BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 151119 Batch 160513 Batch 170203
Condition crowned 6 5 0
Condition worker 3 2 5

Measures of confounding between Batch and Condition

  Standardized Pearson Correlation Coefficient Cramer’s V
Confounding Coefficients (0=no confounding, 1=complete confounding) 0.7166 0.5878

Variation Analysis

Variation explained by Batch and Condition

  Full (Condition+Batch) Condition Batch
Min. 0.049 0 0
1st Qu. 8.098 0.291 4.736
Median 14.87 1.347 11.03
Mean 17.77 3.313 14.16
3rd Qu. 24.76 4.147 20.63
Max. 94.85 59.48 94.76

P-value Analysis

Distribution of Batch and Condition Effect p-values Across Genes

  Min. 1st Qu. Median Mean 3rd Qu. Max. Ps<0.05
Batch P-values 4.125e-10 0.1252 0.3413 0.391 0.6262 1 0.1323
Condition P-values 0.000442 0.3179 0.5402 0.5394 0.7711 1 0.02346

Differential Expression

Expression Plot

Boxplots for all values for each of the samples and are colored by batch membership.

LIMMA

  Condition: worker (logFC) AveExpr t P.Value adj.P.Val B
GNAQ 25.01 37.81 3.763 0.001473 0.9996 -4.593
CX3CR1 132.4 102.9 3.647 0.001905 0.9996 -4.593
GAS7 32.58 36 3.471 0.002804 0.9996 -4.593
DZIP3 5.458 4.571 3.463 0.002857 0.9996 -4.593
CDYL2 7.931 8.524 3.45 0.00294 0.9996 -4.593
PRDM1 15.35 16.86 3.446 0.002962 0.9996 -4.593
CPT1C 6.431 6.286 3.372 0.003483 0.9996 -4.593
CCNDBP1 4.25 2.667 3.303 0.004051 0.9996 -4.593
SULT4A1 5.167 2.524 3.262 0.004433 0.9996 -4.593
SLC6A1 13.03 10.67 3.206 0.00501 0.9996 -4.593

Median Correlations

This plot helps identify outlying samples.

Heatmaps

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.

Circular Dendrogram

This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.

PCA: Principal Component Analysis

PCA

This is a plot of the top two principal components colored by batch to show the batch effects.

Explained Variation

  Proportion of Variance (%) Cumulative Proportion of Variance (%) Percent Variation Explained by Either Condition or Batch Percent Variation Explained by Condition Condition Significance (p-value) Percent Variation Explained by Batch Batch Significance (p-value)
PC1 41.98 41.98 13.1 0.6 0.5413 11.1 0.3194
PC2 10.22 52.21 32.2 0.3 0.8691 32.1 0.03758
PC3 8.156 60.36 10.9 1.9 0.3169 5.3 0.4422
PC4 6.033 66.39 51.1 8.9 0.8939 51 0.00508
PC5 4.381 70.77 26.4 23 0.03642 4.1 0.6792
PC6 3.661 74.44 24.7 6.1 0.82 24.4 0.154
PC7 3.09 77.53 11.7 0.1 0.917 11.7 0.35
PC8 2.894 80.42 14.9 13 0.3147 9.6 0.829
PC9 2.633 83.05 29.5 2.3 0.05 11 0.06275
PC10 2.261 85.31 8.6 2.4 0.9157 8.5 0.5746
PC11 2.091 87.41 13.4 4.2 0.8512 13.2 0.4253
PC12 1.898 89.3 1.6 1 0.8644 1.5 0.9498
PC13 1.815 91.12 9.3 3.8 0.2195 0.6 0.6073
PC14 1.67 92.79 4.3 0.8 0.5149 1.8 0.737
PC15 1.487 94.28 2.5 0 0.9969 2.5 0.8095
PC16 1.44 95.72 26.4 21.9 0.02694 1 0.6001
PC17 1.295 97.01 8.2 2.7 0.5423 6.1 0.6107
PC18 1.163 98.17 1.7 0.1 0.7397 1 0.8769
PC19 1.056 99.23 6.8 6.1 0.3084 0.8 0.9363
PC20 0.7687 100 2.8 0.6 0.9201 2.7 0.8283
PC21 1.377e-28 100 19.4 4.3 0.7407 18.9 0.2319

Shape

This is a heatmap plot showing the variation of gene expression mean, variance, skewness and kurtosis between samples grouped by batch to see the batch effects variation

## Note: Sample-wise p-value is calculated for the variation across samples on the measure across genes. Gene-wise p-value is calculated for the variation of each gene between batches on the measure across each batch. If the data is quantum normalized, then the Sample-wise measure across genes is same for all samples and Gene-wise p-value is a good measure.

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

## Warning in ks.test(gamma.hat[1, ], "pnorm", gamma.bar[1], sqrt(t2[1])): ties should not be present for the Kolmogorov-Smirnov test

## Warning in ks.test(gamma.hat[1, ], "pnorm", gamma.bar[1], sqrt(shinyInput$t2[1])): ties should not be present for the Kolmogorov-Smirnov test
## Warning in ks.test(delta.hat[1, ], invgam): p-value will be approximate in the presence of ties
## Batch mean distribution across genes: Normal vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.04055
## p-value = 0
## 
## 
## Batch Variance distribution across genes: Inverse Gamma vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.0921
## p-value = 0Note: The non-parametric version of ComBat takes much longer time to run and we recommend it only when the shape of the non-parametric curve widely differs such as a bimodal or highly skewed distribution. Otherwise, the difference in batch adjustment is very negligible and parametric version is recommended even if p-value of KS test above is significant.

SVA

Summary

## Number of Surrogate Variables found in the given data: 1