🤔 Aba | 全自动biomarker分析神包！~（原作者用这

写在前面

今天介绍一个Github上的神包吧, 主要是用于Biomarker的临床分析, 原作者用这个包已经发了3篇Nature了, 一起看看吧:👇

< , , >

---

在一些针对Biomarker的临床研究中, 我们常常需对Biomarker进行模型拟合, 预测效果评估等等.🥰
这个包可以完美解决这些问题, 并且直接输出发表级图表, 简单介绍一下用法给大家吧.🥳

用到的包

rm(list = ls())
#devtools::install_github("ncullen93/abaR")
library(aba)
library(tidyverse)
library(ggsci)

示例数据

dat <- adnimerge %>% 
  dplyr::filter(VISCODE == 'bl')

DT::datatable(dat)

变量一览

看一下都有什么变量, 都是什么类型. 🤞

str(dat)

建立模型

5.1 原函数

大家可以通过这种方式来建立模型.

  aba_model(
   data = NULL,
   groups = NULL,
   outcomes = NULL,
   predictors = NULL,
   covariates = NULL,
   stats = NULL,
   evals = NULL,
   include_basic = TRUE
 )

---

5.2 pipeline形式

这里也提供了pipeline的形式来编写代码, 个人也是更倾向于这种方式, 大家试一下吧. 😏

我们在这里设定两个结局指标: ConvertedToAlzheimers和CSF_ABETA_STATUS_bl.🤒
两个预测指标, 即Biomarker, PLASMA_PTAU181_bl和PLASMA_NFL_bl. 🤫

模型为logistic regression. 📈

model <- aba_model() %>% 
  set_data(dat) %>% 
  set_groups(DX_bl %in% c('MCI','AD')) %>% 
  set_outcomes(ConvertedToAlzheimers, CSF_ABETA_STATUS_bl) %>% 
  set_predictors(
    PLASMA_PTAU181_bl,
    PLASMA_NFL_bl,
    c(PLASMA_PTAU181_bl, PLASMA_NFL_bl)
  ) %>% 
  set_covariates(AGE, GENDER, EDUCATION) %>% 
  set_stats(stat_glm(std.beta=T))

model

---

Note! 这里我们注意下如何进行Biomarker的联合应用, 可写为 c(PLASMA_PTAU181_bl, PLASMA_NFL_bl). 😘

---

5.3 拟合

model <- model %>% 
  fit()
model

---

5.4 模型数据

拟合完以后我们就获得了模型的结果, 大家可以大致看一下.🤓

model_summary <- model %>% 
  summary()

model_summary

模型结果的可视化

这个包提供了很多可视化的参数, 可以直接将summary的结果传递给画图函数, 非常简单. 😘

6.1 coeffficients可视化

这里需要说明一下, 函数内自带的配色只有4种, 即jama, nature, lancet, none.
但是大家可以按照ggplot语法更改颜色.🤜

model_summary %>% 
  aba_plot_coef(coord_flip=T,
                palette = 'nature') 

---

6.2 AUC可视化

看看各个模型的AUC吧.😗

model_summary %>% 
  aba_plot_metric(palette = 'nature')

---

6.3 ROC可视化

model_summary %>% 
  aba_plot_roc()

---

6.4 Risk density plot

批量出图, 大家想看哪个predictor的Risk density plot就提取哪个吧. 🤪

fig <- model %>%
  aba_plot_risk_density()
fig

---

随便提取一个看看吧~~~😉

fig$fig[1]

补充一下

这个包还有很多其他强大的功能， 大家可以去进一步地探索一下。😘

欢迎大家留言说一下其他强大的函数哦~

---

这里附上所有函数官方解释：👇

• aba_adjust()

  Create an aba_adjust object.

• aba_control()

  Create an aba control object.

• aba_demographics()

  Create a demographics table from a fitted aba model.

• aba_diagnosticpower()

  Caclulate diagnostic power based on a fitted aba model

• aba_emmeans()

  Calculated estimated marginal means.

• aba_evaluate()

  Evaluate a fitted aba model on new data

• aba_fit()

  Fit an aba model.

