ df['CV'] = (100 * df['sd']) / df['mean']
+
+ cutoff = 0.5 * len(meta_df)
+ dp1 = df.copy(deep=True)
+ dp1 = dp1.loc[dp1['NAs'] <= cutoff, ]
+ dp1 = dp1.loc[np.abs(dp1['mean']) >= meanThreshold, ]
+
+ dp2a = dp1.loc[np.abs(dp1['CV']) > cvThreshold, ]
+ if len(dp2a) > 0:
+ # subset to just the first 10 labels
+ dp2a.sort_values(by=['CV', 'mean'],
+ key=abs,
+ inplace=True,
+ ascending=False)
+ if len(dp2a) > 10:
+ dp2a_label = dp2a[0:10]
+ variable_gene = pd.concat(
+ [variable_gene, dp2a[['mean', 'sd', 'var', 'CV']]], axis=1)
+ variable_gene['donor'] = uS
+ variable_gene['gene'] = variable_gene.index
+
+ dp2b = dp1.loc[np.abs(dp1['CV']) <= cvThreshold, ]
+ if len(dp2b) > 0:
+ dp2b.sort_values(by=['mean', 'CV'],
+ key=abs,
+ inplace=True,
+ ascending=False)
+ stable_gene = pd.concat(
+ [stable_gene, dp2b[['mean', 'sd', 'var', 'CV']]], axis=1)
+ stable_gene['donor'] = uS
+ stable_gene['gene'] = stable_gene.index
+
+ temp = pd.DataFrame(data={'var': df['CV']})
+ temp['gene'] = temp.index
+ temp.rename(columns={'var': '{}_var'.format(uS)}, inplace=True)
+ if (i == 0):
+ res = temp.copy(deep=True)
+ else:
+ res = pd.merge(res, temp, on='gene')
+
+ # remove blanks
+ variable_gene = variable_gene.loc[~variable_gene['donor'].isna(), ]
+ stable_gene = stable_gene.loc[~stable_gene['donor'].isna(), ]
+
+ res_var = res.copy(deep=True)
+ res_var.index = res_var['gene'].values
+ res_var.columns = res_var.columns.str.rstrip('_var')
+
+ uniq_sample = list(
+ set(uniSample).intersection(set(res_var.columns.tolist())))
+
+ res_var['Mean'] = res_var[uniq_sample].mean(axis=1)
+ res_var['Median'] = res_var[uniq_sample].median(axis=1)
+ return res_var
+
+
+@app.callback(Output('cv-density', 'figure'), Input('gene-df', 'data'),
+ Input('input-metadata', 'data'))
+def cv_density_plot(gene_df, metadata_input):
+ '''
+ '''
+ metadata = pd.read_json(metadata_input, orient='split')
+ dff = pd.read_json(gene_df, orient='split')
+ uniSample = metadata['PTID'].unique().tolist()
+ bar_plot_input = pd.wide_to_long(dff, stubnames='PTID', i='gene',
+ j='var').reset_index()
+
+ fig = px.histogram(bar_plot_input,
+ x='PTID',
+ labels={
+ 'PTID': 'CV'
+ },
+ color_discrete_sequence=THISCOLORSCALE).update_layout(
+ yaxis_title='density')
+ return fig
+
+
+@app.callback(Output('cvplot', 'figure'),
+ Output('gene-df', 'data'),
+ Input('gene-type', 'value'),
+ Input('input-metadata', 'data'),
+ Input('input-data', 'data'),
+ State('params-mean', 'value'),
+ State('params-cv', 'value'),
+ prevent_initial_call=True)
+def cvplot(gene_type, metadata_input, matrix_input, mean_threshold,
+ cv_threshold):
+ datamatrix = pd.read_json(matrix_input, orient='split')
+ metadata = pd.read_json(metadata_input, orient='split')
+ mat_input = prep_cvcalc_bulk(mean_threshold, cv_threshold, datamatrix,
+ metadata) # for now, just use the default
+
+ if (gene_type == 'variable'):
+ mat = mat_input.sort_values(by='Median', ascending=False)
+ mat = mat.loc[np.abs(mat['Median']) > cv_threshold, ]
+ col_scale = [[0, 'rgb(210, 201, 242)'], [0.1, 'rgb(233, 237, 14)'],
+ [0.2, 'rgb(224, 36, 29)'], [1, 'rgb(82, 22, 20)']]
+ elif (gene_type == 'stable'):
+ mat = mat_input.sort_values(by='Median', ascending=True)
+ mat = mat.loc[np.abs(mat['Median']) <= cv_threshold, ]
+ col_scale = [[0, 'rgb(210, 201, 242)'], [0.3, 'rgb(100, 79, 179)'],
+ [0.7, 'rgb(82, 68, 242)'], [0.9, 'rgb(233, 237, 14)'],
+ [1, 'rgb(82, 22, 20)']]
+
+ # subset to first 50 entries then create heat map
+ mat = mat[0:50]
+ heat_fig = go.Heatmap(
+ z=mat[['PTID1', 'PTID2', 'PTID3', 'PTID4', 'PTID5', 'PTID6']].values,
+ x=['PTID1', 'PTID2', 'PTID3', 'PTID4', 'PTID5', 'PTID6'],
+ y=mat['gene'].unique().tolist(),
+ colorscale=col_scale)
+ d1 = mat['PTID1'].values
+ d2 = mat['PTID2'].values
+ d3 = mat['PTID3'].values
+ d4 = mat['PTID4'].values
+ d5 = mat['PTID5'].values
+ d6 = mat['PTID6'].values
+
+ b1 = go.Box(y=d1)
+ b2 = go.Box(y=d2)
+ b3 = go.Box(y=d3)
+ b4 = go.Box(y=d4)
+ b5 = go.Box(y=d5)
+ b6 = go.Box(y=d6)
+
+ master_fig = make_subplots(rows=2,
+ cols=1,
+ row_heights=[0.2, 0.8],
+ vertical_spacing=0,
+ specs=[[{
+ 'type': 'box'
+ }], [{
+ 'type': 'xy'
+ }]])
+ master_fig.add_traces([b1, b2, b3, b4, b5, b6], rows=1,
+ cols=1).update_layout(showlegend=False)
+ master_fig.update_xaxes(showticklabels=False, row=1)
+ master_fig.add_traces([heat_fig], rows=2, cols=1)
+ return master_fig, mat.to_json(orient='split')
+
+
+@app.callback(Output('download-cv-gene-df', 'data'),
+ Input('cv_gene_btn', 'n_clicks'), State('gene-df', 'data'))
