kieranrcampbell

rule exploratory_analysis:
    input:
        "data/tec_sceset_qc.rds", "analysis/02_exploratory_analysis.html"
    output:
        "data/tec_sceset_clusters.rds",
        "analysis/02_exploratory_analysis.Rmd",
        "data/deseq2_results.csv"
    shell:
        "Rscript -e \"rmarkdown::render('analysis/02_exploratory_analysis.Rmd')\""

kieranrcampbell

# A function that takes a set of genes and a background list and goes through the
# steps in goseq to output the enriched list

library(goseq)

#' Run all steps for a goseq analysis
#'
#' @param genes The genes "differentially expressed" or in the condition
#' @param all_genes The background gene set
#' @param genome The genome of genes and all_genes, e.g. mm10 or hg19

kieranrcampbell

from edward.models import RandomVariable
import tensorflow as tf
from tensorflow.contrib.distributions import Distribution


class Weibull(RandomVariable, Distribution):
    """Weibull distribution

    The Weibull distribution is defined over the non-negative real numbers.

kieranrcampbell

## Suppose we have a contigency table tbl formed by the table(...) command in R, with
## a logical vector of discoveries as the first argument and a logical vector of
## the ground truth as the second, e.g. tbl <- table(discoveries, ground_truth), then
## this function calculates the true positive rate, false positive rate and false discovery rate
## as per the wikipedia definition at https://en.wikipedia.org/wiki/Sensitivity_and_specificity

calculate_statistics <- function(tbl) {
  P <- sum(tbl[,2])
  N <- sum(tbl[,1])
  TP <- tbl[2,2]

kieranrcampbell

// [[Rcpp::export]]
NumericMatrix sample_tau_pg(NumericMatrix beta,
                            double a_beta, double b_beta) {
  int P = beta.nrow();
  int G = beta.ncol();
  
  NumericMatrix tau_pg(P, G);
  
  for(int p = 0; p < P; p++) {
    for(int g = 0; g < G; g++) {

kieranrcampbell

library(TSCAN)

set.seed(1)

load("X.rda")

dim(X)
## [1] 228   5

ts <- exprmclust(t(X))