Dataset Preprocessing¶

Library loading¶

In [1]:
import scanpy as sc, anndata as ad, numpy as np, pandas as pd
from scipy import sparse
from anndata import AnnData
import warnings
import yaml
import os
import warnings
import socket
import matplotlib as plt


warnings.filterwarnings('ignore')
In [2]:
sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
with open("../data/resources/rcParams.yaml") as f:
    rcParamsDict = yaml.full_load(f)
    for k in rcParamsDict["rcParams"]:
        print("{} {}".format(k,rcParamsDict["rcParams"][k]))
        plt.rcParams[k] = rcParamsDict["rcParams"][k]
    for k1 in set(list(rcParamsDict)).difference(set(["rcParams"])):
        print("{} {}".format(k1,rcParamsDict[k1]))

scanpy==1.8.0 anndata==0.8.0 umap==0.4.6 numpy==1.22.2 scipy==1.6.2 pandas==1.2.3 scikit-learn==0.24.1 statsmodels==0.13.5 python-igraph==0.9.1 louvain==0.7.0 leidenalg==0.8.3
figure.dpi 50
savefig.dpi 500
figure.figsize [10, 10]
axes.facecolor white
dotSize 20
In [3]:
DSname="DownD250"
DSDirName="Sample_S20814_260"

Configure paths¶

In [4]:
outdir = "../data/output"
if not os.path.exists(outdir):
   # Create a new directory because it does not exist
   os.makedirs(outdir)
    
if not os.path.exists("../data/output/adatas"):
   # Create a new directory because it does not exist
   os.makedirs("../data/output/adatas")

with open("../data/resources/iPSC_lines_map.yaml", 'r') as f:
    iPSC_lines_map = yaml.load(f, Loader=yaml.FullLoader)["lines"]
results_file = outdir+'/'+DSname+'.h5ad'
In [5]:
scanpyObj = sc.read_10x_mtx('../data/'+DSDirName+'/filtered_feature_bc_matrix/',  # the directory with the `.mtx` file
    var_names='gene_symbols',                # use gene symbols for the variable names (variables-axis index)
    cache=True)                              # write a cache file for faster subsequent reading

scanpyObj.obs['dataset'] = DSname
scanpyObj.obs_names = [i + "_" + j for i, j in zip(scanpyObj.obs_names.tolist(), scanpyObj.obs["dataset"].tolist())]

... reading from cache file cache/..-data-Sample_S20814_260-filtered_feature_bc_matrix-matrix.h5ad
In [6]:
scanpyObj.var_names_make_unique()
scanpyObj.obs_names_make_unique()

Importing cellIDs¶

In [7]:
cellID = pd.read_csv('../data/'+DSDirName+'/aggregatedCall/aggregatedCall.tsv', sep ="\t",index_col = 0)
cellID
cellID.index = [i + "_" + DSname for i in cellID.index.tolist()]
In [8]:
cellID.Consensus.unique()
Out[8]:
array(['MIFF1', '3391B', 'CROLE', 'A15441', 'DESTa', 'KOLF', 'doublet',
       'LowQuality'], dtype=object)
In [9]:
scanpyObj.obs['cellID'] = cellID.loc[scanpyObj.obs_names, 'Consensus']
In [10]:
scanpyObj.obs
Out[10]:
dataset cellID
AAACCTGAGACAAAGG-1_DownD250 DownD250 doublet
AAACCTGAGAGACTAT-1_DownD250 DownD250 MIFF1
AAACCTGAGCCACTAT-1_DownD250 DownD250 3391B
AAACCTGAGTGTCCAT-1_DownD250 DownD250 CROLE
AAACCTGAGTTATCGC-1_DownD250 DownD250 A15441
... ... ...
TTTGTCAGTCCAAGTT-1_DownD250 DownD250 doublet
TTTGTCAGTCGTTGTA-1_DownD250 DownD250 A15441
TTTGTCAGTGATGCCC-1_DownD250 DownD250 LowQuality
TTTGTCAGTGCACGAA-1_DownD250 DownD250 3391B
TTTGTCATCAATCACG-1_DownD250 DownD250 MIFF1