+def download_cv_gene(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'cv_gene_data.csv')
+
+
+#################################
+# --- Stable/Var gene plots --- #
+#################################
+
+
+@app.callback(Output('input_stablevar', 'data'),
+ Output('stablevar-selector', 'options'),
+ Output('stablevar-selector', 'value'),
+ Input('scrna_stablevar_select', 'value'),
+ Input('subset-stablevar', 'n_clicks'),
+ State('input-cvprof', 'data'),
+ [State('stablevar-selector', 'value')])
+def subset_scrna_stablevar(var_or_stable, clicks, data, genes_chosen):
+ input_df = pd.read_json(data, orient='split')
+ ctx = dash.callback_context
+ action = ctx.triggered[0]['prop_id'].split('.')[0]
+ if var_or_stable == "Stable":
+ stable_df = input_df.loc[input_df['is_var_gene'].eq(0), ]
+ selectable_genes = stable_df['gene'].unique().tolist()
+ selectable_genes.sort()
+ if (clicks == 0) or (action == 'scrna_stablevar_select'):
+ return stable_df.to_json(orient='split'), selectable_genes, []
+ else:
+ stable_df = stable_df.loc[stable_df['gene'].isin(genes_chosen), ]
+ return stable_df.to_json(
+ orient='split'), selectable_genes, genes_chosen
+ elif var_or_stable == "Variable":
+ var_df = input_df.loc[input_df['is_var_gene'].eq(1), ]
+ selectable_genes = var_df['gene'].unique().tolist()
+ selectable_genes.sort()
+ if (clicks == 0) or (action == 'scrna_stablevar_select'):
+ return var_df.to_json(orient='split'), selectable_genes, []
+ else:
+ var_df = var_df.loc[var_df['gene'].isin(genes_chosen), ]
+ return var_df.to_json(
+ orient='split'), selectable_genes, genes_chosen
+
+
+@app.callback(Output('scrna_stable_plot', 'figure'),
+ Output('scrna_stablevar_output', 'data'),
+ Input('input_stablevar', 'data'),
+ State('scrna_stablevar_select', 'value'))
+def scrna_variable_gene_plot(dff, is_stable):
+
+ def _calc_top25(gene_df):
+ # remove duplicates
+ top_genes = gene_df.drop_duplicates(subset='gene')
+
+ # then sort by top 25 freqs
+ top_genes.sort_values(by='freq_x', inplace=True, ascending=False)
+ # return list of top genes
+ return top_genes['gene'][0:25].unique().tolist()
+
+ input_df = pd.read_json(dff, orient='split')
+ if is_stable == 'Stable':
+ var_genes = input_df.loc[input_df['is_var_gene'].eq(0), ]
+ # calculate freq
+ var_genes['freq'] = 1
+ freqs = var_genes.groupby(
+ ['gene'])['freq'].sum().reset_index().sort_values(by='freq',
+ ascending=False)
+ var_genes = pd.merge(freqs, var_genes, on='gene')
+ this_col_scale = [[0, 'rgb(210, 201, 242)'], [1, 'rgb(82, 68, 242)']]
+ top_genes = _calc_top25(var_genes)
+ var_genes = var_genes.loc[var_genes['gene'].isin(top_genes), ]
+ var_genes.sort_values(by='donor', inplace=True, ascending=True)
+ fig = go.Figure(data=go.Heatmap(z=var_genes['CV'],
+ x=var_genes['donor'],
+ y=var_genes['gene'],
+ colorscale=this_col_scale))
+ # fig.update_layout(zaxis_title={'title': 'CV'})
+ return fig, var_genes.to_json(orient='split')
+ elif is_stable == "Variable":
+ var_genes = input_df.loc[input_df['is_var_gene'].eq(1), ]
+ var_genes['freq'] = 1
+ freqs = var_genes.groupby(
+ ['gene'])['freq'].sum().reset_index().sort_values(by='freq',
+ ascending=False)
+ var_genes = pd.merge(freqs, var_genes, on='gene')
+ this_col_scale = [[0,
+ 'rgb(210, 201, 242)'], [0.1, 'rgb(233, 237, 14)'],
+ [0.2, 'rgb(224, 36, 29)'], [1, 'rgb(82, 22, 20)']]
+ top_genes = _calc_top25(var_genes)
+ var_genes = var_genes.loc[var_genes['gene'].isin(top_genes), ]
+ var_genes.sort_values(by='donor', inplace=True, ascending=True)
+ fig = go.Figure(data=go.Heatmap(z=var_genes['CV'],
+ x=var_genes['donor'],
+ y=var_genes['gene'],
+ colorscale=this_col_scale))
+ #fig.update_layout(z='CV')
+ return fig, var_genes.to_json(orient='split')
+
+
+######################
+# --- scRNA UMAP --- #
+######################
+
+
+@app.callback(Output('download-umap-df', 'data'),
+ Input('umap_btn', 'n_clicks'), State('umap_input', 'data'))
+def download_cv_gene(n_clicks, dff):
+ '''
+ '''
+ if n_clicks is not None:
+ dff = pd.read_json(dff, orient='split')
+ # create json blob that will hold these values
+ return dcc.send_data_frame(dff.to_csv, 'umap_data.csv')
+
+
+@app.callback(Output('celltype_umap', 'figure'), Output('pc_fig', 'figure'),
+ Input('umap_input', 'data'))
+def plot_umap(dff):
+ umap_input = pd.read_json(dff, orient='split')
+ umap_input.sort_values(by='predicted.celltype.l2.y', inplace=True)
+ # plot umap
+ umap_celltype = px.scatter(umap_input,
+ x='UMAP_1',
+ y='UMAP_2',
+ color='predicted.celltype.l2.y',
+ height=600,
+ labels={'predicted.celltype.l2.y': 'celltype'})
+ umap_celltype.update_layout(xaxis=dict(showgrid=False),
