10.2 Differential activity analysis
Similar to getDE(), scMINER provides a function, getDA(), to perform the differential activity analysis and identify the group-specific drivers.
## 1. To perform differential expression analysis in a 1-vs-rest manner for all groups
da_res1 <- getDA(input_eset = activity.eset, group_by = "cell_type", use_method = "t.test")## 7 groups were found in group_by column [ cell_type ].
## Since no group was specified, the differential analysis will be conducted among all groups in the group_by column [ cell_type ] in the 1-vs-rest manner.
## 1 / 7 : group 1 ( B ) vs the rest...
## 1912 cells were found for g1.
## 11693 cells were found for g0.
## 2 / 7 : group 1 ( CD4TCM ) vs the rest...
## 2022 cells were found for g1.
## 11583 cells were found for g0.
## 3 / 7 : group 1 ( CD4TN ) vs the rest...
## 2505 cells were found for g1.
## 11100 cells were found for g0.
## 4 / 7 : group 1 ( CD4Treg ) vs the rest...
## 1448 cells were found for g1.
## 12157 cells were found for g0.
## 5 / 7 : group 1 ( CD8TN ) vs the rest...
## 2014 cells were found for g1.
## 11591 cells were found for g0.
## 6 / 7 : group 1 ( Monocyte ) vs the rest...
## 1786 cells were found for g1.
## 11819 cells were found for g0.
## 7 / 7 : group 1 ( NK ) vs the rest...
## 1918 cells were found for g1.
## 11687 cells were found for g0.## feature g1_tag g0_tag g1_avg
## 4 AASDH_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.008071658
## 6 AATF_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.051767485
## 12 ABCB8_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.077615607
## 8 ABCA2_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.081643603
## 10 ABCB1_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.134357577
## 3 AARSD1_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.126010447
## g0_avg g1_pct g0_pct log2FC Pval FDR
## 4 -0.1025141 0.43043933 0.13991277 0.094442475 2.225074e-308 0.000000e+00
## 6 -0.1084165 0.21652720 0.08757376 0.056649005 3.918924e-189 5.878386e-189
## 12 -0.1094585 0.35251046 0.14153767 0.031842866 3.623209e-12 3.952592e-12
## 8 -0.1101867 0.10198745 0.15676045 0.028543082 1.914570e-58 2.418404e-58
## 10 -0.1559384 0.04393305 0.06114770 0.021580866 8.079661e-27 9.233898e-27
## 3 -0.1225746 0.04184100 0.08192936 -0.003435892 4.213744e-02 4.213744e-02
## Zscore
## 4 37.53784
## 6 29.33316
## 12 6.95115
## 8 16.11775
## 10 10.72137
## 3 -2.03216## 2. To perform differential expression analysis in a 1-vs-rest manner for one specific group
da_res2 <- getDA(input_eset = activity.eset, group_by = "cell_type", g1 = c("B"), use_method = "t.test")
## 3. To perform differential expression analysis in a rest-vs-1 manner for one specific group
da_res3 <- getDA(input_eset = activity.eset, group_by = "cell_type", g0 = c("B"), use_method = "t.test")
## 4. To perform differential expression analysis in a 1-vs-1 manner for any two groups
da_res4 <- getDA(input_eset = activity.eset, group_by = "cell_type", g1 = c("CD4Treg"), g0 = c("CD4TCM"), use_method = "t.test")The getTopFeatures() function can aslo be used to easily extract the group-specific markers from the differential expression result:
top_drivers <- getTopFeatures(input_table = da_res1, number = 10, group_by = "g1_tag", sort_by = "log2FC", sort_decreasing = TRUE)
dim(top_drivers)## [1] 16 11## feature g1_tag g0_tag g1_avg
## 4 AASDH_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.008071658
## 6 AATF_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.051767485
## 12 ABCB8_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.077615607
## 8 ABCA2_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.081643603
## 10 ABCB1_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.134357577
## 3 AARSD1_SIG B CD4TCM,CD4TN,CD4Treg,CD8TN,Monocyte,NK -0.126010447
## g0_avg g1_pct g0_pct log2FC Pval FDR
## 4 -0.1025141 0.43043933 0.13991277 0.094442475 2.225074e-308 0.000000e+00
## 6 -0.1084165 0.21652720 0.08757376 0.056649005 3.918924e-189 5.878386e-189
## 12 -0.1094585 0.35251046 0.14153767 0.031842866 3.623209e-12 3.952592e-12
## 8 -0.1101867 0.10198745 0.15676045 0.028543082 1.914570e-58 2.418404e-58
## 10 -0.1559384 0.04393305 0.06114770 0.021580866 8.079661e-27 9.233898e-27
## 3 -0.1225746 0.04184100 0.08192936 -0.003435892 4.213744e-02 4.213744e-02
## Zscore
## 4 37.53784
## 6 29.33316
## 12 6.95115
## 8 16.11775
## 10 10.72137
## 3 -2.03216