Data science

Courses, books

• Data Science at the Command Line by Jeroen Janssens written using bookdown.
• 10 Free Must-Read Books for Machine Learning and Data Science
• STAT 430: Topics in Applied Statistics by Dirk Eddelbuettel
• https://jhu-advdatasci.github.io/2018/ Johns Hopkins SPH
• http://datasciencelabs.github.io/2016/ Harvard SPH
• https://cs109.github.io/2014 Harvard CS
• Biostat 203B: Introduction to Data Science
• Data Science, Big Data Analytics, and Digital Methods Videos. Over 3,200 videos comprising over 120 hours are available.
• The Analytics Edge from edX.org or MOOC/Massive Open Online Courses.
• Introduction to Machine Learning (I2ML)
• Probabilistic Machine Learning: An Introduction
• Telling Stories With Data by Rohan Alexander

Python

• Python Data Science Handbook: Essential Tools for Working with Data
• Getting started with data science using Python from opensource.com

R

• Coursera -> Data Science Specialization by JHS.
• Data science in a box
• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• https://r4ds.had.co.nz/ R for Data Science
• 20 Free Online Books to Learn R and Data Science
• Teaching resources by Irizarry. edx.org. Audit is free.
  • Introduction to Data Science
  • Data Analysis for the Life Sciences
  • Genomics Data Analysis
• Introduction to Data Science or on github Data Analysis and Prediction Algorithms with R, by Rafael A Irizarry (free)
• Why Is It Called That Way?! – Origin and Meaning of R Package Names.
  • dplyr
  • lubridate
  • ggplot2
  • data.table
  • tibble
  • purrr
  • amelia
  • magrittr
  • batman
  • Homeric
  • fcuk
  • hellno

Python vs R

R, Python & Julia in data science : A comparison

Datacamp

• 32 Completely FREE DataCamp Courses To Take In 2020
• How to Get Free DataCamp Subscription For 2 Months? Microsoft is providing a Free DataCamp subscription with Visual Studio Dev Essential Account. You just need to sign up for the account and its done.

How to prepare data for collaboration

How to share data for collaboration. Especially Page 7 has some (raw data) variable coding guidelines.

• naming variables: using meaning variable names, no spacing in column header, avoiding separator (except an underscore)
• coding variables: be consistent, no spelling error
• date and time: YYYY-MM-DD (ISO 8601 standard). A gene symbol "Oct-4" will be interpreted as a date and reformatted in Excel.
• missing data: "NA". Not leave any cells blank.
• using a code book file (*.docx for example): any lengthy explanation about variables should be put here. See p5 for an example.

Five types of data:

• continuous
• oridinal
• categorical
• missing
• censored

Some extra from Data organization in spreadsheets (the paper appears in American Statistician)

• No empty cells
• Put one thing in a cell
• Make a rectangle
• No calculation in the raw data files
• Create a data dictionary (same as code book)

Gene name errors from Excel

• Gene name errors: Lessons not learned.
  • HGNChelper: Identify and Correct Invalid HGNC Human Gene Symbols and MGI Mouse Gene Symbols
  • Some examples: MARCH3, SEPT8, OCT4, DEC1.
• Gene names, data corruption and Excel: a 2021 update

length(x)
# [1] 28109
length(grep("march", x, ignore.case=T))
# [1] 11
length(grep("sep", x, ignore.case=T))
# [1] 24
length(grep("oct", x, ignore.case=T))
# [1] 0
length(grep("dec", x, ignore.case=T))
# [1] 6
grep("sep", x, ignore.case=T, value=T)
 [1] "RNaseP_nuc"             "SEP15"                  "SEPHS1"
 [4] "SEPHS2"                 "SEPN1"                  "SEPP1"
 [7] "SEPSECS"                "SEPT1"                  "SEPT10"
[10] "SEPT11"                 "SEPT12"                 "SEPT14"
[13] "SEPT2"                  "SEPT3"                  "SEPT4"
[16] "SEPT5-GP1BB"            "SEPT6"                  "SEPT7"
[19] "SEPT7P2"                "SEPT7P9"                "SEPT8"
[22] "SEPT9"                  "SEPW1"                  "septin 9/TNRC6C fusion"