+ yaxis=dict(showgrid=False))
+ umap_celltype.update_traces(marker=dict(size=1.5))
+
+ # plot PC1 PC2
+ pc_fig = px.scatter(umap_input,
+ x='PC_1',
+ y='PC_2',
+ color='predicted.celltype.l2.y',
+ height=600,
+ labels={'predicted.celltype.l2.y': 'celltype'})
+ pc_fig.update_layout(xaxis=dict(showgrid=False),
+ yaxis=dict(showgrid=False))
+ pc_fig.update_traces(marker=dict(size=1.5))
+ return umap_celltype, pc_fig
+
+
+##################################
+# --- Prep and data processing --- #
+##################################
+@app.callback(Output('gene-dropdown-select', 'options'),
+ Input('var-decomp', 'data'))
+def define_gene_options(var_decomp):
+ var_decomp = pd.read_json(var_decomp, orient='split')
+ gene_list = var_decomp['Gene'].unique().tolist()
+ return gene_list
+
+
+@app.callback(Output('output-content', 'children'),
+ Output('input-data', 'data'),
+ Output('input-metadata', 'data'),
+ Output('var-decomp', 'data'),
+ Output('input-cvprof', 'data'),
+ Output('input-outlier', 'data'),
+ Output('umap_input', 'data'), [Input('run_app_btn', 'n_clicks')],
+ State('params-dtype', 'value'),
+ State('params-z-score', 'value'),
+ State('params-mean', 'value'),
+ State('params-cv', 'value'),
+ State('params-na', 'value'),
+ prevent_initial_call=True)
+def render_tabs(click1, dtype, zscore_cutoff, mean_cutoff, cv_cutoff,
+ na_cutoff):
+ """ handles loading in the correct tabs """
+ ctx = dash.callback_context
+ action = ctx.triggered[0]['prop_id'].split('.')[0]
+ if (action == 'run_app_btn') and (click1 is not None):
+ if dtype == 'Bulk Plasma':
+ data = pd.read_csv('{}/data/bulk_datamatrix.csv'.format(
+ os.getcwd()))
+ data.index = data['Unnamed: 0']
+ del data['Unnamed: 0']
+
+ metadata = pd.read_csv('{}/data/bulk_metadata.csv'.format(
+ os.getcwd()))
+ metadata.index = metadata['Unnamed: 0']
+ del metadata['Unnamed: 0']
+
+ var_decomp = pd.read_csv('{}/data/bulk_decomp_var.csv'.format(
+ os.getcwd()))
+ var_decomp.index = var_decomp['Unnamed: 0']
+ del var_decomp['Unnamed: 0']
+
+ cv_res = pd.read_csv('{}/data/bulk_cv.csv'.format(os.getcwd()))
+ cv_res.index = cv_res['Unnamed: 0']
+ del cv_res['Unnamed: 0']
+
+ outlier = pd.read_csv('{}/data/bulk_outlier.csv'.format(
+ os.getcwd()))
+ outlier.index = outlier['Unnamed: 0']
+ del outlier['Unnamed: 0']
+
+ umap = pd.DataFrame()
+ if dtype == 'Single-Cell':
+ try:
+ print('trying')
+ data = pd.DataFrame(
+ ) # pd.read_pickle('{}/data/sc_data.pkl'.format(os.getcwd()))
+ #data.index = data['Unnamed: 0']
+ #del data['Unnamed: 0']
+
+ metadata = pd.read_csv('{}/data/sc_metadata.csv'.format(
+ os.getcwd()))
+ metadata.index = metadata['Unnamed: 0']
+ del metadata['Unnamed: 0']
+
+ var_decomp = pd.read_csv('{}/data/sc_decomp_var.csv'.format(
+ os.getcwd()))
+ var_decomp.index = var_decomp['Unnamed: 0']
+ del var_decomp['Unnamed: 0']
+
+ cv_res = pd.read_csv('{}/data/sc_cv.csv'.format(os.getcwd()))
+ cv_res.index = cv_res['Unnamed: 0']
+ del cv_res['Unnamed: 0']
+
+ outlier = pd.DataFrame()
+ umap = pd.read_csv('{}/data/sc_umap.csv'.format(os.getcwd()),
+ usecols=[
+ 'UMAP_1', 'UMAP_2',
+ 'predicted.celltype.l2.y', 'PC_1',
+ 'PC_2'
+ ]).head(n=100)
+ except:
+ print('woops')
+
+ #(data, metadata, var_decomp, cv_res, outlier,
+ # umap) = run_prep(dtype, zscore_cutoff, mean_cutoff, cv_cutoff,
+ # na_cutoff)
+ # if user doesn't specify z-score: return empty data.frame
+
+ # get gene options
+ tabs = []
+ for num in dtype_dict[dtype]:
+ print(num)
+ content = get_tab_content(dtype, num)
+ if (zscore_cutoff is None) and (num == 'Outliers'):
+ outlier = pd.DataFrame()
+ continue
+ tabs.append(
+ dcc.Tab(label=num,
+ value=num,
+ children=[content],
+ className='default-tab-style',
+ selected_className='tab-selected-style'))
+ # return to some default tab
+ return (dcc.Tabs(id='output-content',
+ value='Correlation',
+ children=tabs,
+ colors={
+ 'border': colors['aiwhite'],
+ 'primary': colors['aiwhite'],
+ 'background': colors['aiblue']
+ }), data.to_json(orient='split'),
+ metadata.to_json(orient='split'),
+ var_decomp.to_json(orient='split'),
+ cv_res.to_json(orient='split'),
+ outlier.to_json(orient='split'), umap.to_json(orient='split'))
+
+
+def run_prep(datatype, zscore_cutoff, mean_cutoff, cv_cutoff, na_cutoff):
+ meta_fpath = get_metapath()
+ (data, metadata, var_decomp, cv_res, outlier,
+ umap) = dpp.run(data_filepath=get_datapath(datatype),
+ metadata_filepath=meta_fpath,
+ datatype=determine_datatype(datatype),
+ z_cutoff=zscore_cutoff,
+ mean_threshold=mean_cutoff,
+ cv_threshold=cv_cutoff,
+ na_threshold=na_cutoff,
