I need to give credit to Kenneth Reitz, who gives great instruction on this top.

Lesson 1: reproduce what Ken suggested

	
Fork Ken's github repository and make my own
	
When I am in ./sample/ folder, and try "make", it fails. Error was "OSError: [Errno 13] Permission denied: '/ddn/gs1/biotools/python/lib/python2.7/site-packages/nose'"
	
When I am in ./sample/ folder, and try "python setup.py install", it fails also. Error was " [Errno 13] Permission denied: '/ddn/gs1/biotools/python/lib/python2.7/site-packages/test-easy-install-147300.write-test'"

Topic 1: how can I solve this problem

What is SEM model:

SEM is a combination of factor analysis and multiple regression
It also goes by the aliases “causal modeling” and “analysis of covariance structure”
Special cases of SEM include confirmatory factor analysis and path analysis

The SEM can be divided into two parts. The measurement model is the part which relates measured variables to latent variables. The structural model is the part that relates latent variables to one another.

The path analysis, which is SEM with no latent variables. In other words, path analysis is SEM with a structural model, but no measurement model. A structural equation modeling (SEM) has only single indicators are employed for each of the variables in the causal model.

SEM model in genetics study, I am collecting some lecture notes on SEM.

Karl Wuensch from ECU
David Kenny
Dr. William Revelle from North Western University 
Dr. Brannick from USF has a good post on SEM vs. Path Analysis

David Kenny presents a very thorough explanation of using SEM
A very handy tutorial on SEM

It turns out that Dr. William Revelle from North Western University has a class Psychology 454 syllabus with detail R and R package for SEM.

In one of the lecture, he has a Lecture Note on “Latent Variable Modeling”.

An R package lavaan was created for Latent Variable Analysis and it works perfectly fine for an SEM model as shown in the following diagram.

model <- '
   # latent variables
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + y2 + y3 + y4
     dem65 =~ y5 + y6 + y7 + y8
   # regressions
     dem60 ~ ind60
     dem65 ~ ind60 + dem60
   # residual covariances
     y1 ~~ y5
     y2 ~~ y4 + y6
     y3 ~~ y7
     y4 ~~ y8
     y6 ~~ y8
'
fit <- sem(model,
           data=PoliticalDemocracy)
summary(fit)

Sometimes, there could be data-related “NOT converged” error! It is apparently data-dependent case. But, my model

  mod1 <- '
   SOX17_lev =~ GATA2_lev + PGR_lev
   IHH  ~ SOX17_lev '

happens to be like this!!

There has been a lot of resource from USGS — Dr. James Grace, who provides basic to advanced features and applications of SEM in his research.

Since I made a word-cloud image for a collaborator’s project, it caught people’s attention. It seems to be a good way to display science research in a vivid way. I would like to document this effort for future reference.

I need to give the credit to Rshiny-Application, which gets me interested producing the word cloud in R. 
There are a few libraries are quite popular for making a word-cloud: library(tm), library(SnowballC), library(wordcloud), library(memoise).

I have found some websites that helped me to learn about word-cloud. I’d like to document them for others and give the authors credit for their works.

The one from data science does not work for me. It generates error like "Error in simple_triplet_matrix(i, j, v, nrow = length(terms), ncol = length(corpus),  : 
  'i, j' invalid"
Here is another one
Another package is called wordcloud2 with a few examples

Case: I wanted to list all the file in a directory

Under Linux, Python3

import subprocess
subprocess.call(["ls", "-al"])

Hitchhikers

Case: How to create a Python Package

It is an advance topic, but I am willing to document it in case I need in the future.

First of all, there is a guide from Hitchhikers
I documented it in my Python note with pass word

Moving from “for to foreach”

I wanted to address this question in a broader scope, first of all, let’s look at “for” loop vs “foreach” option

The credit goes to Henry Scharf from Colorado State University.

“apply” and it derivative functions

R parallel — speed up the processes with mutli-core

My note:

I am trying to use PyCharmEde (PCE) incorporating docker on a Django webdev project.

Python teaching note

Found a very useful tutor web for teaching Python
I need a teaching platform and found 
this one at MIT

Came up with a paper on BMC genoics comparing five different somatic snp callers.

GATK UnifiedGenotyper in NaiveSubtract
MuTect1
SomaticSniper from the original paper
Installation help document for Strelka with updated user's guide and a quick start guide
And the original paper on somatic-sniper
VarScan2 from the original paper
Classical samtools method also works.

Calling variants with samtools/bcftools

Help from samtools protocol
Help from samtools/bcftools protocol

Working with Rmarkdown seems quite beneficial, as one can keep good record and Rmarkdown produces html or pdf report.

I need to work with Yicheng on a project on my github. From my existing repository, I need to make a branch and work with him

	
I found out a help post, and I am following it step-by-step
	
git checkout -b Rproj_w_yicheng
	
git checkout Rproj_w_yicheng
        
git remote add ocri3-w-yicheng https://github.com/ImageRecognitionMaster/myOCRI-iii
	
git branch
	
git branch -D Rproj_w_yicheng
	
git push origin :Rproj_w_yicheng

Here is the end results.