Data Summary

This is a summary of the given data matrix for the first 12 genes.

Table continues below

V1	V2	V3	V4	V5	V6
Min. : 3.0	Min. : 0.0	Min. : 0	Min. : 0	Min. : 0	Min. : 0.0
1st Qu.: 952.5	1st Qu.: 12.8	1st Qu.: 2541	1st Qu.: 1231	1st Qu.: 2982	1st Qu.: 107.2
Median : 16976.0	Median : 2723.5	Median : 18304	Median : 5343	Median : 13666	Median : 6351.5
Mean : 38512.8	Mean : 50149.8	Mean : 58634	Mean : 29463	Mean : 33469	Mean : 42320.7
3rd Qu.: 43151.0	3rd Qu.: 39270.5	3rd Qu.: 59952	3rd Qu.: 25138	3rd Qu.: 38146	3rd Qu.: 43330.8
Max. :433393.0	Max. :674276.0	Max. :825606	Max. :562960	Max. :338182	Max. :773652.0

V7	V8	V9	V10	V11	V12
Min. : 0	Min. : 0	Min. : 0.0	Min. : 0.0	Min. : 0.0	Min. : 0
1st Qu.: 3512	1st Qu.: 3320	1st Qu.: 976.5	1st Qu.: 217.8	1st Qu.: 333.8	1st Qu.: 2430
Median : 14372	Median : 14070	Median : 11117.5	Median : 10362.0	Median : 2640.5	Median : 26740
Mean : 43933	Mean : 40692	Mean : 38255.1	Mean : 58424.7	Mean : 38351.8	Mean : 62510
3rd Qu.: 54410	3rd Qu.: 56977	3rd Qu.: 47964.0	3rd Qu.: 56310.5	3rd Qu.: 20305.8	3rd Qu.: 78459
Max. :362156	Max. :238133	Max. :234333.0	Max. :780273.0	Max. :794217.0	Max. :411485

Heatmap

This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.

Sample Correlations

This is a heatmap of the correlation between samples.

This plot helps identify outlying samples.

PCA: Principal Component Analysis

This is a plot of the top two principal components and showing the variation with respect to batch effects and different conditions.

PCA Proportion Variation and correlation

PCA Proportion Variation and correlation Table

Percentage Variation	Cumulative Percentage Variation	Condition Correlation	Batch Correlation
21.3	21.3	6.66	3.61
10.78	32.08	0.04	7.22
7.63	39.71	0.49	0.81
6.68	46.39	0.03	0.84
5.79	52.18	0.09	3.89
5.49	57.67	9.1	11.76
5.04	62.71	6.9	0.69
4.43	67.14	0.93	0.02
3.85	70.99	0.45	0.2
3.34	74.33	0	3.34
3.07	77.4	0.02	3.72
2.75	80.15	0.89	4.79
2.2	82.35	0.09	0.04
1.93	84.28	0.48	0.11
1.79	86.07	1.19	9.36
1.65	87.72	1.53	2.08
1.41	89.13	0.86	1.84
1.2	90.33	0.38	2.02
1.16	91.49	0.36	0.6
0.87	92.36	4.91	0.01
0.83	93.19	8.47	0.4
0.74	93.93	0	1.9
0.72	94.65	0.08	0.08
0.65	95.3	0.44	0.8
0.56	95.86	1.72	0.03
0.55	96.41	0.14	1.66
0.47	96.88	2.51	2.34
0.4	97.28	0.43	3.44
0.36	97.64	2.2	0.5
0.32	97.96	1.95	0.28
0.28	98.24	0.12	0.14
0.26	98.5	0.01	0.72
0.22	98.72	0.02	1.42
0.2	98.92	2.07	0.02
0.19	99.11	0.98	1.18
0.17	99.28	0.7	8.36
0.14	99.42	0.44	0.99
0.12	99.54	5.25	0.9
0.1	99.64	0.29	0.25
0.08	99.72	2.06	0.06
0.05	99.77	1.51	0.81
0.05	99.82	1.6	0.53
0.04	99.86	0.06	0.16
0.03	99.89	0.01	1.01
0.03	99.92	0	1.16
0.02	99.94	0.55	1.47
0.02	99.96	0.02	0.66
0.01	99.97	3.91	0.32
0	99.97	0.66	0.51
0	99.97	5.59	0.06

Combat Plots

This is a plot showing whether parametric or non-parameteric prior is appropriate for this data. It also shows the Kolmogorov-Smirnov test comparing the parametric and non-parameteric prior distribution.

