Sample Data for RCircos Scatter Plot
(B) Lymphocytes were gated based on scatter properties, from which (C) T cells were identified by CD3 expression. (D, E) T cells were then analyzed for their expression of the lineage markers CD4 and CD8 as well as the activation...
AI代码解释 mr_scatter_plot(res,mydata) 这里的res就是函数mr()运行出来的MR结果,而mydata则是harmonise_data()函数的结果。 这幅图上每一个点其实代表的就是一个IV,每个点上的线实际反映的是95%置信区间,横坐标是SNP对暴露(BMI)的效应,纵坐标是SNP对结局(二型糖尿病)的效应,彩色的线表示的是MR拟合结果。
步骤2:提取结局数据的GWAS > ## 4,从数据库中提取结局变量的的GWAS summary数据,SNP用暴露数据的结果> # Get effects of instruments on outcome> outcome_dat = extract_outcome_data(snps=exposure_dat$SNP, outcomes = "ieu-a-7")Extracting data for 79 SNP(s) from 1 GWAS(s)> dim(outcome_dat)[...
PivotTables: Summarize data on minority and women-owned businesses. SUMIFS: Calculate total revenue per industry category. Scatter Plots: Compare business growth versus employment trends. Dashboard: Create interactive visualizations to track business expansion. ...
The left panel of Figure 10.1 shows a scatterplot of the data. (The data are stored in the file splice.dat which can be obtained as described in Section 1.8 of Chapter 1.) Note that the data appear to be heteroscedastic. The usual 0.95 confidence interval (multiplied by 1000 for ...
res <-mr(dat) #> Analysing 'ieu-a-2' on 'ieu-a-7' p1 <-mr_scatter_plot(res, dat) 4.3.2 森林图 首先使用mr_singlesnp获取单个SNP的结果,然后使用mr_forest_plot绘制森林图 res_single <- mr_singlesnp(dat) p2 <- mr_forest_plot(res_single) p2[[1]] #> Warning: Removed 1 rows co...
5. The Scatter Plot display options in the average and sample data editors now include regression values in the right hand legend (requires select of multiple parameters all with at least 2 data points each). 6. An option has been added to the Processing tab of the File Import Configuration...
UseplotVarianceLinkto plot a scatter plot for each experimental condition (for X and Y conditions), with the sample variance on the common scale versus the estimate of the condition-dependent mean. Use a linear scale for both axes. Include curves for all other linkage options by setting'Compar...
def plot_points_and_axes(x_points, y_points, delta_x, delta_y, offset_x=0, offset_y=0): plt.figure(figsize=(12, 6)) # 绘制原始点 plt.subplot(1, 2, 1) # 第一个子图 plt.scatter(x_points, y_points, color='blue', label='Data Points') ...