-ct
for total,
-cp
for pre-existing,
-cn
for newly transcribed RNA,
-ca
for attributes), the input file and the filtering methods. We will
filter the data with a numerical threshold of 50 and according to
present/absent calls with two different calls: 'A' and 'M'. Additionally
we use the present/absent call method with the gene names and call '---'
to filter out the probesets without annotated genes.
We set -caPC
to TRUE
since we try to load
present call attributes, in order to speed up the process.
You will use the following parameters (for detailed
explanation of the parameters see here):
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca
Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE
Your complete call will look like this:
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE
-f threshold=50
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:A:1
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:M:1
-f present=Gene~Symbol:---:1
-of Example_mouse_filtered.txt |
Parameters:
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca
Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE Your complete call will look like this:
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE
-f threshold=50
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:A:1
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:M:1
-f present=Gene~Symbol:---:1
-of Example_mouse_filtered.txt
-l standard
-h1 new
-h2 pre
-t 55
-o Example_mouse_halflives.txt
-w halflife
-m new,pre
-plot TRUE |
-uf
) and load separate attributes from the original file (with the
flag
-ca2
). Please note that we have to replace any whitespace characters from
our values with '~' for the program to ran correctly, so we have to call
-ca2
with the label Gene~Symbol
instead of
Gene Symbol
. You also have to define the column of the fasta header that contains
the gene name, and a ratio ( e.g. log(e'/n')) for the comparison of
uracil numbers with this ratio. We will also print the probeset quality
score to a file called
Example_mouse_quality.txt
and plot a histogram of the previous quality control (with the flag
-pp
).
Additionally we will use the probeset quality score to find the best
probeset for each gene.
You will use the following parameters (for detailed
explanation of the parameters see here):
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca
Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE Your complete call will look like this:
-i data/Example_mouse.txt
-ct T1,T2,T3
-cp U1,U2,U3
-cn E1,E2,E3
-ca Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3
-pc TRUE
-ca2 Gene~Symbol
-genelabel Gene~Symbol
-f threshold=50
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:A:1
-f present=Call_T1,Call_T2,Call_T3,Call_U1,Call_U2,Call_U3,Call_E1,Call_E2,Call_E3:M:1
-f present=Gene~Symbol:---:1
-f pqs=min
-uf data/sequences_mouse.txt
-uc 3
-ur "log(e'/n')"
-pqs
Example_mouse_quality.txt
-pp TRUE |
Reading data...
Loading data...
Done loading data.
You have 31451 probesets.
------------------------------
Done reading data
Filtering data...
Filtering data...
Done filtering data.
You have 11031 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10984 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10937 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10731 probesets.
------------------------------
Done filtering data
Writing filtered data into file...
Done writing filtered data
Writing filtered data into file...
Done writing filtered data
Reading data...
Loading data...
Done loading data.
You have 31451 probesets.
------------------------------
Done reading data
Filtering data...
Filtering data...
Done filtering data.
You have 11031 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10984 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10937 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10731 probesets.
------------------------------
Done filtering data
Writing filtered data into file...
Done writing filtered data
Performing normalization...
Starting linear regression...
Done with linear regression.
These are your correction factors:
c_u: 0.8326610520522192
c_l: 0.11605928227524738
------------------------------
Done with normalization
Calculating half-lives...
Starting half-life calculation...
Done calculating half-lives.
------------------------------
Starting half-life calculation...
Done calculating half-lives.
------------------------------
Done calculating half-lives
Writing results into file...
Writing results into file...
Done writing results.
Done writing results
Reading data...
Loading data...
Done loading data.
You have 31451 probesets.
------------------------------
Loading attributes...
Done loading attributes.
------------------------------
Done reading data
Evaluating data...
------------------------------
Starting quality control...
13571 probesets had to be discarded, because no sequence data was available for them.
You have 17881 probesets.
Done with quality control
------------------------------
Done evaluating
Filtering data...
Filtering data...
Done filtering data.
You have 11031 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10984 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10937 probesets.
------------------------------
Filtering data...
Done filtering data.
You have 10731 probesets.
------------------------------
Done filtering data
|