## Found 3 batches
## Adjusting for 1 covariate(s) or covariate level(s)
## Standardizing Data across genes
## Fitting L/S model and finding priors

## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  gamma.hat[1, ]
## D = 0.1321, p-value = 0.3187
## alternative hypothesis: two-sided

Batch Effects testing

This is a summary of the statistical test for batch effects.

## 
## Call:
## lm(formula = pc[, 1] ~ fbatch + fcond)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2124 -0.9805 -0.2123  0.6178 11.7385 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.70903    0.53661  -1.321   0.1918  
## fbatch2     -0.08874    0.65721  -0.135   0.8931  
## fbatch3      1.35003    0.65721   2.054   0.0446 *
## fcond1       0.57721    0.53661   1.076   0.2867  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.078 on 56 degrees of freedom
## Multiple R-squared:  0.1136, Adjusted R-squared:  0.06612 
## F-statistic: 2.392 on 3 and 56 DF,  p-value: 0.07812
## 
## 
## Call:
## lm(formula = pc[, 2] ~ fbatch + fcond)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.5546 -0.5030  0.0587  0.7284  5.7549 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.06335    0.47433   0.134    0.894
## fbatch2     -0.31186    0.58094  -0.537    0.594
## fbatch3      0.37279    0.58094   0.642    0.524
## fcond1      -0.16733    0.47433  -0.353    0.726
## 
## Residual standard error: 1.837 on 56 degrees of freedom
## Multiple R-squared:  0.02637,    Adjusted R-squared:  -0.02578 
## F-statistic: 0.5057 on 3 and 56 DF,  p-value: 0.68
## 
## 
## Call:
## lm(formula = pc[, 3] ~ fbatch + fcond)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9648 -0.9155  0.0228  0.6581  5.2799 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.779111   0.430342   1.810   0.0756 .
## fbatch2     -0.793963   0.527059  -1.506   0.1376  
## fbatch3     -0.005443   0.527059  -0.010   0.9918  
## fcond1      -1.025284   0.430342  -2.382   0.0206 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.667 on 56 degrees of freedom
## Multiple R-squared:  0.1342, Adjusted R-squared:  0.08784 
## F-statistic: 2.894 on 3 and 56 DF,  p-value: 0.04318
## 
## 
## Call:
## lm(formula = pc[, 4] ~ fbatch + fcond)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6322 -0.8311 -0.2285  0.4871  7.2334 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.4656     0.4426  -1.052    0.297
## fbatch2       0.2736     0.5421   0.505    0.616
## fbatch3       0.5671     0.5421   1.046    0.300
## fcond1        0.3708     0.4426   0.838    0.406
## 
## Residual standard error: 1.714 on 56 degrees of freedom
## Multiple R-squared:  0.03108,    Adjusted R-squared:  -0.02082 
## F-statistic: 0.5988 on 3 and 56 DF,  p-value: 0.6184
## 
## 
## Call:
## lm(formula = pc[, 5] ~ fbatch + fcond)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7811 -0.6142  0.1258  0.6663  3.5820 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.7510     0.4236   1.773   0.0817 .
## fbatch2      -0.9589     0.5188  -1.848   0.0699 .
## fbatch3      -0.7490     0.5188  -1.444   0.1544  
## fcond1       -0.3634     0.4236  -0.858   0.3947  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.641 on 56 degrees of freedom
## Multiple R-squared:  0.07455,    Adjusted R-squared:  0.02497 
## F-statistic: 1.504 on 3 and 56 DF,  p-value: 0.2235