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Title: How to Perform Nearest Centroid Classification with BigQuery
Author: Lauren Hagen
Created: 2019-12-17
URL: https://github.com/isb-cgc/Community-Notebooks/blob/master/Notebooks/How_to_perform_Nearest_Centroid_Classification_with_BigQuery.Rmd
Purpose: Demonstrate using BigQuery to categorize patients based on gene expression using the Nearest Centroid Classification.
Notes:
This notebook is to demonstrate how to use BigQuery to categorize patients based on gene expression using the Nearest Centroid Classifier. We will be using the Kidney renal papillary cell carcinoma (KIRP) study from The Cancer Genome Atlas (TCGA) as an example data set and will use the RNA Sequence Gene Expression Data.
The Nearest Centroid Classifier assigns a label based on the nearest mean (centroid) of the training samples that the observation is closest to1. This classifier is an example of supervised learning and does not create a model for use later2.
Before we get started, we will need to load the BigQuery module, authenticate ourselves, create a client variable, and load necessary libraries.
library(bigrquery)
library(stringr)
library(caret)billing <- 'your_project_number' # Insert your project ID in the ''
if (billing == 'your_project_number') {
print('Please update the project number with your Google Cloud Project')
}We will be using the top three genes with the highest mean expression as the genes we will use to categorize the clinical stage for each case.
top_20_highest_mean_gene_exp <- "SELECT gene_name, AVG(HTSeq__FPKM_UQ) as m
FROM `isb-cgc.TCGA_hg38_data_v0.RNAseq_Gene_Expression`
WHERE project_short_name = 'TCGA-KIRP'
GROUP BY gene_name
ORDER BY m DESC
LIMIT 20"# To see the R console output with query processing information, turn queit to FALSE
top_genes_result <- bq_project_query(billing, top_20_highest_mean_gene_exp, quiet = TRUE)
# Transform the query result into a tibble
top_genes_result <- bq_table_download(top_genes_result, quiet = TRUE)
top_genes_result## # A tibble: 20 x 2
## gene_name m
## <chr> <dbl>
## 1 MT-CO3 527567678.
## 2 MT-CO1 463810408.
## 3 MT-CO2 437422084.
## 4 MT-ND4 418890462.
## 5 MT-RNR2 338365522.
## 6 MT-ATP6 271538283.
## 7 MT-ND3 247704187.
## 8 MT-CYB 239706855.
## 9 FTL 163189324.
## 10 MT-ND2 162676400.
## 11 MT-ND1 160206384.
## 12 MT-ND4L 129925423.
## 13 MT-ATP8 106401853.
## 14 MT-ND6 101825708.
## 15 MT-ND5 84101367.
## 16 MT-RNR1 82018149.
## 17 TMSB10 72683143.
## 18 MT-TP 68068246.
## 19 SPP1 63083630.
## 20 MTATP6P1 40230222.
We will be attempting to predict the clinical stage for a case based on a specific subset of genes and their expression levels.
First, we will filter the TCGA clinical table for our target disease (KIRP) and not include the cases where the clinical stage is missing. We need there to be no missing clinical stage features due to the nature of k nearest algorithms.
filtered_clin <- "
WITH
clin AS (
SELECT
case_barcode,
clinical_stage
FROM
`isb-cgc.TCGA_bioclin_v0.Clinical`
WHERE
disease_code = 'KIRP'
AND clinical_stage <> 'NULL'
), "Next, we will want to get the gene expressions for the genes that we are going to use to attempt to identify the clinical stage. We will filter for the 3 genes that we want, then we will label each case with whether it will be in the training or testing group. For more information on randomization in BigQuery, please see the How to Create a Random Sample in BigQuery Notebook in the ISB-CGC Community Notebook Repository.
