Log In Sign Up
Models:
Alyssa Salazar
AlyssaS/
COVID-19_Multi-Omics
0
Downloads: 1
[bd22c4]: / eda / IJM / proteomics / 6.0_gelsolin_stats.R

Download this file

94 lines (63 with data), 2.1 kB 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
# load proteomics data with clinical metadata

proteomics_data_path = "~/Desktop/UW_2020/COVID19_multi-omics/COVID-19_Multi-Omics/data/proteomics_measurements_w_clinical_metadata.csv"



# note the 'color_by' column represents the four patient groups:

#COVID_ICU    COVID_NONICU    NONCOVID_ICU NONCOVID_NONICU 

#51              51              15              10 



proteomics_df <- read.table(proteomics_data_path, sep=",", header=T)

covid_df <- subset(proteomics_df, COVID == 1)

no_covid_df <- subset(proteomics_df, COVID == 0)



#### 1.	COVID	pGSC ~ Hospital Free Days at Day 45 ####



test_1 <- lm(X7974 ~ Hospital_free_days_45, data=covid_df)

summary(test_1)



# 1.03e-06

# Multiple R-squared:  0.2133

# dof: 100



#### 2.	NO COVID	pGSC ~ Hospital Free Days at Day 45 ####



test_2 <- lm(X7974 ~ Hospital_free_days_45, data=no_covid_df)

summary(test_2)



# p-value: 0.0388

# Multiple R-squared:  0.1728

# dof: 23



#### 3.	COVID_NONICU-COVID_ICU ####



gelsolin.lm <- lm(X7974 ~ color_by, data = proteomics_df)

gelsolin.av <- aov(gelsolin.lm)

summary(gelsolin.av)



test_3 <- TukeyHSD(gelsolin.av)

test_3



# p-value: 4e-07



#### 4. COVID	pGSC ~ Vent Free Days at Day 28 ####



test_4 <- lm(X7974 ~ Vent_free_days, data=covid_df)

summary(test_4)



# p-value: 2.35e-05

# Multiple R-squared:  0.1645

# dof: 100



#### 5. NO COVID	pGSC ~ Vent Free Days at Day 28 ####



test_5 <- lm(X7974 ~ Vent_free_days, data=no_covid_df)

summary(test_5)



# p-value: 0.371

# Multiple R-squared:  0.03492

# dof: 23



#### 6. COVID	pGSC ~ APACHE II ####



test_6 <- lm(X7974 ~ APACHEII, data=covid_df)

summary(test_6)



# p-value: 0.000992

# Multiple R-squared:  0.1774

# dof: 56



#### 7. COVID	pGSC ~ SOFA ####



test_7 <- lm(X7974 ~ SOFA, data=covid_df)

summary(test_7)



# p-value: 3.04e-05

# Multiple R-squared:  0.2732

# dof: 55



#### 8. COVID	pGSC ~ SAPS II ####



test_8 <- lm(X7974 ~ SAPS2, data=covid_df)

summary(test_8)



# p-value: 3.04e-05

# Multiple R-squared:  0.2732

# dof: 55



#### 9. COVID	pGSC ~ P/F ratio ####



test_9 <- lm(X7974 ~ PF_ratio_numeric, data=covid_df)

summary(test_9)



# p-value: 0.0253

# Multiple R-squared:  0.102

# dof: 47