GraphPadPrism integrates biostatistics, curve fitting, and scientific and technological mapping, and its functions are very practical and refined, including ROC curve analysis, Bland-Altman analysis and other special functions; The plotting function of GraphPadPrism is also very distinctive, there is no need to draw statistical graphs, as long as the data is entered, the chart will be automatically generated and updated in time with the change of data, which is very convenient to use.
Software address: https://9h4gu.top
New features in the official version of Graphpad Prism
First, higher dimensional data!
Prism 9 introduces a number of significant improvements to multivariate data tables. Explore larger data sets using standard structures and perform new and improved analysis with the following improvements:
1. Increased data limit - enter up to 1024 columns of data in each data table
2. Automatic identification of variable types - identify variables in multivariate data tables as continuous values, categorical values or label values
3. Text information in the data table - enter data directly in text form. No need to encode variables like "0" and "1", just enter "Male" and "Female" directly into the datasheet
4. Automatic variable coding - enter your data and let Prism take care of the rest. Prism automatically encodes categorical text variables into numeric "dummy" variables
2. Principal Component Analysis (PCA)
Sometimes, the number of variables collected far exceeds the number of disciplines available for study. Consider a gene expression study, in which expression levels of hundreds or thousands of different genes are measured from subjects divided into two groups: the treatment group and the control group. It may simply be that there are too many variables to fit the model into the data. However, choosing a few variables to exclude from the analysis will only throw away potentially useful information! PCA is a "dimensionality reduction" technique that can be used to reduce the number of variables required while eliminating as little information as possible from the data.
Other features available in PCA include:
1. Select components by parallel analysis (as well as Kaiser method, total variance threshold method, etc.).
2. Generation of gravel plot, fraction plot and double line plot
3. Automatically prepare PCA results for further use in multiple linear regression (principal component regression)
3. Add a new dimension to the drawing
Create bubble charts directly from the original data, coded variables of symbol position (X and Y coordinates), size, and fill color. Note that symbol color and symbol size can be defined using categorical (grouping) or continuous variables.
On this graph, more than 100 countries are displayed as a single circle. The X coordinate of each circle represents the country's GDP (PPP), while the Y coordinate represents the average life expectancy at birth. The size of each symbol is proportional to the population of the country it represents (the two largest symbols represent China and India, respectively). Finally, the color of each symbol represents the continent where the country is located. In this case, this final variable (color) is categorical, but the color in the bubble chart can also be defined by a continuous variable: in this plot, the X coordinate, Y coordinate, and size of the symbol are the same as before. However, now the color of the symbol continuously represents the country's birth rate per 1,000 people. Prism now also has a built-in translucent color scheme so that overlapping symbols can be seen more clearly.
4. Automatically add multiple comparison results to the graph
Simply perform a proper analysis of multiple pairwise comparisons. Then click once to automatically add these results to the drawing. To customize these lines and asterisks, simply click the toolbar button again. Adjust the data or analysis, and the results displayed on the graph are automatically updated. But remember, the P value is only part of the story. Don't forget to report performance estimates (e.g. mean difference is 95% confidence interval!). ）
5. Use the estimation chart to better visualize the T test results
Executed in the test, Prism will now automatically create an estimate plot of the results. On this plot, both sets of raw data will be plotted on the left Y-axis. On the right Y-axis, the difference between the group means and its 95% confidence interval are plotted. This visualization provides more information than the P-value alone because it not only shows whether the 95% CI contains zeros (if the 95% CI contains zeros, the P value will be greater than 0.05); it also shows the width of the 95% CI). If 95%CI does not include zero, the P value will be less than 0.05)