Pathology and Social Media
Pathology Tweets
Pathophysiology
Carcinogenesis, Hallmarks of Cancer
Pathology and Social Media
Pathology Tweets

Reproducibility

Reproducible Research

https://www.nature.com/articles/d41586-019-03350-5

A push for reproducibility in biomedical research

http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers

http://statcheck.io/index.php

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

http://statcheck.io/

  • Stat-checking software stirs up psychology

http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049

Coursera: Reproducible Templates for Analysis and Dissemination

https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples

Reproducibility Articles

Document Conversion

  • Pandoc

    • A universal document converter

Other Resources

Reproducible Research

  • A push for reproducibility in biomedical research

http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers

http://statcheck.io/index.php

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

http://statcheck.io/

  • Stat-checking software stirs up psychology

http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049

Coursera: Reproducible Templates for Analysis and Dissemination

https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples

Reproducibility Articles

Document Conversion

  • Pandoc

    • A universal document converter

Other Resources

Reproducibility

Reproducible Research

  • A push for reproducibility in biomedical research

http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers

http://statcheck.io/index.php

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

http://statcheck.io/

  • Stat-checking software stirs up psychology

http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049

Coursera: Reproducible Templates for Analysis and Dissemination

https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples

Reproducibility Articles

Document Conversion

  • Pandoc

    • A universal document converter

Other Resources

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
  • Export R output to a file

https://www.r-bloggers.com/export-r-output-to-a-file/

out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
  • Export R output to a file

https://www.r-bloggers.com/export-r-output-to-a-file/

out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)

Reproducible Reports

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html
library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)
table
html5(table)
latex(table)
index(table)
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")
write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")
tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]",
data=pbc,
pformat = 5)
write(html5(tbl,
fragment=TRUE,
inline="lancet.css",
caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")
index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]
library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")
MDL307_Data <- as.data.frame(MDL307_Data)
names(MDL307_Data)
View(MDL307_Data)
MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"
collist <- c("gleasonskor",
"tersiyer",
"kribriform",
"cerrahisinir",
"ekstaprostatik",
"lenfnodu",
"seminalvezikul"
)
MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)
table <- tangram(biyokimyasalrekurrens ~ yas +
gleasonskor +
tersiyer +
kribriform +
kribriformyuzde +
cerrahisinir +
ekstaprostatik +
lenfnodu +
seminalvezikul +
biyokimyasalrekurrens,
data = MDL307_Data)
table
  • Export R output to a file

https://www.r-bloggers.com/export-r-output-to-a-file/

out <- capture.output(summary(my_very_time_consuming_regression))
cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)