+ housekeeping_genes=['GAPDH', 'ACTB'],
+ feature_set=['PTID', 'Time'])
+ return (data, metadata, var_decomp, cv_res, outlier, umap)
+
+
+# structure to determine number/names of tabs
+dtype_dict = {
+ 'Bulk Plasma': [
+ 'Correlation', 'Expression Level', 'Variance', 'Intra-donor Variation',
+ 'Outliers'
+ ],
+ 'Single-Cell':
+ ['Expression Level', 'Variance', 'Intra-donor Variation', 'UMAP']
+}
+
+# some sort of tab handler maps tab-dtype to content
+bulk_tab_content = {
+ 'Correlation':
+ html.Div([
+ html.Br(),
+ html.Button("Download CSV",
+ id='corr_btn',
+ className='button-default-style'),
+ html.H4("Choose the correlation coefficient method",
+ className='header-button-text'),
+ dcc.Dropdown(id='corr-dropdown',
+ options=['pearson', 'spearman'],
+ value='spearman',
+ className='graph-component-align'),
+ dcc.Download(id='download-corr-df'),
+ dcc.Graph(id='corr-fig', className='matrix-graph-style'),
+ ]),
+ 'Expression Level':
+ html.Div([
+ html.Br(),
+ html.Button('Download CSV',
+ id='expression_dl_button',
+ className='button-default-style'),
+ html.H4('Pick gene of interest', className='header-button-text'),
+ dcc.Dropdown(value='ABL1', id='this-gene',
+ className='custom-dropdown'),
+ dcc.Download(id='download-expression-df'),
+ dcc.Graph(id='var-gene-plot', className='side-by-side-graph'),
+ dcc.Graph(id='geneplot', className='side-by-side-graph')
+ ]),
+ 'Variance':
+ html.Div([
+ html.Br(),
+ html.Button("Download CSV",
+ id='var_btn',
+ className='button-default-style'),
+ html.H4("Select feature(s) of interest",
+ className='header-button-text'),
+ dcc.Dropdown(id='gene-dropdown-select',
+ multi=True,
+ className='custom-dropdown'),
+ html.Button(id='feature-button',
+ n_clicks=0,
+ children='Update feature plot',
+ className='button-default-style'),
+ dcc.Download(id='download-var-df'),
+ dcc.Graph(id='variance-contribution'),
+ dcc.Graph(id='violin-box-var-plot'),
+ ]),
+ 'Intra-donor Variation':
+ html.Div([
+ html.Br(),
+ html.Button("Download CSV",
+ id='cv_gene_btn',
+ className='button-default-style'),
+ html.H4("Choose to plot variable or stable genes",
+ className='header-button-text'),
+ dcc.RadioItems(['stable', 'variable'],
+ 'stable',
+ id='gene-type',
+ className='graph-component-align'),
+ dcc.Download(id='download-cv-gene-df'),
+ dcc.Graph(id='cvplot', className='custom-cv-graph'),
+ dcc.Graph(id='cv-density'),
+ ]),
+ 'Outliers':
+ html.Div([
+ html.Br(),
+ html.Button('Download CSV',
+ id='outlier_dl_button',
+ className='button-default-style'),
+ html.H4('Select a sample of interest', className='header-button-text'),
+ dcc.Dropdown(id='sample-selector',
+ multi=True,
+ placeholder='Select samples of interest',
+ className='graph-component-align'),
+
+ # insert button
+ html.Button('Update Plots',
+ id='z-submit',
+ className='button-default-style'),
+ dcc.Download(id='download-outlier-df'),
+ dcc.RadioItems(
+ ['meanDev', 'z'],
+ 'z',
+ id='center-measure',
+ ),
+ html.Div(children=[
+ dcc.Graph(id='outlier-plot', className='side-by-side-graph'),
+ dcc.Graph(id='number-features-bar',
+ className='side-by-side-graph'),
+ ]),
+ html.H5("Choose to plot where |Z| is above or below z-cutoff",
+ className='header-button-text'),
+ dcc.RadioItems(['below', 'above', 'all'],
+ 'all',
+ id='z-score-cutoff',
+ className='graph-component-align'),
+ html.Div(children=[
+ dcc.Graph(id='p-scatter', className='side-by-side-graph'),
+ dcc.Graph(id='p-bar', className='side-by-side-graph')
+ ])
+ ])
+}
+
+scrna_tab_content = {
+ 'Expression Level':
+ html.Div([
+ html.Button("Download CSV",
+ id='scrna_exp_dl_btn',
+ className='button-default-style'),
+ dcc.Download(id='download-scrna-exp-df'),
+ html.H4('Pick gene of interest', className='header-button-text'),
+ dcc.Dropdown(value='ABL1',
+ id='this-gene-scrna',
+ className='custom-dropdown'),
+ dcc.Graph(id='scrna_exp_gene_plot', className='side-by-side-graph'),
+ dcc.Graph(id='scrna_exp_gene_time_plot',
+ className='side-by-side-graph')
+ ]),
+ 'Variance':
+ html.Div([
+ html.Br(),
+ html.Button("Download CSV",
+ id='scrna_variance_dl_btn',
+ className='button-default-style'),
+ dcc.Download(id='download-scrna-variance'),
+ html.H4("Select feature(s) of interest",
+ className='header-button-text'),
+ dcc.Dropdown(id='gene-dropdown-select',
+ multi=True,
+ className='custom-dropdown'),
+ html.Button(id='var-select-btn',
+ children='Update feature plot',
+ className='button-default-style'),
+ dcc.Graph(id='scrna_features_decomp'),
+ dcc.Graph(id='scrna_variance_decomp')
+ ]),
+ 'Intra-donor Variation':
