Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add a QC condition for phenotype_imputation.ipynb #1061

Merged
merged 4 commits into from
Oct 1, 2024
Merged

Add a QC condition for phenotype_imputation.ipynb #1061

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
471b02e
update QC for all steps except for gEBMF
rl3328 Sep 30, 2024
e200dab
gEBMF not resolved
rl3328 Sep 30, 2024
8f7d250
Update containers.csv
rl3328 Oct 1, 2024
657b110
Update YAML files and Markdown table
rl3328 Oct 1, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
159 changes: 136 additions & 23 deletions code/data_preprocessing/phenotype/phenotype_imputation.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -178,7 +178,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "9073376b-b7e5-46d6-ae70-54229fa72453",
"metadata": {
"kernel": "SoS"
Expand Down Expand Up @@ -211,6 +211,8 @@
" Work directory & output directory\n",
" --phenoFile VAL (as path, required)\n",
" Molecular phenotype matrix\n",
" --[no-]qc-prior-to-impute (default to True)\n",
" QC before imputation to remove unqualified features\n",
" --job-size 1 (as int)\n",
" For cluster jobs, number commands to run per job\n",
" --walltime 72h\n",
Expand Down Expand Up @@ -285,6 +287,8 @@
"parameter: cwd = path(\"output\")\n",
"# Molecular phenotype matrix\n",
"parameter: phenoFile = path\n",
"# QC before imputation to remove unqualified features \n",
"parameter: qc_prior_to_impute = True\n",
"# For cluster jobs, number commands to run per job\n",
"parameter: job_size = 1\n",
"# Wall clock time expected\n",
Expand Down Expand Up @@ -312,7 +316,6 @@
"parameter: prior = \"ebnm_point_laplace\"\n",
"parameter: varType = '1'\n",
"parameter: num_factor = '60'\n",
"parameter: qc_prior_to_impute = True\n",
"input: phenoFile\n",
"output: f'{cwd:a}/{_input:bn}.imputed.bed.gz'\n",
"task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_output:bn}', cores = numThreads \n",
Expand All @@ -327,8 +330,8 @@
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where all values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)]))) == (ncol(pheno) -4))\n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
Expand Down Expand Up @@ -417,7 +420,22 @@
" library(\"softImpute\")\n",
" \n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" pheno_range <- range(unlist(as.list(as.data.frame(pheno_NAs))), na.rm = TRUE) \n",
" if (pheno_range[1] >= 0 && pheno_range[2] <= 1) {\n",
" dat <- qnorm(as.matrix(pheno_NAs))\n",
Expand Down Expand Up @@ -452,7 +470,7 @@
" pheno_imp <- x_imp\n",
" }\n",
" \n",
" write_delim(cbind(pheno[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\")\n",
" write_delim(cbind(pheno_qc[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\")\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -488,11 +506,28 @@
" library(\"tibble\")\n",
" library(\"readr\")\n",
" library(\"dplyr\")\n",
" \n",
" library(\"missForest\")\n",
"\n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" pheno_imp <- missForest(as.matrix(pheno_NAs), parallelize = 'variables')$ximp\n",
" write_delim(cbind(pheno[, 1:4], pheno_imp), ${_output:r}, delim = \"\\t\" )\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" pheno_imp <- missForest(as.matrix(pheno_NAs), parallelize = 'no')$ximp\n",
" # If ’variables’ the data is split into pieces of the size equal to the number of cores registered in the parallel backend.\n",
" write_delim(cbind(pheno_qc[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -524,15 +559,31 @@
"output: f'{cwd:a}/{_input:bn}.imputed.bed.gz'\n",
"task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_output:bn}' \n",
"R: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container = container, entrypoint=entrypoint\n",
" source('/mnt/vast/hpc/csg/zq2209/xgb_imp.R')\n",
