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# Plot histogram | |
(def d (read-dataset "/Users/mikaelhuss/Desktop/SAGE-liver-CVD/deliver_clinical_traits.txt" :header true :delim \tab)) | |
(view (histogram (filter number? (sel d :cols 5)))) | |
# Or the same with with-data | |
(with-data | |
(read-dataset "/Users/mikaelhuss/Desktop/SAGE-liver-CVD/deliver_clinical_traits.txt" :header true :delim \tab) | |
(view (histogram (filter number? ($ 5 ))))) |
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import sys | |
for line in open(sys.argv[1]): | |
if 'layer' in line: | |
fname = '_'.join(line.strip().split()) + '_activities.txt' | |
outf = open(fname,'w') | |
else: | |
outf.write(line) |
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import sys | |
f = open(sys.argv[1]) | |
wts = [] # This will be a list of list of lists (=list of matrices) with weights | |
bias_in = [] # List of lists of bias values in the input part of each sublayer | |
bias_out = [] # List of lists of bias values in the output part of each sublayer | |
prefs = [] # Prefixes for file names | |
state = None # state can be None, 'weight', 'bias_in' or 'bias_out' |
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args = commandArgs(trailingOnly=TRUE) | |
path=args[1] | |
files=Sys.glob(paste0(path,"/*/abundance.tsv")) | |
#print(files) | |
merge_two <- function(x,y){ | |
#print(dim(x)) | |
if ("tpm" %in% colnames(x)){ | |
x_ <- x[,c(1,5)] | |
} | |
else{ |
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import sys | |
import gzip | |
if len(sys.argv)<3: | |
sys.exit("python sum_per_gene.py <cDNA FASTA file> <TPM table>") | |
ensg = {} | |
mapf = gzip.open(sys.argv[1]) | |
ctr = 0 |
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library(tensorflow) | |
tf$reset_default_graph() | |
x_data <- runif(100, min=0, max=1) | |
y_data <- x_data * 0.1 + 0.3 + rnorm(n, mean=0, sd=0.025) | |
W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0)) | |
b <- tf$Variable(tf$zeros(shape(1L))) | |
y <- W * x_data + b |
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# Installation (only needs to be done once) | |
source("http://bioconductor.org/biocLite.R") | |
biocLite("rhdf5") | |
install.packages("devtools") | |
devtools::install_github("pachterlab/sleuth") | |
# Now load the package | |
library("sleuth") | |
# A function (borrowed from the Sleuth documentation) for connecting Ensembl transcript names to common gene names |
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def read_single_cossmo_example(serialized_example, n_tissues=1, coord_sys='rna1'): | |
"""Decode a single COSSMO example | |
coord_sys must be one of 'rna1' or 'dna0', if 'dna0' then an extra 'strand' field | |
must exist in the tfrecord and is extracted. | |
""" | |
assert coord_sys in ['dna0', 'rna1'] | |
context_features = { |
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from pathlib import Path | |
import os | |
import sys | |
from fire import Fire | |
import numpy as np | |
import pandas as pd | |
from tqdm import tqdm | |
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1. Install appropriate version of the Tensorflow (Python) framework from https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html | |
In my case (Mac OS X 10.11), I did: | |
- Get the .whl file (this is more likely to work than a direct pip install) | |
wget https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.11.0-py3-none-any.whl | |
- Install using non-Anaconda pip | |
/usr/local/bin/pip3 install tensorflow-0.11.0-py3-none-any.whl_ |
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