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Clean up dead/hosted code [zh2310] (#2430)

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* Clean up dead/hosted code

* Remove schema conversion tool and related
* Remove cxg references
* Remove locust

* missed a spot

* Remove aws secret manager

* Merge branch 'main' into brodgers/2310/code-cleanup-v1

* cleanup merge
This commit is contained in:
Ben MR
2021-09-17 20:41:12 +00:00
committed by GitHub
parent 69e159916e
commit ef2ab07ca0
35 changed files with 5 additions and 2882 deletions
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backend/test/fixtures/schema_test_data/generate_test_data.sh
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#!/bin/bash
wget "https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz"
tar xf "pbmc3k_filtered_gene_bc_matrices.tar.gz"
python3 - <<MERGE_GENES
import os
from scipy.io import mmread, mmwrite
import scipy.sparse
import pandas as pd
from server.converters.schema import gene_symbol
mat = mmread("filtered_gene_bc_matrices/hg19/matrix.mtx").todense()
genes = pd.read_csv("filtered_gene_bc_matrices/hg19/genes.tsv", sep='\t', names=["gene_id", "gene_symbol"])
upgraded_genes = gene_symbol.get_upgraded_var_index(pd.DataFrame(index=genes["gene_symbol"]))
df = pd.DataFrame(data=mat, index=upgraded_genes).T
merged = df.sum(axis=1, level=0, skipna=False)
os.makedirs("merged")
merged.columns.to_frame().to_csv("merged/genes.tsv", index=False, header=False)
mmwrite("merged/matrix.mtx", scipy.sparse.coo_matrix(merged).T)
MERGE_GENES
cp "filtered_gene_bc_matrices/hg19/barcodes.tsv" "merged/barcodes.tsv"
awk '{print $1"\t"$1}' merged/genes.tsv > genes_tmp.tsv; mv genes_tmp.tsv merged/genes.tsv
echo -e "\n\n\nRunning tutorial on original\n\n\n"
Rscript - <<TUTORIAL
library(Seurat)
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.features = 200)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- RunUMAP(pbmc, dims = 1:10)
saveRDS(pbmc, file = "./seurat_tutorial.rds")
TUTORIAL
echo -e "\n\n\nRunning tutorial on merged\n\n\n"
Rscript - <<TUTORIAL_MERGED
library(Seurat)
pbmc.data <- Read10X(data.dir = "merged/")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.features = 200)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- RunUMAP(pbmc, dims = 1:10)
saveRDS(pbmc, file = "./seurat_tutorial_merged.rds")
TUTORIAL_MERGED
echo -e "\n\n\nRunning SCTransform on original\n\n\n"
Rscript - <<SCTRANSFORM
library(Seurat)
library(sctransform)
pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")
pbmc <- CreateSeuratObject(counts = pbmc.data)
pbmc <- PercentageFeatureSet(pbmc, pattern = "^MT-", col.name = "percent.mt")
pbmc <- SCTransform(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
pbmc <- RunPCA(pbmc, verbose = FALSE)
pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE)
pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE)
pbmc <- FindClusters(pbmc, verbose = FALSE)
saveRDS(pbmc, file = "./sctransform.rds")
SCTRANSFORM
echo -e "\n\n\nRunning SCTransform on merged\n\n\n"
Rscript - <<SCTRANSFORM_MERGED
library(Seurat)
library(sctransform)
pbmc.data <- Read10X(data.dir = "merged/")
pbmc <- CreateSeuratObject(counts = pbmc.data)
pbmc <- PercentageFeatureSet(pbmc, pattern = "^MT-", col.name = "percent.mt")
pbmc <- SCTransform(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
pbmc <- RunPCA(pbmc, verbose = FALSE)
pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE)
pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE)
pbmc <- FindClusters(pbmc, verbose = FALSE)
saveRDS(pbmc, file = "./sctransform_merged.rds")
SCTRANSFORM_MERGED
echo -e "\n\n\nConverting\n\n\n"
Rscript - <<SCEASY
library(sceasy)
srt <- readRDS("seurat_tutorial.rds")
sceasy::convertFormat(srt,
outFile = "seurat_tutorial.h5ad",
from = "seurat",
to = "anndata",
assay = "RNA",
main_layer = "data",
transfer_layers = c("data", "counts", "scale.data"),
drop_single_values = FALSE)
srt <- readRDS("seurat_tutorial_merged.rds")
sceasy::convertFormat(srt,
outFile = "seurat_tutorial_merged.h5ad",
from = "seurat",
to = "anndata",
assay = "RNA",
main_layer = "data",
transfer_layers = c("data", "counts", "scale.data"),
drop_single_values = FALSE)
srt <- readRDS("sctransform.rds")
sceasy::convertFormat(srt,
outFile = "sctransform.h5ad",
from = "seurat",
to = "anndata",
assay = "SCT",
main_layer = "data",
transfer_layers = c("data", "counts", "scale.data"),
drop_single_values = FALSE)
srt <- readRDS("sctransform_merged.rds")
sceasy::convertFormat(srt,
outFile = "sctransform_merged.h5ad",
from = "seurat",
to = "anndata",
assay = "SCT",
main_layer = "data",
transfer_layers = c("data", "counts", "scale.data"),
drop_single_values = FALSE)
SCEASY