Comparing cell-cell communication among different conditions

[liaojinyue]

Hi Rongbin,

Thank you for providing such a useful tool. I've tested Mebocost on my personal scRNA-seq dataset, and it worked beautifully. I’m wondering whether you will include functions for direct comparison among different groups/conditions as shown in the figure 5 in the preprint? Could you perhaps provide the script that was used to create these figures?

Thanks, Jason

[zhengrongbin]

Hello, Thank you for your interest in our work! The way I generated figure 5 was by running a prediction for each condition and then identifying highly changed communications based on the index of dispersion of communication score across conditions. I am happy to share that chunk of the script with you, please send an email at Rongbin.Zheng@childrens.harvard.edu, I will send you the script by email. Best, Rongbin

[DrZGQ37]

Hello, Thank you for your interest in our work! The way I generated figure 5 was by running a prediction for each condition and then identifying highly changed communications based on the index of dispersion of communication score across conditions. I am happy to share that chunk of the script with you, please send an email at Rongbin.Zheng@childrens.harvard.edu, I will send you the script by email. Best, Rongbin

Dear Rongbin, I am glad to see that you could share the script that identified highly changed communications across different conditions. I think MEBOCOST comparing cell-cell communication among different conditions/groups could expand its applicability for more scRNA-seq datasets. I've tested Mebocost on my personal scRNA-seq dataset, and it basically worked well. However, my scRNA-seq dataset include two conditions and I want to compare the cell communications between these conditions. I am very appreciated that if you could send me the scripts that compared cell-cell communication among different conditions/groups by email? My email address is zhuguiqi37@163.com. Thank you very much. Looking forward to your reply.

Best, Guiqi Zhu

[zhengrongbin]

Here is what I used to find condition-specific communications. The idea is to run mebocost by pooling all cells but label each cell type by each condition. In this way, mebocost use same amount of cells for background estimation in permutation testing, and the communication score and significance are comparable.

create a new label of cell group by combining cell type and condition

for example

adata.obs['label'] = adata.obs['cell_type'] + '~' + adata.obs['condition']

run mebocost by giving "label" as cell group column

please check mebocost tutorial for how to run mebocost

cellall_mebo is the object of mebocost result

cellall_mebo = mebocost.load_obj(path = './scBAT_mebocost_allcond.pk') 

focus on communications in same condition

commu_res = cellall_mebo.commu_res.copy()

you can modify this chunk of code based on your data, here I write this as my cell group labeled as celltype~condition, keep it if it's same

commu_res['sender_cond'] = [x.split("~")[-1] for x in commu_res['Sender'].tolist()]
commu_res['receiver_cond'] = [x.split("~")[-1] for x in commu_res['Receiver'].tolist()]
commu_res_new = pd.DataFrame()
for c in commu_res['sender_cond'].unique().tolist():
    tmp = commu_res[(commu_res['sender_cond'] == c) & (commu_res['receiver_cond'] == c)]
    commu_res_new = pd.concat([commu_res_new, tmp])

cellall_mebo.commu_res = commu_res_new.copy()

find significant communication in at least one condition

commu_res_new['label'] = commu_res_new['Sender'].apply(lambda x: x.split('~')[0])+'~'+commu_res_new['Metabolite_Name']+'~'+commu_res_new['Sensor']+'~'+commu_res_new['Receiver'].apply(lambda x: x.split('~')[0])
significant = commu_res_new[commu_res_new['permutation_test_fdr'] < 0.05]

significant

commu_res_need = commu_res_new[commu_res_new['label'].isin(significant['label'])]

commu_res_need_mat = commu_res_need.pivot_table(index = 'label', columns = 'sender_cond', values = 'Commu_Score')

Index of dispersion

IOD = commu_res_need_mat.apply(lambda row: np.var(row)/np.mean(row), axis = 1).sort_values(ascending=False)

select top 100 based on IOD

top_n = 100
most_var_commu = commu_res_need_mat.loc[IOD.head(top_n).index]
most_var_commu = pd.concat([most_var_commu,
                            pd.DataFrame(most_var_commu.index.str.split('~').tolist(),
                                         index = most_var_commu.index, 
                                         columns = ['Sender', 'Met', 'Sensor', 'Receiver'])],
                          axis = 1)

then you can plot by this data frame: most_var_commu

[Yiting-T]

Dear Dear Rongbin, I am very grateful for the extremely useful tool you have provided. I tested Mebocost on the data you provided, and it performed well. However, when I applied Mebocost to my own database, I noticed that some cells were not represented in the visualizations. Specifically, I would like to know if the bar plots are capable of displaying cells with zero metabolic communication scores. It would be greatly helpful if you could provide some insights into this matter or suggest any potential solutions to ensure that all cells are accounted for in the visualizations. Thank you very much for your time and assistance. Looking forward to your response. Best regards, Ting Yi

[zhengrongbin]

The mebocost generates figures only based on significant metabolite-sensor communications (usually is FDR < 0.05 and Commu_Score > 0) since we focus on cell types with communications in most cases. Can you try to run several lines of scripts to draw the figure by yourself if the cell type with 0 communication number has to be displayed? The following code will be helpful: `## to obtain the data matrix for the bar plot ptmp=obj.CP._matrixcommu(comm_res=obj.comm_res, pval_method='permutation_test_fdr', pval_cutoff=0.05, comm_score_col = 'Commu_Score', comm_score_cutoff =0)

count

sender_n = ptmp.groupby('Sender')['Sender'].count() receiver_n = ptmp.groupby('Receiver')['Receiver'].count()

plot sender and receiver number

df = pd.concat([sender_n, receiver_n], axis = 1).fillna(0) df = df.sort_values('Sender', ascending = False)`

You can add cell type with zero communications to the df data frame, then plot the bar plot using matplotlib or seaborn.

Let me know if you still have any questions.