+ html.Div([
+ html.Br(),
+ html.Button("Download CSV",
+ id='scrna_cv_gene_btn',
+ className='button-default-style'),
+ html.H4("Select between variale or stable",
+ className='header-button-text'),
+ dcc.RadioItems(['Stable', 'Variable'],
+ 'Variable',
+ id='scrna_stablevar_select',
+ className='graph-component-align'),
+ dcc.Dropdown(id='stablevar-selector',
+ multi=True,
+ placeholder='Select genes of interest',
+ className='custom-dropdown'),
+ html.Button(id='subset-stablevar',
+ n_clicks=0,
+ children='Update Plot',
+ className='button-default-style'),
+ dcc.Download(id='download-scrna-cv-gene-df'),
+ dcc.Graph(id='scrna_stable_plot'),
+ html.H4("Select celltype of interest", className='header-button-text'),
+ dcc.Dropdown(id='this-gene-intra',
+ value='ASDC',
+ className='custom-dropdown'),
+ dcc.Graph(id='scrna_cv_profile'),
+ # dcc.Graph(id='cvprof-hg-fig'),
+ ]),
+ 'UMAP':
+ html.Div([
+ html.Button("Download CSV",
+ id='umap_btn',
+ className='button-default-style'),
+ dcc.Graph(id='pc_fig', className='side-by-side-graph'),
+ dcc.Graph(id='celltype_umap', className='side-by-side-graph'),
+ dcc.Download(id='download-umap-df'),
+ ])
+}
+
+
+def get_tab_content(datatype, this_tab):
+ if datatype == 'Bulk Plasma':
+ return bulk_tab_content[this_tab]
+ elif datatype == "Single-Cell":
+ return scrna_tab_content[this_tab]
+
+
+def get_datapath(data_entry):
+ '''
+ '''
+ if data_entry == 'Bulk Plasma':
+ return ('{}/data/Olink_NPX_log2_Protein.Rda'.format(os.getcwd()))
+ if data_entry == "Single-Cell":
+ return ('{}/data/AIFI-scRNA-PBMC-FinalData.RDS'.format(os.getcwd()))
+
+
+def get_metapath():
+ return '{}/data/data_Metadata.Rda'.format(os.getcwd())
+
+
+def determine_datatype(user_input):
+ """ based on users' submission, return proper datatype"""
+ if user_input == 'Bulk Plasma':
+ return 'bulk'
+ elif user_input == 'Single-Cell':
+ return 'singlecell'
+
+
+@app.callback(Output('params-content', 'children'),
+ Input('params-dtype', 'value'))
+def render_params(dtype):
+ """ returns div object that holds submission for parameters """
+ if dtype == 'Bulk Plasma':
+ return html.Div(children=[
+ dbc.Row([]),
+ dbc.Row([
+ dbc.Col([
+ html.H4("Choose CV threshold",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Required)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-cv',
+ type='number',
+ value=5,
+ className='center-component-style',
+ style={'display': 'none'}),
+ ]),
+ dbc.Col([
+ html.H4("Choose mean threshold",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Required)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-mean',
+ type='number',
+ value=1,
+ className='center-component-style',
+ style={'display': 'none'}),
+ ]),
+ dbc.Col([
+ html.H4("Choose z-score cutoff",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Optional)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-z-score',
+ type='number',
+ value=2,
+ className='center-component-style',
+ style={'display': 'none'}),
+ ]),
+ dbc.Col([
+ html.H4("Choose NA Threshold",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Optional)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-na',
+ type='number',
+ className='center-component-style',
+ style={'display': 'none'})
+ ])
+ ],
+ style={"background-color": colors['aiblue']})
+ ])
+ elif dtype == "Single-Cell":
+ return html.Div(children=[
+ dbc.Row([
+ dbc.Col([
+ dcc.Input(id='params-z-score',
+ type='number',
+ value=2,
+ className='center-component-style',
+ style={'display': 'none'})
+ ]),
+ dbc.Col([
+ dcc.Input(id='params-na',
+ type='number',
+ className='center-component-style',
+ style={'display': 'none'})
+ ])
+ ]),
+ dbc.Row([
+ dbc.Col([
+ html.H4("Choose CV threshold",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Required)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-cv',
+ type='number',
+ value=10,
+ className='center-component-style',
+ style={'display': 'none'})
+ ]),
+ dbc.Col([
+ html.H4("Choose mean threshold",
+ className='params-header',
+ style={'display': 'none'}),
+ html.P('(Required)',
+ className='optional-text',
+ style={'display': 'none'}),
+ dcc.Input(id='params-mean',
+ type='number',
+ value=0.1,
+ className='center-component-style',
+ style={'display': 'none'}),
+ ])
+ ]),
+ ],
+ style={"background-color": colors['aiblue']})
+
+
+submit_params_page = html.Div(
+ className='wrapper',
+ children=[
+ html.Header(
+ className='header',
+ children=[
+ html.Nav(
+ className='navbar navbar-dark bg-hise-blue-1',
+ children=[
+ html.Div(
+ className='container-fluid',
+ children=[
+ html.