10448 rows × 2 columns

Configure colors¶

In [11]:
cellID_colors = {}
cellID_newName_colors = {}
cellID_newNames = {}


for line in iPSC_lines_map.keys():
    cellID_colors[iPSC_lines_map[line]["oldName"]] = iPSC_lines_map[line]["color"]
    cellID_newName_colors[iPSC_lines_map[line]["newName"]] = iPSC_lines_map[line]["color"]
    cellID_newNames[iPSC_lines_map[line]["oldName"]] = iPSC_lines_map[line]["newName"]

scanpyObj.obs["cellID"] = scanpyObj.obs["cellID"].astype("category")
scanpyObj.obs["cellID_newName"] = scanpyObj.obs["cellID"].replace(cellID_newNames, inplace=False).astype("category")
scanpyObj.uns["cellID_colors"] = [cellID_colors[line] for line in scanpyObj.obs["cellID"].cat.categories]
scanpyObj.uns["cellID_newName_colors"] = [cellID_newName_colors[line] for line in scanpyObj.obs["cellID_newName"].cat.categories]
In [12]:
[cellID_colors[line] for line in scanpyObj.obs["cellID"].cat.categories]
Out[12]:
['#DBB807',
 '#64f5d9',
 '#006666',
 '#FF00CC',
 '#0FB248',
 '#a8a5a5',
 '#7B00FF',
 '#403b3b']

Preprocessing¶

Show those genes that yield the highest fraction of counts in each single cells, across all cells.

In [13]:
sc.pl.highest_expr_genes(scanpyObj, n_top=20 )

normalizing counts per cell
    finished (0:00:00)

Subsetting according to good barcodes¶

In [14]:
goodBarcodes=pd.read_csv(outdir+'/'+DSname+'_filteredCells.tsv', sep="\t")["BCs"]+"_"+DSname
scanpyObj = scanpyObj[goodBarcodes,:]

QC metrices¶

In [15]:
scanpyObj.var['mt'] = scanpyObj.var_names.str.startswith('MT-')  # annotate the group of mitochondrial genes as 'mt'
sc.pp.calculate_qc_metrics(scanpyObj, qc_vars=['mt'], percent_top=None, log1p=True, inplace=True)

scanpyObj.var['ribo'] = scanpyObj.var_names.str.startswith('RP')  # annotate the group of ribosomal genes as 'ribo'
sc.pp.calculate_qc_metrics(scanpyObj, qc_vars=['ribo'], percent_top=None, log1p=True, inplace=True)
In [16]:
scanpyObj
Out[16]:
AnnData object with n_obs × n_vars = 3035 × 33538
    obs: 'dataset', 'cellID', 'cellID_newName', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'ribo'
    uns: 'cellID_colors', 'cellID_newName_colors'
In [17]:
scanpyObj.var
Out[17]:
gene_ids feature_types mt n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts log1p_total_counts ribo
MIR1302-2HG ENSG00000243485 Gene Expression False 1 0.000329 0.000329 99.967051 1.0 0.693147 False
FAM138A ENSG00000237613 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
OR4F5 ENSG00000186092 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
AL627309.1 ENSG00000238009 Gene Expression False 6 0.001977 0.001975 99.802306 6.0 1.945910 False
AL627309.3 ENSG00000239945 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
... ... ... ... ... ... ... ... ... ... ...
AC233755.2 ENSG00000277856 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
AC233755.1 ENSG00000275063 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
AC240274.1 ENSG00000271254 Gene Expression False 134 0.047117 0.046041 95.584843 143.0 4.969813 False
AC213203.1 ENSG00000277475 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False
FAM231C ENSG00000268674 Gene Expression False 0 0.000000 0.000000 100.000000 0.0 0.000000 False

33538 rows × 10 columns

In [18]:
scanpyObj.obs
Out[18]:
dataset cellID cellID_newName n_genes_by_counts log1p_n_genes_by_counts total_counts log1p_total_counts total_counts_mt log1p_total_counts_mt pct_counts_mt total_counts_ribo log1p_total_counts_ribo pct_counts_ribo
AAACCTGAGAGACTAT-1_DownD250 DownD250 MIFF1 CTL02A 2586 7.858254 6358.0 8.757627 175.0 5.170484 2.752438 1071.0 6.977282 16.844919
AAACCTGCATGGTTGT-1_DownD250 DownD250 KOLF CTL08A 1372 7.224753 2540.0 7.840313 73.0 4.304065 2.874016 349.0 5.857933 13.740158
AAACCTGTCAGTTAGC-1_DownD250 DownD250 3391B CTL01 1216 7.104144 2859.0 7.958577 43.0 3.784190 1.504022 713.0 6.570883 24.938791
AAACGGGCAGATCGGA-1_DownD250 DownD250 3391B CTL01 2043 7.622664 5681.0 8.645059 107.0 4.682131 1.883471 1692.0 7.434258 29.783491
AAACGGGCAGCTATTG-1_DownD250 DownD250 MIFF1 CTL02A 1682 7.428333 3906.0 8.270525 117.0 4.770685 2.995392 1089.0 6.993933 27.880182
... ... ... ... ... ... ... ... ... ... ... ... ... ...
TTTGGTTGTGGCTCCA-1_DownD250 DownD250 MIFF1 CTL02A 1277 7.153052 2764.0 7.924796 25.0 3.258096 0.904486 651.0 6.480044 23.552822
TTTGGTTGTTCGGCAC-1_DownD250 DownD250 MIFF1 CTL02A 898 6.801283 1394.0 7.240650 33.0 3.526361 2.367288 150.0 5.017280 10.760402
TTTGGTTTCAAGGCTT-1_DownD250 DownD250 KOLF CTL08A 2006 7.604396 5483.0 8.609591 239.0 5.480639 4.358928 1246.0 7.128496 22.724787
TTTGTCACAGATGGGT-1_DownD250 DownD250 KOLF CTL08A 1609 7.383989 4091.0 8.316789 138.0 4.934474 3.373258 1103.0 7.006695 26.961622
TTTGTCATCAATCACG-1_DownD250 DownD250 MIFF1 CTL02A 807 6.694562 1245.0 7.127694 21.0 3.091043 1.686747 164.0 5.105946 13.172690