# Count non-alphanumeric symbols from a string
ind <- grep("[^[:alnum:] ]", x)
length(ind)
# [1] 1108

# Some cases: 
# "5S_rRNA"
# "HGC6.1.1"
# "Ig alpha 1-[alpha]2m"
# "T-cell receptor alpha chain variable ..."
# "TRA@"
# "TRNA_Ala"
# "TTN-AS1"
# "aromatase cytochrome P-450 (P-450AROM)"
# "immunoglobulin epsilon chain constant..."
# "septin 9/TNRC6C fusion"

All NIH-funded data must be made freely accessible

Data Sharing and Public Access Policies

complete.cases()

Count the number of rows in a data frame that have missing values with

sum(!complete.cases(dF))

> tmp <- matrix(1:6, 3, 2)
> tmp
     [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
> tmp[2,1] <- NA
> complete.cases(tmp)
[1]  TRUE FALSE  TRUE

Wrangling categorical data in R

https://peerj.com/preprints/3163.pdf

Some approaches:

• options(stringAsFactors=FALSE)
• Use the tidyverse package

Base R approach:

GSS <- read.csv("XXX.csv")
GSS$BaseLaborStatus <- GSS$LaborStatus
levels(GSS$BaseLaborStatus)
summary(GSS$BaseLaborStatus)
GSS$BaseLaborStatus <- as.character(GSS$BaseLaborStatus)
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Temp not working"] <- "Temporarily not working"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Unempl, laid off"] <- "Unemployed, laid off"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Working fulltime"] <- "Working full time"
GSS$BaseLaborStatus[GSS$BaseLaborStatus == "Working parttime"] <- "Working part time"
GSS$BaseLaborStatus <- factor(GSS$BaseLaborStatus)

Tidyverse approach:

GSS <- GSS %>%
    mutate(tidyLaborStatus =
        recode(LaborStatus,
            `Temp not working` = "Temporarily not working",
            `Unempl, laid off` = "Unemployed, laid off",
            `Working fulltime` = "Working full time",
            `Working parttime ` = "Working part time"))

NIH CBIIT

http://datascience.cancer.gov/

Seminars

NCI Data science webinar series

Reproducibility

Bioinformatics advice I wish I learned 10 years ago from NIH

Project and Data Organization

• Bioinformatics advice I wish I learned 10 years ago.

Project Organization

proj
├── dev
│   ├── clustering.Rmd
│   └── dim_reduce.Rmd
├── doc
├── output
│   ├── 2019-05-10
│   ├── 2019-05-19
│   └── 2019-05-21
├── README.Rmd
├── renv
├── rmd
└── scripts

Data Organization

data
├── annotations
│   ├── clue_drug_repurposing_hub
│   │   ├── repurposing_drugs_20180907.txt
│   │   └── repurposing_samples_20180907.txt
│   └── ...
├── containers
│   └── singularity
│       └── sclc-george2015
├── projects
│   ├── nih
│   │   ├── mm-feature-selection
│   │   ├── mm-p3-variants
│   │   └── sclc-doe
├── public
│   └── human
│       ├── array_express
│       ├── geo
│       │   └── GSE6477
│       │       ├── processed
│       │       │   ├── GSE6477_expr.csv
│       │       │   └── sample_metadata.csv
│       │       └── raw
│       │           ├── GPL96.soft
│       │           └── GSE6477_series_matrix.txt.gz
└── ref
    └── human
        ├── agilent
        ├── gatk
        ├── gencode-v30
        └── rRNA

Container

Data Science for Startups: Containers Building reproducible setups for machine learning

Big data

Spark

• How to analyze log data with Python and Apache Spark
• PyBDA: a command line tool for automated analysis of big biological data sets
• Parallel Computing With R: A Brief Review by Dirk Eddelbuettel
• sparklyr 1.1: Foundations, Books, Lakes and Barriers