filtered_expr <- "
expr AS (
SELECT
case_barcode,
IF ( MOD(ABS(FARM_FINGERPRINT(case_barcode)),10) > 1, 'TRAIN', 'TEST') as class,
gene_name,
HTSeq__FPKM_UQ
FROM
`isb-cgc.TCGA_hg38_data_v0.RNAseq_Gene_Expression`
WHERE
project_short_name = 'TCGA-KIRP'
AND gene_name IN ('MT-CO3','MT-CO1','MT-CO2')
AND (
case_barcode IN (select case_barcode from clin)
)
GROUP BY case_barcode, gene_name, HTSeq__FPKM_UQ
),"We will then find the centroids or means for each clinical stage within the training data.
centroids <- "
mean AS (
SELECT
expr.class,
clin.clinical_stage,
AVG(CASE
WHEN gene_name = 'MT-CO3' THEN HTSeq__FPKM_UQ
END
) AS gene1,
AVG(CASE
WHEN gene_name = 'MT-CO1' THEN HTSeq__FPKM_UQ
END
) AS gene2,
AVG(CASE
WHEN gene_name = 'MT-CO2' THEN HTSeq__FPKM_UQ
END
) AS gene3
FROM
expr
JOIN
clin
ON
expr.case_barcode = clin.case_barcode
WHERE
expr.class = 'TRAIN'
GROUP BY
expr.class,
clin.clinical_stage),"We will also need the testing data to have the genes that we are going to use in the classifier in separate columns for the test cases.
test <- "
test AS (
SELECT
case_barcode,
class,
MAX(CASE WHEN gene_name = 'MT-CO3' THEN HTSeq__FPKM_UQ END) AS gene1,
MAX(CASE WHEN gene_name = 'MT-CO1' THEN HTSeq__FPKM_UQ END) AS gene2,
MAX(CASE WHEN gene_name = 'MT-CO2' THEN HTSeq__FPKM_UQ END) AS gene3
FROM
expr
WHERE
class = 'TEST'
GROUP BY case_barcode, class),"This next section of the query will find the euclidean distance for each case in the testing data set3. The euclidean distance can be found by the following equation4:
[d(q,p) = \sqrt{(q_1-p_1)^2+(q_2-p_2)^2+...+(q_n-p_n)^2}]
dist <- "
distance AS (SELECT
case_barcode,
gene1,
gene2,
SQRT(POWER((SELECT gene1 FROM mean WHERE clinical_stage = 'Stage I')-gene1, 2) + POWER((SELECT gene2 FROM mean WHERE clinical_stage = 'Stage I')-gene2, 2) + POWER((SELECT gene3 FROM mean WHERE clinical_stage = 'Stage I')-gene3, 2)) as stage1,
SQRT(POWER((SELECT gene1 FROM mean WHERE clinical_stage = 'Stage II')- gene1, 2) + POWER((SELECT gene2 FROM mean WHERE clinical_stage = 'Stage II')-gene2, 2) + POWER((SELECT gene3 FROM mean WHERE clinical_stage = 'Stage II')-gene3, 2)) as stage2,
SQRT(POWER((SELECT gene1 FROM mean WHERE clinical_stage = 'Stage III')-gene1, 2) + POWER((SELECT gene2 FROM mean WHERE clinical_stage = 'Stage III')-gene2, 2) + POWER((SELECT gene3 FROM mean WHERE clinical_stage = 'Stage III')-gene3, 2)) as stage3,
SQRT(POWER((SELECT gene1 FROM mean WHERE clinical_stage = 'Stage IV')-gene1, 2) + POWER((SELECT gene2 FROM mean WHERE clinical_stage = 'Stage IV')-gene2, 2) + POWER((SELECT gene3 FROM mean WHERE clinical_stage = 'Stage IV')-gene3, 2)) as stage4,
FROM test),
"Finally, we will calculate which category or stage has the smallest distance from the centroid and assign that stage to the test case. We will also include the case barcode and actual clinical stage as columns in our final result to assist with calculating the accuracy of the classification.
pred <- "
pred AS (SELECT case_barcode,
(CASE WHEN stage1<stage2 AND stage1<stage3 AND stage1<stage4 THEN 'Stage I'
WHEN stage2<stage1 AND stage2<stage3 AND stage2<stage4 THEN 'Stage II'
WHEN stage3<stage1 AND stage3<stage2 AND stage3<stage4 THEN 'Stage III'
ELSE 'Stage IV' END) AS prediction
FROM distance)
SELECT a.case_barcode, a.prediction, b.clinical_stage
FROM pred AS a
JOIN clin AS b
ON a.case_barcode = b.case_barcode
"Now that we have our full query laid out, we can combine all of the sub-queries into the full string and query the data in BigQuery.