• aba_longpower()

  Run power analysis on a longitudinal-based aba model.

• aba_model()

  Create an aba model.

• aba_plot()

  Plot an aba object

• aba_plot_coef()

  Plot coefficients of an aba model summary

• aba_plot_metric()

  Plot metrics of an aba model summary

• aba_plot_predictor_risk()

  Plot predictor values versus predicted risk from fitted aba model

• aba_plot_risk_density()

  Plot risk density split by binary outcome class

• aba_plot_roc()

  Plot ROC curves from an aba model

• aba_predict()

  Get individual predictions from a fitted aba model

• aba_read()

  Read an aba object from file

• aba_robust()

  Evaluate the robustness of an aba model to systematic and random error.

• aba_screen()

  Create an aba screen object.

• aba_selection()

  Run model selection on an aba model.

• aba_summary()

  Summarise a fitted aba model.

• aba_write()

  Write an aba object to file.

• adnimerge

  A sample of ADNI data in long format

• all_combos()

  Create all possible combinations of a set of variables

• all_levels()

  Create groups from all levels of one or more variables

• as_reactable()

  Convert an aba summary to a interactive react table

• as_reactable(*<abaSummary>*)

  Convert an aba summary to a interactive react table

• as_table()

  Convert an aba summary to a nicely formatted table

• as_table(*<abaSummary>*)

  Convert an aba summary to a nicely formatted table

• eval_boot()

  Create a bootstrap evaluator

• eval_cv()

  Create a cross validation evaluator

• eval_standard()

  Create a standard evaluator

• eval_traintest()

  Create a train-test evaluator

• everyone()

  Use all data rows as a group in an aba model.

• fit(*<abaModel>*)

  Fit an aba model.

• predict(*<abaModel>*)

  Get individual predictions from a fitted aba model

• set_covariates()

  Set the covariates of an aba model.

• set_data()

  Set the data of an aba model

• set_evals()

  Set the evals of an aba model

• set_groups()

  Set the groups of an aba model.

• set_outcomes()

  Set the outcomes of an aba model.

• set_predictors()

  Set the predictors of an aba model.

• set_stats()

  Set the stats of an aba model

• stat_ancova()

  Create an ancova stat object.

• stat_cox()

  Create a glm stat object.

• stat_glm()

  Create a glm stat object.

• stat_lm()

  Create an lm stat object.

• stat_lme()

  Create an lme stat object.

• stat_lmer()

  Create an lmer stat object.

• stat_mmrm()

  Create an mmrm stat object.

• stat_retest()

  Create a retest stat object.

• stat_roc()

  Create a roc stat object.

• theme_aba()

  Custom aba ggplot2 theme

---

<img src="https://img.haomeiwen.com/i24475539/a8d99c85dde3e123.png" alt="果冻" style="zoom:25%;" />

<center>最后祝大家早日不卷!~</center>

---

点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰

<center> <b>📍 往期精彩 <b> </center>

📍 <font size=1>🤩 ComplexHeatmap | 颜狗写的高颜值热图代码！</font>
📍 <font size=1>🤥 ComplexHeatmap | 你的热图注释还挤在一起看不清吗！？</font>
📍 <font size=1>🤨 Google | 谷歌翻译崩了我们怎么办！？（附完美解决方案）</font>
📍 <font size=1>🤩 scRNA-seq | 吐血整理的单细胞入门教程</font>
📍 <font size=1>🤣 NetworkD3 | 让我们一起画个动态的桑基图吧~</font>
📍 <font size=1>🤩 RColorBrewer | 再多的配色也能轻松搞定！~</font>
📍 <font size=1>🧐 rms | 批量完成你的线性回归</font>
📍 <font size=1>🤩 CMplot | 完美复刻Nature上的曼哈顿图</font>
📍 <font size=1>🤠 Network | 高颜值动态网络可视化工具</font>
📍 <font size=1>🤗 boxjitter | 完美复刻Nature上的高颜值统计图</font>
📍 <font size=1>🤫 linkET | 完美解决ggcor安装失败方案（附教程）</font>
📍 <font size=1>......</font>

本文由mdnice多平台发布