" source('./xgb_imp.R')\n",
" library(\"tibble\")\n",
" library(\"readr\")\n",
" library(\"dplyr\")\n",
" \n",
" library(\"missForest\")\n",
"\n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" pheno_imp <- xgboost_imputation(as.matrix(pheno_NAs))\n",
" write_delim(cbind(pheno[, 1:4], pheno_imp), ${_output:r}, delim = \"\\t\" )\n",
" write_delim(cbind(pheno_qc[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -569,9 +620,26 @@
" library(\"dplyr\")\n",
" \n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" pheno_NAs = pheno_NAs %>% select(where(~mean(is.na(.)) < 0.8))\n",
" # I removed columns that have more than 80 % missing values, cuz knn cannot impute them. \n",
" pheno_imp <- impute.knn(as.matrix(pheno_NAs), rowmax = 1)$data\n",
" write_delim(cbind(pheno[, 1:4], pheno_imp), ${_output:r}, delim = \"\\t\" )\n",
" write_delim(cbind(pheno_qc[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -608,12 +676,27 @@
" library(\"dplyr\")\n",
" \n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" X_mis_C <- as(as.matrix(pheno_NAs), \"Incomplete\")\n",
" ###uses \"svd\" algorithm\n",
" fit <- softImpute(X_mis_C,rank = 50,lambda = 30,type = \"svd\")\n",
" pheno_imp <- complete(as.matrix(pheno_NAs),fit)\n",
" write_delim(cbind(pheno[, 1:4], pheno_imp), ${_output:r}, delim = \"\\t\" )\n",
" write_delim(cbind(pheno_qc[, 1:4], pheno_imp), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -649,12 +732,27 @@
" library(\"dplyr\")\n",
" \n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" Yfill <- as.matrix(pheno_NAs)\n",
" for (t.row in 1:nrow(pheno_NAs)) {\n",
" Yfill[t.row, is.na(Yfill[t.row,])] <- rowMeans(Yfill, na.rm = TRUE)[t.row] \n",
" } \n",
" write_delim(cbind(pheno[, 1:4], Yfill), ${_output:r}, delim = \"\\t\" )\n",
" write_delim(cbind(pheno_qc[, 1:4], Yfill), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -690,12 +788,27 @@
" library(\"dplyr\")\n",
" \n",
" pheno <- read_delim(${_input:ar}, delim = \"\\t\")\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" # Get index of features that have > 40% missing \n",
" miss.ind <- which(rowSums(is.na(pheno[, 5:ncol(pheno)]))/(ncol(pheno) -4) > 0.4) \n",
" # Get index of features where more than 95% values are zero.\n",
" value0.ind <- which(rowSums((pheno[, 5:ncol(pheno)] == 0 | is.na(pheno[, 5:ncol(pheno)])))/(ncol(pheno) -4) > 0.95)\n",
" miss.ind <- c(miss.ind, value0.ind)\n",
" # remove these rows if qc_prior_to_impute = True\n",
" if (${\"TRUE\" if qc_prior_to_impute else \"FALSE\"}) {\n",
" if (length(miss.ind) > 0) {\n",
" pheno_qc <- pheno[-miss.ind, ]\n",
" } else {\n",
" pheno_qc <- pheno\n",
" }\n",
" pheno_NAs <- pheno_qc[, 5:ncol(pheno_qc)]\n",
" } else {\n",
" pheno_NAs <- pheno[, 5:ncol(pheno)]\n",
" }\n",
" Yfill <- as.matrix(pheno_NAs)\n",
" for (t.row in 1:nrow(pheno_NAs)) {\n",
" Yfill[t.row, is.na(Yfill[t.row,])] <- min(Yfill[t.row, ], na.rm = TRUE)\n",
" }\n",
" write_delim(cbind(pheno[, 1:4], Yfill), ${_output:r}, delim = \"\\t\" )\n",
" write_delim(cbind(pheno_qc[, 1:4], Yfill), \"${_output:n}\", delim = \"\\t\" )\n",
"\n",
"bash: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" bgzip -f ${_output:n}\n",
Expand Down Expand Up @@ -732,7 +845,7 @@
"# with missing rate smaller than tol_missing will be mean_imputed. Say if we want to keep rows with less than 5% missing, then we use 0.05 as tol_missing.\n",
"parameter: tol_missing = 0.05\n",
"input: phenoFile\n",
"output: f'{_input:nn}.filter_na.{impute_method}.bed.gz'\n",
"output: f'{cwd:a}/{_input:nn}.filter_na.{impute_method}.bed.gz'\n",
"task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads\n",
"R: expand= \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', container=container, entrypoint=entrypoint\n",
" library(\"dplyr\")\n",
Expand Down
Loading
Loading