+ A(className='navbar-brand',
+ href='#',
+ children=[
+ html.Img(
+ src=
+ 'https://storage.googleapis.com/aifi-static-assets/logo_imm_graphic_desktop.png',
+ alt='AI logo',
+ height='24')
+ ])
+ ])
+ ]),
+ ]),
+ html.Main(
+ className='bg-hise-blue-1',
+ children=[
+ html.Section(
+ className='container-fluid',
+ children=[
+ html.
+ H1(children=
+ 'Platform for Analyzing Longitudinal Multi-omics Data',
+ className="text-white text-center"),
+ ]),
+ html.Section(
+ className='container-fluid',
+ children=[
+ html.Form(children=[
+ html.Div(
+ className='row mb-3',
+ children=[
+ html.Div(
+ className='col-md-2',
+ children=[
+ html.Label(
+ children='Choose datatype',
+ htmlFor='params-dtype',
+ className=
+ 'form-label text-white'),
+ dcc.Dropdown(
+ id='params-dtype',
+ options=['Bulk Plasma'],
+ className=''),
+ ])
+ ]),
+ html.Div(className='row mb-3',
+ children=[
+ html.Div(className='col-md-12',
+ children=[
+ html.Div(
+ id='params-content',
+ className='mb-3'),
+ ])
+ ]),
+ html.Div(
+ className='row pb-3',
+ children=[
+ html.Div(
+ className='col-md-12 text-center',
+ children=[
+ html.Button(
+ "Run App",
+ id='run_app_btn',
+ className=
+ 'btn btn-hise-green-2 btn-lg'),
+ ])
+ ]),
+ ]),
+ dbc.Spinner(id='loading-content',
+ children=[html.Div(id='output-content')],
+ delay_hide=10,
+ fullscreen=True)
+ ]),
+ ]),
+ html.Footer(),
+ ])
+
+data_store = html.Div(children=[
+ dcc.Store('input-data'),
+ dcc.Store('scrna_stablevar_output'),
+ dcc.Store('scrna_gene_subset_output'),
+ dcc.Store('input-metadata'),
+ dcc.Store('var-decomp'),
+ dcc.Store('scrna-subset-data'),
+ dcc.Store('umap_input'),
+ dcc.Store('input-cvprof'),
+ dcc.Store('input-outlier'),
+ dcc.Store('corr-matrix'),
+ dcc.Store('download-var-df'),
+ dcc.Store('variance-out'),
+ dcc.Store('expression-out'),
+ dcc.Store('outlier-input-state'),
+ dcc.Store('gene-df'),
+ dcc.Store('input_stablevar')
+])
+
+app.layout = html.Div([submit_params_page, data_store])
+
+if __name__ == '__main__':
+ app.run_server(host='0.0.0.0', port=8050)
\ No newline at end of file
diff --git a/dash_app/assets/styles.css b/dash_app/assets/styles.css
new file mode 100644
index 0000000..7df73ca
--- /dev/null
+++ b/dash_app/assets/styles.css
@@ -0,0 +1,75 @@
+.tab-selected-style {
+ border-top: 1px solid #d6d6d6;
+ border-bottom: 1px solid #d6d6d6;
+ background-color: rgb(120, 156, 74) !important;
+ color: white !important;
+ padding: 6px;
+}
+.default-tab-style {
+ border-bottom: 1px solid #d6d6d6;
+ padding: 6px;
+ font-weight: bold;
+ color: white;
+}
+.params-header {
+ text-align: center;
+ color: white;
+ padding-top: 12px;
+ display: grid;
+ justify-content: center;
+}
+.optional-text {
+ color: white;
+ justify-content: center;
+ display: grid;
+}
+.header-button-text {
+ text-align: center;
+ color: rgb(0, 48, 87);
+ padding-top: 16px;
+ display: grid;
+ justify-content: center;
+}
+.center-component-style {
+ width: 200px;
+ margin: 0 auto;
+ align-items: center;
+ display: grid;
+}
+.button-default-style {
+ align-items: center;
+ width: 200px;
+ display: grid;
+ justify-content: center;
+ margin: 0 auto;
+ background: rgb(120, 156, 74);
+}
+.graph-component-align {
+ width: 200px;
+ margin: 0 auto;
+ align-items: center;
+}
+.matrix-graph-style {
+ height: 800px;
+ width: 800px;
+ margin: 0 auto;
+}
+.side-by-side-graph {
+ display: inline-block;
+ width: 50%;
+}
+.custom-dropdown {
+ width: 200px;
+ margin: 0 auto;
+}
+.custom-cv-graph {
+ height: 150vh;
+}
+.custom-h2-header {
+ text-align: center;
+ color: white;
+ background-color: rgb(0, 48, 87);
+}
+.need-to-run-message {
+ text-align: center;
+}
diff --git a/dash_app/data/bulk_cv.csv b/dash_app/data/bulk_cv.csv
new file mode 100644
index 0000000..496ffef
--- /dev/null
+++ b/dash_app/data/bulk_cv.csv
@@ -0,0 +1,1047 @@
+,PTID1,PTID2,PTID3,PTID4,PTID5,PTID6,Mean,Median,NAs
+AARSD1,13.208195342487203,9.992418659699316,55.452635099048834,9.15135029502645,11.77450418579582,8.269496374469762,17.97476665942123,10.883461422747569,0
+ABHD14B,4.8704674237657715,8.535307480770696,108.69988517380513,6.263079071234408,7.606300170011155,8.125778977239765,24.016803049471154,7.866039573625461,0
+ABL1,7.816971144996077,15.229976149303974,11.4885681211162,7.407833503595557,15.239137226830557,13.969470691801893,11.85865947294071,12.729019406459047,0
+ACAA1,12.50147375418179,11.087883050988726,7.504585531156262,10.515979257108977,7.023060378015156,10.61288627743214,9.874311374813843,10.56443276727056,0
+ACAN,4.730024892568468,4.4999303001236655,4.218283025714982,3.2101343932451627,4.304788230097857,4.117916755446956,4.180179599532848,4.261535627906419,0
