Tests for checking Batch Effects
Batch 180306 | |
---|---|
Condition crowned | 12 |
Condition worker | 10 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | NA | NA |
Full (Condition+Batch) | Condition | Batch | |
---|---|---|---|
Min. | 0 | 0 | 0 |
1st Qu. | 0.8995 | 0.8995 | 0 |
Median | 3.75 | 3.75 | 0 |
Mean | 6.553 | 6.553 | 0 |
3rd Qu. | 9.447 | 9.447 | 0 |
Max. | 64.72 | 64.72 | 0 |
Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | Ps<0.05 | |
---|---|---|---|---|---|---|---|
Batch P-values | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
Condition P-values | 6.39e-06 | 0.1641 | 0.3879 | 0.4277 | 0.6746 | 1 | 0.0912 |
Boxplots for all values for each of the samples and are colored by batch membership.
Condition: worker (logFC) | AveExpr | t | P.Value | adj.P.Val | B | |
---|---|---|---|---|---|---|
DYNC1I1 | 66.78 | 375.3 | 6.131 | 4.743e-06 | 0.07422 | -1.977 |
DNAJC12 | 36 | 148.4 | 5.215 | 3.806e-05 | 0.1303 | -2.357 |
ATP8A2 | 248.5 | 1166 | 5.152 | 4.409e-05 | 0.1303 | -2.386 |
TAOK3 | 129.1 | 927.1 | 5.115 | 4.804e-05 | 0.1303 | -2.403 |
SLC29A4 | -135 | 315.9 | -5.021 | 5.994e-05 | 0.1303 | -2.448 |
ACPP | 66.48 | 127.1 | 4.96 | 6.898e-05 | 0.1303 | -2.477 |
KCTD17 | -593.9 | 2465 | -4.957 | 6.955e-05 | 0.1303 | -2.478 |
KCNT2 | 35.5 | 71.64 | 4.904 | 7.869e-05 | 0.1303 | -2.504 |
GH1 | -489808 | 1254339 | -4.858 | 8.776e-05 | 0.1303 | -2.526 |
FBP1 | 118 | 583.3 | 4.852 | 8.906e-05 | 0.1303 | -2.529 |
This plot helps identify outlying samples.
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
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 | 18.66 | 18.66 | 9.4 | 9.4 | 0.1645 | 0 | 1 |
PC2 | 10.63 | 29.29 | 10.3 | 10.3 | 0.1457 | 0 | 1 |
PC3 | 8.074 | 37.36 | 8.8 | 8.8 | 0.1793 | 0 | 1 |
PC4 | 7.731 | 45.1 | 0 | 0 | 0.9437 | 0 | 1 |
PC5 | 6.394 | 51.49 | 19.1 | 19.1 | 0.04182 | 0 | 1 |
PC6 | 5.745 | 57.23 | 0.6 | 0.6 | 0.7358 | 0 | 1 |
PC7 | 4.841 | 62.08 | 0 | 0 | 0.9593 | 0 | 1 |
PC8 | 4.548 | 66.62 | 12.9 | 12.9 | 0.1006 | 0 | 1 |
PC9 | 3.652 | 70.28 | 9.3 | 9.3 | 0.1666 | 0 | 1 |
PC10 | 3.244 | 73.52 | 0 | 0 | 0.9367 | 0 | 1 |
PC11 | 3.071 | 76.59 | 12.2 | 12.2 | 0.1108 | 0 | 1 |
PC12 | 2.859 | 79.45 | 0.3 | 0.3 | 0.815 | 0 | 1 |
PC13 | 2.745 | 82.2 | 5.4 | 5.4 | 0.2975 | 0 | 1 |
PC14 | 2.676 | 84.87 | 4.1 | 4.1 | 0.3666 | 0 | 1 |
PC15 | 2.468 | 87.34 | 0.2 | 0.2 | 0.8375 | 0 | 1 |
PC16 | 2.295 | 89.64 | 0.9 | 0.9 | 0.6669 | 0 | 1 |
PC17 | 2.264 | 91.9 | 1.1 | 1.1 | 0.6439 | 0 | 1 |
PC18 | 2.13 | 94.03 | 1.6 | 1.6 | 0.5732 | 0 | 1 |
PC19 | 2.051 | 96.08 | 0.1 | 0.1 | 0.8901 | 0 | 1 |
PC20 | 2.023 | 98.1 | 3.4 | 3.4 | 0.4094 | 0 | 1 |
PC21 | 1.897 | 100 | 0 | 0 | 0.9298 | 0 | 1 |
PC22 | 6.786e-29 | 100 | 1 | 1 | 0.6632 | 0 | 1 |
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.
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.
## Warning in combatPlot(shinyInput$lcounts, batch = shinyInput$batch, mod = mod): There is no batch
## Number of Surrogate Variables found in the given data: 0