ShixiangWang

#' @function survival analysis according to gene expression for primary tumor
#' @param geneSymbol identify gene symbol 
#' @param exp TCGA format gene expression dataset
#' @param  cli Clinical information dataset
#' @param method method use to dive samples into groups. Options are "quantile", "median", "mean". the "quartile" use first and third quartile as threshold
#' @param trans transform the clinical IDs from '-' separate to '.' separate
#' @return include the plot and p value
#' @author Shixiang Wang

# input test data

ShixiangWang

T_test = function(data, group1, group2, adj.method="fdr"){
  # the first column of data is identifier
  # the others columns cotain gene expression, samples' name should be colnames
  # group1, group2 are used to compare two groups of data
  # adj.method defines which method used to adjust p values
  
  colnames(data)[1] = "geneSymbol"
  # only get samples we wanna to compare, be sure 
  # all names in group1 and group2 can match colnames of data 

ShixiangWang

pltDiff = function(data=NULL, sample=NULL, target=NULL,
                   group1, group2, group1.name="group1",
                   group2.name="group2", title="data", x_lab="", y_lab="",
                   notch=TRUE){
  require(ggpubr, quietly = TRUE)
  
  if(!is.data.frame(data)) stop("Wrong input! Please check your dataset. The input data type must be data.frame.")
  a = data.frame(value=data[, target], group=ifelse(!(data[,sample]%in%c(group1,group2)),
                                                 NA, ifelse(data[,sample]%in%group1, group1.name, group2.name)))
  a = a[a$group%in%c(group1.name, group2.name), ]

ShixiangWang

getID = function(exp_value, method){
  # compute threshold according to method parameter
  if(method=="quartile"){
    symbol_stat <- summary(exp_value)
    ths1 <- as.numeric(symbol_stat[2])
    ths2 <- as.numeric(symbol_stat[5])
    down_id <- names(exp_value[exp_value<=ths1])
    up_id <- names(exp_value[exp_value>=ths2])
  }else if(method=="mean"){
    ths <- mean(exp_value)

ShixiangWang

#' @function Summary functions for error bar plot.
#' @references http://www.jianshu.com/p/003138ac593b

## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,

ShixiangWang

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

ShixiangWang

# ---------------------------
# Accessing the material
# https://tinyurl.com/bioc2017-ELMER
# ---------------------------
library("Bioc2017.TCGAbiolinks.ELMER")
Biobase::openVignette("Bioc2017.TCGAbiolinks.ELMER")

# ---------------------------
# Section 1: 
# Aims:

ShixiangWang

# Your init script
#
# Atom will evaluate this file each time a new window is opened. It is run
# after packages are loaded/activated and after the previous editor state
# has been restored.
#
# An example hack to log to the console when each text editor is saved.
#
# atom.workspace.observeTextEditors (editor) ->
#   editor.onDidSave ->

ShixiangWang

ShixiangWang

#!/bin/bash
# function Extract for common file formats

function extract {
 if [ -z "$1" ]; then
    # display usage if no parameters given
    echo "Usage: extract <path/file_name>.<zip|rar|bz2|gz|tar|tbz2|tgz|Z|7z|xz|ex|tar.bz2|tar.gz|tar.xz>"
 else
    if [ -f "$1" ] ; then
        NAME=${1%.*}