cplaisier

# -*- coding: utf-8 -*-
"""
Created on Fri Nov 19 17:22:40 2021

@author: Pamela Appiah
"""

#Load Up Data
import pandas as pd
from scipy.stats import hypergeom

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import scanpy as sc
import scvelo as scv
import pandas as pd
import numpy as np
#from matplotlib.backends.backend_pdf import PdfPages
#import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

# Load U5 data from Seurat
#adata_Seurat = scv.read_loom('U5_all_v3.loom')

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##########################################################
## OncoMerge:  alignment.py                             ##
##  ______     ______     __  __                        ##
## /\  __ \   /\  ___\   /\ \/\ \                       ##
## \ \  __ \  \ \___  \  \ \ \_\ \                      ##
##  \ \_\ \_\  \/\_____\  \ \_____\                     ##
##   \/_/\/_/   \/_____/   \/_____/                     ##
## @Developed by: Plaisier Lab                          ##
##   (https://plaisierlab.engineering.asu.edu/)         ##
##   Arizona State University                           ##

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## Docker instance for analysis
# docker run -it -v "/home/bgasvoda/Pia:/files" cplaisier/scrna_seq_velocity

# Commands to run on Docker instance before starting
# cd /files
# pip3 install kneed
# python3

import pandas as pd
import numpy as np

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import re
import pandas as pd

def compareMiRNANames(a, b):
    if a==b:
        return 1
    if len(a)<len(b):
        if a[-3:]=='-3p':
            re1 = re.compile(a+'[a-oq-z]?(-\d)?-3p$')
        else:

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##########################################################
## OncoMerge:  oncoMerge.py                             ##
##  ______     ______     __  __                        ##
## /\  __ \   /\  ___\   /\ \/\ \                       ##
## \ \  __ \  \ \___  \  \ \ \_\ \                      ##
##  \ \_\ \_\  \/\_____\  \ \_____\                     ##
##   \/_/\/_/   \/_____/   \/_____/                     ##
## @Developed by: Plaisier Lab                          ##
##   (https://plaisierlab.engineering.asu.edu/)         ##
##   Arizona State University                           ##

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plotPCA_v2 <- function(x, intgroup = "condition", ntop = 500, col) 
{
    rv = rowVars(x)
    select = order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
    pca = prcomp(t(x[select, ]))
    fac = factor(apply(as.data.frame(colData(x)[, intgroup, drop = FALSE]), 
        1, paste, collapse = " : "))
    if (missing(col)) {
        col = if (nlevels(fac) >= 3) 
            brewer.pal(nlevels(fac), "Paired")

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#!/usr/bin/env Rscript
#suppressMessages(library(GOSim))
suppressMessages(library(GOSemSim))
suppressMessages(library(getopt))

spec = matrix(c(
  'outdir', 'o', 1, 'character',
  'help', 'h', 0, 'logical'
), byrow=TRUE, ncol=4)

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## Semantic similarity
library(GOSim)

l1 = list()
#l1$Adaptive_Response = c('GO:0002250','GO:0002819')
l1$Akt_Signalling = c('GO:0043491','GO:0051896')
l1$Antigen_Presentation = c('GO:0019882','GO:0002472','GO:0002625','GO:0019886')
l1$Apoptosis = c('GO:0006915','GO:0033028','GO:0071888','GO:2000110','GO:2000111','GO:0012501','GO:0048102') # ,'GO:2001242'
l1$Cell_Cycle = c('GO:0007050','GO:0051726','GO:0006260','GO:0000075')
l1$Cell_Cycle_Checkpoint = c('GO:0000075')

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cd /Users/baileyquinn/Desktop/BT145/

docker run -it -v "/Users/baileyquinn/Desktop/BT145:/files" cplaisier/scrna_seq_velocity
​
cd /files

pip3 install clusim
pip3 install statsmodels

python3