kieranrcampbell

library(TSCAN)

set.seed(1)

load("X.rda")

dim(X)
## [1] 228   5

ts <- exprmclust(t(X))

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

## 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

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

# 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

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

library(stringr)
library(ggplot)
library(dplyr)

## Assuming "go" has columns "term" and "q_value"

go_top <- head(go, n = 10)
go_top$term <- str_to_title(go_top$term)
terms_sorted <- arrange(go_top, desc(q_value)) %>% .$term
go_top$term <- factor(go_top$term, levels = terms_sorted)

kieranrcampbell

## You can show that the estimate of fg is wrong *only*
## when m_beta = 1 and m_c = 1 and xx = 1 and that in
## such a case it appears to overcount by 2, implying term 11 in the derivation is wrong

m_alpha <- 0
s_alpha <- 1
m_beta <- 1
s_beta <- 1
m_t <- 0
s_t <- 1

kieranrcampbell

library(stringr)
library(ggplot2)
library(scran)
library(scater)
library(dplyr)

# Step 1: do the overdispersion analysis

sc2 <- sce[rowMeans(round(exprs(sce)) > 0) > 0, ] # Keep only genes that are expressed
is_ercc <- grepl("NA_ERCC", featureNames(sc2)) # Your ERCCs might be named differently to mine!

kieranrcampbell

# Nice colour palette for visualising copy number profiles
# inspired by DLP paper
cnv_cols <- c("0" = "#deebf7",
          "1" = "#9ecae1", 
          "2" = "grey80", 
          "3" = "#fdae6b", 
          "4" = "#e6550d")

# Factors for chromosomes
chr_levels <- c(as.character(1:23), "X", "Y")