# Build the query with all of the subqueries
nc_query <- str_c(filtered_clin, filtered_expr, centroids, test, dist, pred)
# To see the R console output with query processing information, turn queit to FALSE
nc_result <- bq_project_query(billing, nc_query, quiet = TRUE)
# Transform the query result into a tibble
nc <- bq_table_download(nc_result, quiet = TRUE)
nc## # A tibble: 40 x 3
## case_barcode prediction clinical_stage
## <chr> <chr> <chr>
## 1 TCGA-BQ-7055 Stage I Stage I
## 2 TCGA-2Z-A9JK Stage IV Stage III
## 3 TCGA-EV-5903 Stage IV Stage I
## 4 TCGA-2Z-A9JE Stage I Stage I
## 5 TCGA-G7-6797 Stage IV Stage III
## 6 TCGA-AL-3473 Stage IV Stage II
## 7 TCGA-A4-A4ZT Stage II Stage II
## 8 TCGA-BQ-5881 Stage I Stage I
## 9 TCGA-B1-5398 Stage II Stage II
## 10 TCGA-A4-A57E Stage I Stage IV
## # ... with 30 more rows
To find the accuracy of the model, we weil use a confusion matrix and then will calculate the accuracy with the following formula3:
[Accuracy = \frac{M_{ii}}{\sum_{j}M_{ji}}]
# Create a confusion matrix for the clinical predictions and
# calculate the accuracy
confusionMatrix(as.factor(nc$prediction),as.factor(nc$clinical_stage))## Confusion Matrix and Statistics
##
## Reference
## Prediction Stage I Stage II Stage III Stage IV
## Stage I 12 0 1 2
## Stage II 3 2 1 0
## Stage III 1 0 0 0
## Stage IV 13 2 3 0
##
## Overall Statistics
##
## Accuracy : 0.35
## 95% CI : (0.2063, 0.5168)
## No Information Rate : 0.725
## P-Value [Acc > NIR] : 1.000000
##
## Kappa : 0.0545
##
## Mcnemar's Test P-Value : 0.009041
##
## Statistics by Class:
##
## Class: Stage I Class: Stage II Class: Stage III Class: Stage IV
## Sensitivity 0.4138 0.5000 0.0000 0.0000
## Specificity 0.7273 0.8889 0.9714 0.5263
## Pos Pred Value 0.8000 0.3333 0.0000 0.0000
## Neg Pred Value 0.3200 0.9412 0.8718 0.9091
## Prevalence 0.7250 0.1000 0.1250 0.0500
## Detection Rate 0.3000 0.0500 0.0000 0.0000
## Detection Prevalence 0.3750 0.1500 0.0250 0.4500
## Balanced Accuracy 0.5705 0.6944 0.4857 0.2632
It seems that this model has an overall accuracy of 35% and was able to get close to 41% accuracy for Stage 1 but it was not able to accurately predict any of the other classes. We can tell that this is not a good model for this data set though it could easily be updated to different genes or data sets.
1Statistical classification - Wikipedia. n.d. https://en.wikipedia.org/wiki/Statistical_classification.
2Euclidean distance - Wikipedia. n.d. https://en.wikipedia.org/wiki/Euclidean_distance.
3Finding Nearest Neighbors in SQL | Sisense. n.d. https://www.sisense.com/blog/finding-nearest-neighbors-in-sql/.
4Machine Learning with Python: Confusion Matrix in Machine Learning with Python. n.d. https://www.python-course.eu/confusion_matrix.php.