+ACE2,4.508587302169628,5.764873129983021,4.009702271909277,2.6550111682328152,6.771251540919798,3.647794397598146,4.559536635135448,4.259144787039453,0
+ACP6,1.5571264678991403,3.313480763947926,2.8551573233321856,3.4706779607247293,4.045471795080968,4.327894462884878,3.261634795644971,3.392079362336328,0
+ACVRL1,1.631536246934627,1.7399063726887793,2.159264418987909,0.9934900984112675,1.6351106518013339,1.3941268209230178,1.592239101624489,1.6333234493679805,0
+ADA,2.2334626825959667,2.378230573745991,2.349109152060414,1.9451879121755968,2.4694300968464513,2.3014358835037627,2.279476050154697,2.3252725177820883,0
+ADAM15,1.0819647212609727,0.8893575272035517,11.286383135019937,1.69235766257064,1.3344670761963995,1.32184061332483,2.9343951225960554,1.3281538447606147,0
+ADAM22,2.381562778128728,3.0412493823226123,3.2865195021175455,2.354973785265245,3.2666433089223244,3.280804082780647,2.93529213992285,3.1539463456224683,0
+ADAM23,2.908467521387324,3.3117339067782026,3.1855689967930645,1.886592866113567,4.426667218110317,4.2525866493093325,3.3286028597486346,3.2486514517856335,0
+ADAM8,2.2681319234220267,1.8945699068231747,1.6035373472524315,1.8479988923972956,2.0518566169240775,0.5872099880942964,1.7088841124855505,1.8712843996102353,0
+ADAMTS13,0.9079118984914722,1.2491271387254668,1.6499181010226776,0.7604617656595132,0.701286388512421,1.727466361472646,1.1660286089806995,1.0785195186084695,0
+ADAMTS15,7.113670085421559,4.803256732009095,4.582735235500936,5.0710132946938264,4.106157806047957,4.3956402908367425,5.012078907418353,4.692995983755015,0
+ADGRE2,4.104224837207327,5.429683791380281,6.226421540882014,3.8374158980598945,3.0162572199974447,2.3180309906594783,4.155339046364406,3.9708203676336105,0
+ADGRE5,2.559087102411393,2.365520786580856,2.624845495553538,2.532112267177025,2.348911768130541,1.9843542106276453,2.4024719384134996,2.4488165268789404,0
+ADGRG1,3.3523740442279104,6.452813475181994,3.4173220994653866,5.77670735743761,8.872857253368402,7.997051331559567,5.978187593540145,6.114760416309802,0
+ADGRG2,5.786329592582984,5.654721781781438,5.462397747246841,3.930535656090944,4.67666678711922,3.8053483836395854,4.885999991410168,5.06953226718303,0
+ADM,1.5334162732241223,2.1101542609980712,2.0193265888461025,2.1051943111673754,1.8085572941421617,2.1731445315226945,1.958298876650088,2.0622604500067387,0
+AFP,3.918573140316304,2.366925398195232,2.645930961395405,4.160557360384615,1.8224618207014018,4.068044785637387,3.1637489111050576,3.2822520508558544,0
+AGER,0.8254261164035017,0.9876605355095044,1.4595228374060272,0.6382022912293971,0.5675457109353609,1.5882431881174193,1.0111001132668684,0.906543325956503,0
+AGR2,19.761841773880352,15.246874956521507,10.674117461807752,14.515061183929792,19.58750016835486,10.798143018392043,15.097256427147718,14.880968070225649,0
+AGRP,3.8408285006965777,8.039607693327067,6.7340150727232135,7.127108971119046,5.4712809161653,6.186544783231833,6.23323098954384,6.460279927977523,0
+AIFM1,27.539452630960966,29.128986730539424,18.87586259259596,19.934279979563147,27.319787220445292,23.163124274136063,24.326915571373476,25.24145574729068,0
+AIMP1,15.87146908112027,19.354976269347976,11.444191593578447,13.328669043746677,14.298658170969713,14.52359338851626,14.803592924546557,14.411125779742985,0
+AKR1B1,7.156997250909105,12.199735054972061,6.168497937574889,8.981995272468815,1.1393048657326867,7.891992997448921,7.256420563184413,7.524495124179013,0
+AKT1S1,8.738070444434168,7.803884316567027,26.829865652438176,5.973263228280651,8.70078536149862,7.820627224195561,10.977749371235701,8.26070629284709,0
+ALCAM,1.4842247357925178,1.7803934632129923,1.5300255081014589,1.5406910519591686,1.4924278453589588,2.92292964810857,1.7917820420889443,1.5353582800303136,0
+ALDH1A1,3.3942041421929177,8.29382501477472,8.662731316855462,8.531770553655978,5.984566830240778,9.84989701336919,7.452832478514841,8.41279778421535,0
+ALDH3A1,24.513465828340557,10.043800898125395,10.33708579621348,10.03467909309621,12.588731670848729,15.524178933067558,13.840323703281989,11.462908733531105,0
+ALPP,5.447642279769519,2.5118528963015456,2.4521044129521687,5.196884043642561,3.5660811726207826,3.400601986441663,3.7625277986213734,3.483341579531223,0
+AMBP,0.654940959774814,1.4599964275432067,1.4051273914579134,0.7088067430421721,1.2790675481808031,0.8851682798382692,1.0655178916395298,1.0821179140095363,0
+AMIGO2,3.0718848723337753,3.9250397109421233,2.4991290603706435,2.197581285033016,2.949527150492329,2.0930940949973382,2.789376029028204,2.7243281054314865,0
