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="DownD100"
DSDirName="Sample_S31807_MET6"

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_S31807_MET6-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(['809', 'KOLF', 'MIFF1', 'doublet', 'LowQuality'], dtype=object)
In [9]:
scanpyObj.obs['cellID'] = cellID.loc[scanpyObj.obs_names, 'Consensus']
In [10]:
scanpyObj.obs
Out[10]:
dataset cellID
AAACCTGAGAAACCGC-1_DownD100 DownD100 809
AAACCTGAGACAGAGA-1_DownD100 DownD100 KOLF
AAACCTGAGAGATGAG-1_DownD100 DownD100 809
AAACCTGAGAGTCTGG-1_DownD100 DownD100 LowQuality
AAACCTGAGCACCGCT-1_DownD100 DownD100 doublet
... ... ...
TTTGTCATCCAACCAA-1_DownD100 DownD100 809
TTTGTCATCCAGTATG-1_DownD100 DownD100 doublet
TTTGTCATCGCAAGCC-1_DownD100 DownD100 MIFF1
TTTGTCATCTAAGCCA-1_DownD100 DownD100 MIFF1
TTTGTCATCTCAAACG-1_DownD100 DownD100 809

14017 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]:
['#FF0054', '#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 = 2460 × 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 0 0.000000 0.000000 100.000000 0.0 0.000000 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 9 0.003659 0.003652 99.634146 9.0 2.302585 False
AL627309.3 ENSG00000239945 Gene Expression False 4 0.001626 0.001625 99.837398 4.0 1.609438 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 168 0.073171 0.070618 93.170732 180.0 5.198497 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
AAACCTGAGAAACCGC-1_DownD100 DownD100 809 CTL04E 938 6.844815 1565.0 7.356280 373.0 5.924256 23.833866 24.0 3.218876 1.533546
AAACCTGAGCTATGCT-1_DownD100 DownD100 KOLF CTL08A 1705 7.441907 3037.0 8.018954 565.0 6.338594 18.603886 95.0 4.564348 3.128087
AAACCTGAGGCCCTCA-1_DownD100 DownD100 MIFF1 CTL02A 1501 7.314553 2415.0 7.789868 364.0 5.899898 15.072463 42.0 3.761200 1.739130
AAACCTGCAGCGTCCA-1_DownD100 DownD100 809 CTL04E 1057 6.964136 1719.0 7.450079 341.0 5.834811 19.837114 38.0 3.663562 2.210588
AAACCTGTCAGCTTAG-1_DownD100 DownD100 MIFF1 CTL02A 626 6.440947 1202.0 7.092574 420.0 6.042633 34.941761 36.0 3.610918 2.995008
... ... ... ... ... ... ... ... ... ... ... ... ... ...
TTTGTCAGTGCAGTAG-1_DownD100 DownD100 MIFF1 CTL02A 664 6.499787 1205.0 7.095064 398.0 5.988961 33.029045 19.0 2.995732 1.576764
TTTGTCAGTGCTAGCC-1_DownD100 DownD100 809 CTL04E 620 6.431331 1005.0 6.913737 275.0 5.620401 27.363184 40.0 3.713572 3.980099
TTTGTCATCCAAACAC-1_DownD100 DownD100 MIFF1 CTL02A 624 6.437752 1516.0 7.324490 742.0 6.610696 48.944592 46.0 3.850148 3.034301
TTTGTCATCCAACCAA-1_DownD100 DownD100 809 CTL04E 663 6.498282 1448.0 7.278629 625.0 6.439351 43.162983 25.0 3.258096 1.726519
TTTGTCATCTCAAACG-1_DownD100 DownD100 809 CTL04E 501 6.218600 901.0 6.804615 316.0 5.758902 35.072144 33.0 3.526361 3.662597

2460 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', '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 (0:00:10)

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
AAACCTGAGAAACCGC-1_DownD100 DownD100 809 CTL04E 938 6.844815 1565.0 7.356280 373.0 5.924256 23.833866 24.0 3.218876 1.533546
AAACCTGAGCTATGCT-1_DownD100 DownD100 KOLF CTL08A 1705 7.441907 3037.0 8.018954 565.0 6.338594 18.603886 95.0 4.564348 3.128087
AAACCTGAGGCCCTCA-1_DownD100 DownD100 MIFF1 CTL02A 1501 7.314553 2415.0 7.789868 364.0 5.899898 15.072463 42.0 3.761200 1.739130
AAACCTGCAGCGTCCA-1_DownD100 DownD100 809 CTL04E 1057 6.964136 1719.0 7.450079 341.0 5.834811 19.837114 38.0 3.663562 2.210588
AAACCTGTCAGCTTAG-1_DownD100 DownD100 MIFF1 CTL02A 626 6.440947 1202.0 7.092574 420.0 6.042633 34.941761 36.0 3.610918 2.995008
... ... ... ... ... ... ... ... ... ... ... ... ... ...
TTTGTCAGTGCAGTAG-1_DownD100 DownD100 MIFF1 CTL02A 664 6.499787 1205.0 7.095064 398.0 5.988961 33.029045 19.0 2.995732 1.576764
TTTGTCAGTGCTAGCC-1_DownD100 DownD100 809 CTL04E 620 6.431331 1005.0 6.913737 275.0 5.620401 27.363184 40.0 3.713572 3.980099
TTTGTCATCCAAACAC-1_DownD100 DownD100 MIFF1 CTL02A 624 6.437752 1516.0 7.324490 742.0 6.610696 48.944592 46.0 3.850148 3.034301
TTTGTCATCCAACCAA-1_DownD100 DownD100 809 CTL04E 663 6.498282 1448.0 7.278629 625.0 6.439351 43.162983 25.0 3.258096 1.726519
TTTGTCATCTCAAACG-1_DownD100 DownD100 809 CTL04E 501 6.218600 901.0 6.804615 316.0 5.758902 35.072144 33.0 3.526361 3.662597

2460 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 = 2460 × 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) (0:00:02)
Out[42]:
AnnData object with n_obs × n_vars = 2460 × 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.         0.9912036  0.9894429  0.9881998  0.9852557  0.9833145
     0.97345155 0.9672742  0.96481884 0.96223086 0.947279   0.93627626
     0.9298588  0.92587274 0.9227226 ]
    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