* Click on the drop-down arrow on your "Pvalue" column.  Select "Custom".  In the window that appears, set a criterion that will filter your data so that the Pvalue has to be less than 0.05.

* Click on the drop-down arrow on your "Pvalue" column.  Select "Custom".  In the window that appears, set a criterion that will filter your data so that the Pvalue has to be less than 0.05.

** '''''How many genes have p value < 0.05? and what is the percentage (out of 5221)?'''''

** '''''How many genes have p value < 0.05? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.01? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.01? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.001? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.001? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.0001? and what is the percentage (out of 5221)?'''''

** '''''What about p < 0.0001? and what is the percentage (out of 5221)?'''''

* When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero less than 5% of the time.

* When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero less than 5% of the time.

* We have just performed 5221 T tests for significance.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 261 times. (Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/].) Since we have more than 261 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your data to determine the following:

* We have just performed 5221 T tests for significance.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 261 times. (Test your understanding: [http://xkcd.com/882/ http://xkcd.com/882/].) Since we have more than 261 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, filter your data to determine the following:

** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 5221)?'''''

** '''''How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 5221)?'''''

** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?'''''

** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?'''''

* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.   

* In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.   

* The "Avg_LogFC_all" tells us the size of the gene expression change and in which direction.  Positive values are increases relative to the control; negative values are decreases relative to the control.

* The "Avg_LogFC_all" tells us the size of the gene expression change and in which direction.  Positive values are increases relative to the control; negative values are decreases relative to the control.

** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero.  '''''How many are there? (and %)'''''

** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero.  '''''How many are there? (and %)'''''

** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero.  '''''How many are there? (and %)'''''

** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero.  '''''How many are there? (and %)'''''

** '''''What about an average log fold change of > 0.25 and p < 0.05? (and %)'''''

** '''''What about an average log fold change of > 0.25 and p < 0.05? (and %)'''''