+AMN,1.8695226341099815,2.0806426397848017,3.4301921108370546,3.7523876666195135,5.960078208391689,3.629712705132072,3.453755994145852,3.5299524079845632,0
+ANG,2.6065142400905477,4.961820793802045,3.001102841617409,4.054561689050042,3.678475611066826,3.5699784384821336,3.645408935684834,3.6242270247744797,0
+ANGPT1,5.6877143379634605,5.557836131306955,5.410080690303986,3.4883028072475395,6.684796236234148,4.051075769602787,5.1466343287764795,5.483958410805471,0
+ANGPT2,4.191232485592213,5.933188177861488,5.449601805403181,4.842408205047449,6.450832637740592,4.577078220240183,5.240723588647517,5.146005005225315,0
+ANGPTL1,4.470121579378623,6.49247011368989,4.0811886946325,3.692949888379899,3.5310742132929858,2.653372754419773,4.153529540632278,3.8870692915061995,0
+ANGPTL3,2.4037694409824697,6.282213372829829,2.304534150621557,3.843535215928779,3.151613893204252,5.244468346638188,3.8716890700341793,3.4975745545665156,0
+ANGPTL4,10.417279138800499,7.115648621692707,6.898884042509839,5.672280829433146,6.329834284225388,6.55050361163169,7.164071754715545,6.724693827070764,0
+ANGPTL7,7.47744410702518,6.067314829154711,5.187569061671872,5.347930513800017,9.638745525706556,4.2562294116344646,6.329205574832133,5.707622671477363,0
+ANPEP,1.758978795046035,2.0226520972097664,1.7058429210736796,1.5140417760249176,1.9922587058255183,3.444316749251354,2.0730151740718785,1.8756187504357766,0
+ANXA1,7.015788939788003,9.74164682250169,6.253312737426348,6.979487563242706,4.855706850054233,5.607341095759627,6.7422140014621,6.616400150334527,0
+ANXA10,13.903170901098342,11.547987885065494,49.011441656298274,12.555026051181445,8.342123176420603,25.832850338750227,20.198766668135733,13.229098476139892,0
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diff --git a/dash_app/data/bulk_data.csv b/dash_app/data/bulk_data.csv
new file mode 100644
index 0000000..385f749
--- /dev/null
+++ b/dash_app/data/bulk_data.csv
@@ -0,0 +1,1047 @@
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diff --git a/dash_app/data/bulk_datamatrix.csv b/dash_app/data/bulk_datamatrix.csv
new file mode 100644
index 0000000..385f749
--- /dev/null
+++ b/dash_app/data/bulk_datamatrix.csv
@@ -0,0 +1,1047 @@
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diff --git a/dash_app/data/bulk_decomp_var.csv b/dash_app/data/bulk_decomp_var.csv
new file mode 100644
index 0000000..b2783e3
--- /dev/null
+++ b/dash_app/data/bulk_decomp_var.csv
@@ -0,0 +1,1047 @@
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diff --git a/dash_app/data/bulk_metadata.csv b/dash_app/data/bulk_metadata.csv
new file mode 100644
index 0000000..bebe0c7
--- /dev/null
+++ b/dash_app/data/bulk_metadata.csv
@@ -0,0 +1,61 @@
+,Sample,PTID,Time,Sex
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+PTID1W2,PTID1W2,PTID1,W2,Female
+PTID1W3,PTID1W3,PTID1,W3,Female
+PTID1W4,PTID1W4,PTID1,W4,Female
+PTID1W5,PTID1W5,PTID1,W5,Female
+PTID1W6,PTID1W6,PTID1,W6,Female
+PTID1W7,PTID1W7,PTID1,W7,Female
+PTID1W8,PTID1W8,PTID1,W8,Female
+PTID1W9,PTID1W9,PTID1,W9,Female
+PTID1W10,PTID1W10,PTID1,W10,Female
+PTID2W1,PTID2W1,PTID2,W1,Male
+PTID2W2,PTID2W2,PTID2,W2,Male
+PTID2W3,PTID2W3,PTID2,W3,Male
+PTID2W4,PTID2W4,PTID2,W4,Male
+PTID2W5,PTID2W5,PTID2,W5,Male
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+PTID2W7,PTID2W7,PTID2,W7,Male
+PTID2W8,PTID2W8,PTID2,W8,Male
+PTID2W9,PTID2W9,PTID2,W9,Male
+PTID2W10,PTID2W10,PTID2,W10,Male
+PTID3W1,PTID3W1,PTID3,W1,Female
+PTID3W2,PTID3W2,PTID3,W2,Female
+PTID3W3,PTID3W3,PTID3,W3,Female
+PTID3W4,PTID3W4,PTID3,W4,Female
+PTID3W5,PTID3W5,PTID3,W5,Female
+PTID3W6,PTID3W6,PTID3,W6,Female
+PTID3W7,PTID3W7,PTID3,W7,Female
+PTID3W8,PTID3W8,PTID3,W8,Female
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+PTID3W10,PTID3W10,PTID3,W10,Female
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diff --git a/dash_app/data/bulk_outlier.csv b/dash_app/data/bulk_outlier.csv
new file mode 100644
index 0000000..165b62a
--- /dev/null
+++ b/dash_app/data/bulk_outlier.csv
@@ -0,0 +1,1962 @@
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diff --git a/dash_app/data/bulk_umap.csv b/dash_app/data/bulk_umap.csv
new file mode 100644
index 0000000..e16c76d
--- /dev/null
+++ b/dash_app/data/bulk_umap.csv
@@ -0,0 +1 @@
+""
diff --git a/dash_app/data/bulk_var_decomp.csv b/dash_app/data/bulk_var_decomp.csv
new file mode 100644
index 0000000..f610a95
--- /dev/null
+++ b/dash_app/data/bulk_var_decomp.csv
@@ -0,0 +1,1047 @@
+,Gene,mean,median,sd,max,PTID,Time,Residual
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