# Visualizing Proteomics Data Read in data ``` ms=pd.read_csv("C:\\Users\duan\Desktop\IntroductionToSeaborn\mass_spec_new.csv") ``` ``` ms ```
``` a = sns.histplot(ms['light Precursor Mz'], bins=20) #simple, right? ```
``` a = sns.histplot(data=ms,x='light Precursor Mz', bins=20, kde="TRUE",color="lightseagreen",alpha=0.1)#make the plot more elegant ```
``` a = sns.histplot(data=ms,x='light +1 charge mass', bins=20, kde="TRUE",color="red",alpha=0.1) #O no, what could be wrong? ```
``` v = ms['light +1 charge mass'].values #get all of the values v.sort() #sort the values v #print the values - what's wrong? ``` ![](https://498238201-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWuHhstIreJ3jFvE4gQ3y%2Fuploads%2FcHPlP9l38Px8Hemc3Qqx%2Fimage.png?alt=media\&token=6e45d8a0-a3c0-4fd4-bc13-6fa974877977) ![](https://498238201-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWuHhstIreJ3jFvE4gQ3y%2Fuploads%2FZ0MTFrXW6xioNtaRuaqm%2Fimage.png?alt=media\&token=7a547f03-3de4-4763-8e62-bf98b43b479a) ``` ms['light +1 charge mass']=ms['light Precursor Mz']*ms['Precursor Charge'] - ((ms['Precursor Charge']-1)*1.0078) ``` ``` a = sns.histplot(data=ms,x='light +1 charge mass', bins=20, kde="TRUE",color="red",alpha=0.1) ```
``` #Let's see if +2 and +3 charged peptides exhibit a different distribution. a = sns.histplot(data=ms.loc[ms['Precursor Charge'] == 3], x='light +1 charge mass', bins=10, label='+3',kde="TRUE",color="chocolate",alpha=0.2) sns.histplot(data=ms.loc[ms['Precursor Charge'] == 2], x='light +1 charge mass', bins=10, ax=a, label='+2',kde="TRUE",color="orchid",alpha=0.1) a.legend() ```
``` #Here, I made a new column that is the ratio of light-to-heavy intensity...the details aren't important. What is important is that we have a new column of values we can plot ms['Total Area Ratio BT2_HFX_5'] = ms['light BT2_HFX_5 Total Area']/ms['15N BT2_HFX_5 Total Area'] ms['Total Area Ratio BT2_HFX_7'] = ms['light BT2_HFX_7 Total Area']/ms['15N BT2_HFX_7 Total Area'] ``` ``` sns.set_context('poster') #you don't need to do this - it's just to make the figures easier to see f = pylab.figure(figsize=(20,10)) swarm = sns.swarmplot(x='Protein Gene', y='Total Area Ratio BT2_HFX_5', data=ms.loc[0:100,:], size=6) pylab.tight_layout() pylab.savefig('swarmplot.png') ```
``` sns.set_context('poster') #you don't need to do this - it's just to make the figures easier to see f = pylab.figure(figsize=(20,10)) box = sns.boxplot(x='Protein Gene', y='Total Area Ratio BT2_HFX_5', data=ms.loc[0:100,:]) pylab.tight_layout() pylab.savefig('boxplot.pdf') #this will save your figure! ```
``` sns.set_context('poster') #you don't need to do this - it's just to make the figures easier to see f = pylab.figure(figsize=(20,10)) box = sns.violinplot(x='Protein Gene', y='Total Area Ratio BT2_HFX_5', data=ms.loc[0:30,:]) #pylab.tight_layout() pylab.savefig('boxplot.pdf') #this will save your figure! ```