Last updated: 2019-02-05

workflowr checks: (Click a bullet for more information)

• ✔ R Markdown file: up-to-date

  Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

• ✔ Repository version: c77dbd3

  Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
  
  Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

  
  Ignored files:
      Ignored:    .DS_Store
      Ignored:    docs/figure/
  
  Unstaged changes:
      Modified:   analysis/_site.yml
  
  

  Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	c77dbd3	courtneyschiffman	2019-02-05	wflow_publish(“analysis/publications.Rmd”)
html	b4202aa	courtneyschiffman	2018-12-13	Build site.
Rmd	d9439d5	courtneyschiffman	2018-12-13	wflow_publish(c(“analysis/about.Rmd”, “analysis/publications.Rmd”,
html	d0ccc3c	courtneyschiffman	2018-12-13	Build site.
html	b4fb801	courtneyschiffman	2018-12-13	Build site.
Rmd	8886030	courtneyschiffman	2018-12-13	wflow_publish(c(“analysis/cv.Rmd”, “analysis/”))

---

Kang, et al., “Electrophoretic cytopathology resolves ERB2 forms with single-cell”, NPJ Precision Oncology, 2018.

Perttula, et al., “Lipidomic features associated with colorectal cancer in a prospective cohort”, BMC Cancer, 2018.

Petrick, et al., “An untargeted metabolomics method for archived newborn dried blood spots in epidemiological studies”, Metabolomics, 2017.

Petrick, et al., “Metabolomics of Neonatal Blood Spots Reveals Lipid Associations with Pediatric Acute Lymphoblastic Leukemia,” Cancer Letters, 2019. Under review.

Schiffman et al., “SIDEseq: a cell similarity measure defined by shared identified differentially expressed genes for single-cell RNA-sequencing data”, Statistics in Biosciences, 2017.

Schiffman et al., “Identification of gene expression predictors of occupational benzene exposure,” PLOS ONE, 2018.

Schiffman et al., “Data-adaptive pipeline for filtering and normalizing metabolomics data.,” bioRxiv, 2018, doi: https://doi.org/10.1101/387365.

Yano, et al., “Untargeted Adductomics of Cys34 Modification to Human Serum Albumin in Newborn Dried Blood Spots,” ABC, 2019.

---

This reproducible R Markdown analysis was created with workflowr 1.1.1

---