Cluster spread and patient metadata

Data

sample	donor_id	uicc_stage	tumor_stage	ever_smoker	age	dataset	Cluster	meandiff	cl_std	pt_std	cl_cells	pt_cells
Adams_Kaminski_2020_001C	Adams_Kaminski_2020_001C	non-cancer	non-cancer	no	22	Adams_Kaminski_2020	19	0.6152031	0.1807231	0.1224745	2808	9
Adams_Kaminski_2020_003C	Adams_Kaminski_2020_003C	non-cancer	non-cancer	no	67	Adams_Kaminski_2020	19	0.2688007	0.1807231	0.0625149	2808	2
Adams_Kaminski_2020_056CO	Adams_Kaminski_2020_056CO	non-cancer	non-cancer	yes	57	Adams_Kaminski_2020	19	0.3557951	0.1807231	0.0757451	2808	3
Adams_Kaminski_2020_065C	Adams_Kaminski_2020_065C	non-cancer	non-cancer	no	66	Adams_Kaminski_2020	19	0.2707238	0.1807231	0.0574544	2808	2
Adams_Kaminski_2020_065C	Adams_Kaminski_2020_065C	non-cancer	non-cancer	no	66	Adams_Kaminski_2020	5	0.5043957	0.1536721	0.0623073	56444	2
Adams_Kaminski_2020_098C-a	Adams_Kaminski_2020_098C	non-cancer	non-cancer	no	41	Adams_Kaminski_2020	19	0.4186656	0.1807231	0.0919973	2808	4

Cluster 19

## `geom_smooth()` using formula 'y ~ x'

Categorical Regression

## 
## Call:
## lm(formula = pt_std ~ factor(uicc_stage, ordered = F), data = mdata[Cluster == 
##     19])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.062214 -0.027048  0.000105  0.024254  0.068742 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.081050   0.004660  17.395  < 2e-16
## factor(uicc_stage, ordered = F)I         0.004631   0.007367   0.629  0.53051
## factor(uicc_stage, ordered = F)II        0.008718   0.011726   0.743  0.45832
## factor(uicc_stage, ordered = F)III       0.014308   0.009111   1.570  0.11837
## factor(uicc_stage, ordered = F)III or IV 0.025234   0.007861   3.210  0.00161
## factor(uicc_stage, ordered = F)IV        0.006456   0.007593   0.850  0.39645
##                                             
## (Intercept)                              ***
## factor(uicc_stage, ordered = F)I            
## factor(uicc_stage, ordered = F)II           
## factor(uicc_stage, ordered = F)III          
## factor(uicc_stage, ordered = F)III or IV ** 
## factor(uicc_stage, ordered = F)IV           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03228 on 155 degrees of freedom
## Multiple R-squared:  0.06864,    Adjusted R-squared:  0.03859 
## F-statistic: 2.285 on 5 and 155 DF,  p-value: 0.04892

Linear Regression

## 
## Call:
## lm(formula = pt_std ~ as.numeric(uicc_stage), data = mdata[Cluster == 
##     19])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.059566 -0.028960  0.000345  0.022481  0.072502 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.080017   0.004932  16.223   <2e-16 ***
## as.numeric(uicc_stage) 0.002895   0.001327   2.181   0.0306 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03254 on 159 degrees of freedom
## Multiple R-squared:  0.02906,    Adjusted R-squared:  0.02295 
## F-statistic: 4.758 on 1 and 159 DF,  p-value: 0.03063

Cluster 5

## `geom_smooth()` using formula 'y ~ x'

Categorical Regression

## 
## Call:
## lm(formula = pt_std ~ factor(uicc_stage, ordered = F), data = mdata[Cluster == 
##     5])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.065482 -0.018082  0.001068  0.014718  0.071035 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.079288   0.003121  25.402  < 2e-16
## factor(uicc_stage, ordered = F)I         0.015895   0.004997   3.181 0.001645
## factor(uicc_stage, ordered = F)II        0.021147   0.006152   3.437 0.000684
## factor(uicc_stage, ordered = F)III       0.036942   0.006554   5.636 4.51e-08
## factor(uicc_stage, ordered = F)III or IV 0.046284   0.005210   8.884  < 2e-16
## factor(uicc_stage, ordered = F)IV        0.009920   0.004851   2.045 0.041862
##                                             
## (Intercept)                              ***
## factor(uicc_stage, ordered = F)I         ** 
## factor(uicc_stage, ordered = F)II        ***
## factor(uicc_stage, ordered = F)III       ***
## factor(uicc_stage, ordered = F)III or IV ***
## factor(uicc_stage, ordered = F)IV        *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02703 on 260 degrees of freedom
## Multiple R-squared:  0.2681, Adjusted R-squared:  0.254 
## F-statistic: 19.04 on 5 and 260 DF,  p-value: 3.895e-16

