MATLAB exercises - #1

First part of MATLAB practice exercises

1. Generate random data for testing purpose
2. Output mean and standard deviation of the data
3. Plot histogram of the data

Part 1.

Generate random data:

• using uniform distribution rand()
• using normal distribution randn()

Using uniform distribution:

x_uniform = rand(100,1); % creates vector of 100 elements samples from uniform distribution, 0<=x<=1

Using uniform distribution:

x_normal = randn(100,1); % creates vector of 100 elements samples from normal distribution

Part 2.

Output mean of x_uniform:

mean(x_uniform)

Now, output mean of x_normal:

Show answer

mean(x_normal)

Output standard deviation of x_uniform:

std(x_uniform)

Now, output standard deviation of x_normal:

Show answer

mean(x_normal)

Part 3.

Plot histogram of the data using hist()function. Do it for both data vectors (random data from uniform and normal distributions)

Show answer

x_uniform = randn(100,1);
hist(x_uniform)

The Cell 9-8

The Cell (Alberts) 9-8 question Antibodies that bind to specific proteins are important tools for defining the locations of molecules in cells. The sensitivity of primary antibody, the antibody that reacts with the target molecule, is often enhanced by using labeled secondary antibodies that bind to it. What are the advantages and disadvantages of using secondary antibodies that carry fluorescent tags versus those that carry bound enzymes? Show answer

A lot of useful information can be found on Wikipedia article Immunohistochemistry . Fluorescent tags on secondary antibody allow for higher signal and labeling multiple targets using orthogonal pairs of antibodies. For example, we can label at the same time tubulin and actin. Secondary antibodies, though, are bulkier and can decrease resolution, especially in super-resolution microscopy. Using multiple antibodies also can increase chance of off-target labeling (when antibody binds elsewhere rather than it's antigen).