3035 rows × 13 columns

In [19]:
sc.pl.violin(scanpyObj, ['n_genes_by_counts', 'total_counts'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)

sc.pl.violin(scanpyObj, ['total_counts_mt','total_counts_ribo'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)
In [20]:
sc.pl.violin(scanpyObj, ['log1p_n_genes_by_counts', 'log1p_total_counts'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)

sc.pl.violin(scanpyObj, ['log1p_total_counts_mt','log1p_total_counts_ribo'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)
In [21]:
sc.pl.scatter(scanpyObj, x='log1p_total_counts', y='log1p_n_genes_by_counts')
sc.pl.scatter(scanpyObj, x='log1p_total_counts', y='log1p_total_counts_mt')
sc.pl.scatter(scanpyObj, x='log1p_total_counts', y='log1p_total_counts_ribo')
In [22]:
sc.pl.violin(scanpyObj, ['log1p_n_genes_by_counts', 'log1p_total_counts'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)

sc.pl.violin(scanpyObj, ['log1p_total_counts_mt','log1p_total_counts_ribo'],
             groupby= "cellID", jitter=0.4, multi_panel=True, rotation=90)
In [23]:
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_n_genes_by_counts')
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_total_counts_mt')
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_total_counts_ribo')
In [24]:
sc.pl.violin(scanpyObj, ['log1p_n_genes_by_counts', 'log1p_total_counts'],
             groupby= "cellID", jitter=0.4, multi_panel=True)

sc.pl.violin(scanpyObj, ['log1p_total_counts_mt','log1p_total_counts_ribo'],
             groupby= "cellID", jitter=0.4, multi_panel=True)
In [25]:
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_n_genes_by_counts')
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_total_counts_mt')
sc.pl.scatter(scanpyObj, x='total_counts', y='log1p_total_counts_ribo')
In [26]:
scanpyObj.write_h5ad(outdir+'/adatas/'+DSname+'_raw.h5ad')
In [27]:
sc.pp.normalize_total(scanpyObj, exclude_highly_expressed=True, max_fraction=.1)

normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
['MALAT1', 'TTR', 'MT-CO1']
    finished (0:00:00)
In [28]:
sc.pp.log1p(scanpyObj)
In [29]:
scanpyObj.raw = scanpyObj

Subset according to JointHVGs and Filtered Barcodes from previous step¶

In [30]:
HVGs=pd.read_csv(outdir+"/HVG_list_intersection_Curated.txt", sep = "\t")["HVG"]

scanpyObj = scanpyObj[:,HVGs]
scanpyObj.var["highly_variable"] = True
In [31]:
#sc.pp.highly_variable_genes(scanpyObj, min_mean=0.0125, max_mean=5, min_disp=0.5)
In [32]:
#scanpyObj = scanpyObj[:, HVG.tolist()]

#Multiplexing = Multiplexing[:, Multiplexing.var.highly_variable]
In [33]:
#sc.pl.highly_variable_genes(scanpyObj)

Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed. Scale the data to unit variance.

In [34]:
sc.pp.regress_out(scanpyObj, ['total_counts',"pct_counts_mt"])

regressing out ['total_counts', 'pct_counts_mt']
    sparse input is densified and may lead to high memory use
    finished (0:00:09)

Scale each gene to unit variance. Clip values exceeding standard deviation 10.