Linear Regression

## 
## Call:
## lm(formula = pt_std ~ as.numeric(uicc_stage), data = mdata[Cluster == 
##     5])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.079335 -0.020495 -0.001869  0.020721  0.069626 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.0823665  0.0036165  22.775  < 2e-16 ***
## as.numeric(uicc_stage) 0.0043656  0.0009554   4.569 7.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03019 on 264 degrees of freedom
## Multiple R-squared:  0.07329,    Adjusted R-squared:  0.06978 
## F-statistic: 20.88 on 1 and 264 DF,  p-value: 7.524e-06

Cluster 18

## `geom_smooth()` using formula 'y ~ x'

Categorical Regression

## 
## Call:
## lm(formula = pt_std ~ factor(uicc_stage, ordered = F), data = mdata[Cluster == 
##     18])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.05607 -0.01853 -0.00011  0.01898  0.06313 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.053738   0.006158   8.726 2.76e-15
## factor(uicc_stage, ordered = F)I         0.029957   0.007616   3.934 0.000123
## factor(uicc_stage, ordered = F)II        0.046322   0.008304   5.579 9.74e-08
## factor(uicc_stage, ordered = F)III       0.035970   0.008304   4.332 2.56e-05
## factor(uicc_stage, ordered = F)III or IV 0.034184   0.007893   4.331 2.57e-05
## factor(uicc_stage, ordered = F)IV        0.021921   0.007242   3.027 0.002867
##                                             
## (Intercept)                              ***
## factor(uicc_stage, ordered = F)I         ***
## factor(uicc_stage, ordered = F)II        ***
## factor(uicc_stage, ordered = F)III       ***
## factor(uicc_stage, ordered = F)III or IV ***
## factor(uicc_stage, ordered = F)IV        ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02613 on 165 degrees of freedom
## Multiple R-squared:  0.1865, Adjusted R-squared:  0.1619 
## F-statistic: 7.567 on 5 and 165 DF,  p-value: 2.011e-06

Linear Regression

## 
## Call:
## lm(formula = pt_std ~ as.numeric(uicc_stage), data = mdata[Cluster == 
##     18])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.060531 -0.021248 -0.001227  0.022737  0.054562 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.077683   0.005262  14.762   <2e-16 ***
## as.numeric(uicc_stage) 0.001090   0.001237   0.882    0.379    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02856 on 169 degrees of freedom
## Multiple R-squared:  0.004577,   Adjusted R-squared:  -0.001313 
## F-statistic: 0.7771 on 1 and 169 DF,  p-value: 0.3793

Cluster 13

## `geom_smooth()` using formula 'y ~ x'

Categorical Regression

## 
## Call:
## lm(formula = pt_std ~ factor(uicc_stage, ordered = F), data = mdata[Cluster == 
##     13])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.042485 -0.014971 -0.000693  0.015387  0.063889 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              0.054059   0.004143  13.049  < 2e-16
## factor(uicc_stage, ordered = F)I         0.015337   0.005859   2.618 0.010088
## factor(uicc_stage, ordered = F)II        0.009004   0.008910   1.011 0.314394
## factor(uicc_stage, ordered = F)III       0.034000   0.008513   3.994 0.000117
## factor(uicc_stage, ordered = F)III or IV 0.032689   0.007447   4.390  2.6e-05
## factor(uicc_stage, ordered = F)IV        0.014884   0.005859   2.540 0.012454
##                                             
## (Intercept)                              ***
## factor(uicc_stage, ordered = F)I         *  
## factor(uicc_stage, ordered = F)II           
## factor(uicc_stage, ordered = F)III       ***
## factor(uicc_stage, ordered = F)III or IV ***
## factor(uicc_stage, ordered = F)IV        *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02231 on 111 degrees of freedom
## Multiple R-squared:  0.2031, Adjusted R-squared:  0.1672 
## F-statistic: 5.659 on 5 and 111 DF,  p-value: 0.0001097

Linear Regression

## 
## Call:
## lm(formula = pt_std ~ as.numeric(uicc_stage), data = mdata[Cluster == 
##     13])
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.049698 -0.018628 -0.001927  0.014567  0.081227 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.057546   0.004258  13.514  < 2e-16 ***
## as.numeric(uicc_stage) 0.003294   0.001107   2.974  0.00358 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02366 on 115 degrees of freedom
## Multiple R-squared:  0.07143,    Adjusted R-squared:  0.06336 
## F-statistic: 8.847 on 1 and 115 DF,  p-value: 0.003578

Cluster spread and patient metadata

Yasir Suhail

2023-05-16

Data

Cluster 19

Categorical Regression

Linear Regression

Cluster 5

Categorical Regression

Linear Regression

Cluster 18

Categorical Regression

Linear Regression

Cluster 13

Categorical Regression

Linear Regression