× Didn't find what you were looking for? Ask a question
  
  
Top Posters
Since Sunday
39
s
27
9
k
8
B
7
o
7
n
7
x
6
b
6
k
6
m
5
d
5
New Topic  
lifeisadventure lifeisadventure
wrote...
Posts: 3
Rep: 0 0
8 months ago
Using GEPIA database, choosing "pathological stage plot"  GEPIA can automatically generate violin plots of expression data. Thus, researchers can have an idea if the investigated gene expression have some difference in with tumor stage. Could anyone give some detail about the interpretation of the statistics of this graph? For example stgae I and II is spicular some are blunt point? Is it releated with distribution? Some have a black line at their bottom. What does this black line refers to? F value is important and when it is higher is means there is a significant difference? and also Pr>F is smaller than 0.05 is significant. Finally, white dot in the stage images refer to median?
Note: I added figures to the attachment
Thank you very much in advance.
Attached file(s)
Thumbnail(s):
You must login or register to gain access to these attachments.
Read 117 times
1 Reply

Related Topics

Replies
Woo
wrote...
8 months ago
A violin plot is the blend of a box-and-whisker plot and kernel density estimate (KDE), but shows a lot more than a box-and-whisker plot (shown below).



While a box plot only shows summary statistics such as mean/median and interquartile ranges, the violin plot shows the full distribution of the data, as well as summary statistics.

A KDE is the estimation of the unknown density function of the distribution of data. Basically it estimates the unknown density function with the following formula:



K is the kernel — a non-negative function — and h > 0 is a smoothing parameter called the bandwidth.

Benefits of KDE:

1. It can tell us whether is normally distributed or not

2. Whether the data is skewed or not

3. Whether has high variance



Here using KDE we can say that the data is not skewed, approximately normal, the data have not had high variance, etc.

In a violin plot, a KDE plot can be found on both sides of the spinal cord of the plot.

Box plots are not affected by the data’s distribution. When the data “morph” but manage to maintain their stat summaries (medians and ranges), their box plots stay the same whereas the violin plot not only shows the summary statistics it also shows how the data got distributed. It changes its nature greatly as the distribution of data changes.

If the violin got bigger then we can say that the has high variance neatly as compared to the box plot.

New Topic      
Explore
Post your homework questions and get free online help from our incredible volunteers
  218 People Browsing
 503 Signed Up Today
Related Images
  
 154
  
 131
  
 31
Your Opinion
Who's your favorite biologist?
Votes: 245