In [35]:
sc.pp.scale(scanpyObj, max_value=20)
In [36]:
scanpyObj.obs
Out[36]:
dataset cellID cellID_newName n_genes_by_counts log1p_n_genes_by_counts total_counts log1p_total_counts total_counts_mt log1p_total_counts_mt pct_counts_mt total_counts_ribo log1p_total_counts_ribo pct_counts_ribo
AAACCTGAGAGACTAT-1_DownD250 DownD250 MIFF1 CTL02A 2586 7.858254 6358.0 8.757627 175.0 5.170484 2.752438 1071.0 6.977282 16.844919
AAACCTGCATGGTTGT-1_DownD250 DownD250 KOLF CTL08A 1372 7.224753 2540.0 7.840313 73.0 4.304065 2.874016 349.0 5.857933 13.740158
AAACCTGTCAGTTAGC-1_DownD250 DownD250 3391B CTL01 1216 7.104144 2859.0 7.958577 43.0 3.784190 1.504022 713.0 6.570883 24.938791
AAACGGGCAGATCGGA-1_DownD250 DownD250 3391B CTL01 2043 7.622664 5681.0 8.645059 107.0 4.682131 1.883471 1692.0 7.434258 29.783491
AAACGGGCAGCTATTG-1_DownD250 DownD250 MIFF1 CTL02A 1682 7.428333 3906.0 8.270525 117.0 4.770685 2.995392 1089.0 6.993933 27.880182
... ... ... ... ... ... ... ... ... ... ... ... ... ...
TTTGGTTGTGGCTCCA-1_DownD250 DownD250 MIFF1 CTL02A 1277 7.153052 2764.0 7.924796 25.0 3.258096 0.904486 651.0 6.480044 23.552822
TTTGGTTGTTCGGCAC-1_DownD250 DownD250 MIFF1 CTL02A 898 6.801283 1394.0 7.240650 33.0 3.526361 2.367288 150.0 5.017280 10.760402
TTTGGTTTCAAGGCTT-1_DownD250 DownD250 KOLF CTL08A 2006 7.604396 5483.0 8.609591 239.0 5.480639 4.358928 1246.0 7.128496 22.724787
TTTGTCACAGATGGGT-1_DownD250 DownD250 KOLF CTL08A 1609 7.383989 4091.0 8.316789 138.0 4.934474 3.373258 1103.0 7.006695 26.961622
TTTGTCATCAATCACG-1_DownD250 DownD250 MIFF1 CTL02A 807 6.694562 1245.0 7.127694 21.0 3.091043 1.686747 164.0 5.105946 13.172690

3035 rows × 13 columns

Principal component analysis¶

In [37]:
sc.tl.pca(scanpyObj, svd_solver='arpack')

computing PCA
    on highly variable genes
    with n_comps=50
    finished (0:00:00)
In [38]:
sc.pl.pca(scanpyObj, color='MKI67')
In [39]:
sc.pl.pca_variance_ratio(scanpyObj, log=True)
In [40]:
scanpyObj
Out[40]:
AnnData object with n_obs × n_vars = 3035 × 3499
    obs: 'dataset', 'cellID', 'cellID_newName', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'ribo', 'highly_variable', 'mean', 'std'
    uns: 'cellID_colors', 'cellID_newName_colors', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'

Computing the neighborhood graph¶

In [41]:
sc.pp.neighbors(scanpyObj, n_neighbors=10, n_pcs=9)

computing neighbors
    using 'X_pca' with n_pcs = 9
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)

Embedding the neighborhood graph¶

In [42]:
sc.tl.umap(scanpyObj)
scanpyObj

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:03)
Out[42]:
AnnData object with n_obs × n_vars = 3035 × 3499
    obs: 'dataset', 'cellID', 'cellID_newName', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'ribo', 'highly_variable', 'mean', 'std'
    uns: 'cellID_colors', 'cellID_newName_colors', 'log1p', 'pca', 'neighbors', 'umap'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
In [43]:
sc.pl.umap(scanpyObj, color=['MKI67', 'S100B',"NES","PAX6","DCX",'STMN2',"DCN", "BGN"])
In [44]:
sc.tl.diffmap(scanpyObj)

computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
    finished (0:00:00)
    eigenvalues of transition matrix
    [1.         0.99931264 0.99668825 0.9957199  0.9937119  0.9929281
     0.9921038  0.9814363  0.97676903 0.9760958  0.97134644 0.9686266
     0.9669611  0.9621827  0.9587331 ]
    finished: added
    'X_diffmap', diffmap coordinates (adata.obsm)
    'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)
In [45]:
sc.tl.dpt(scanpyObj)

WARNING: No root cell found. To compute pseudotime, pass the index or expression vector of a root cell, one of:
    adata.uns['iroot'] = root_cell_index
    adata.var['xroot'] = adata[root_cell_name, :].X
computing Diffusion Pseudotime using n_dcs=10
    finished: added
 (0:00:00)
In [46]:
sc.pl.diffmap(scanpyObj, color=[ 'MKI67', 'S100B',"NES","PAX6","DCX",'STMN2',"DCN", "BGN"])
In [47]:
scanpyObj.X.max()
Out[47]:
20.0